From d71458669a70468d692bd2b48e2f32a995af9989 Mon Sep 17 00:00:00 2001 From: yuanzhipeng <2501363769@qq.com> Date: Thu, 11 Jun 2026 18:56:15 +0800 Subject: [PATCH] =?UTF-8?q?```=20docs:=20=E5=88=A0=E9=99=A4=E9=A1=B9?= =?UTF-8?q?=E7=9B=AE=E6=96=87=E6=A1=A3=E5=92=8C=E9=85=8D=E7=BD=AE=E6=96=87?= =?UTF-8?q?=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 移除主 README.md 文件，包含项目概述、安装指南和配置示例 移除 lzwcai_mcp_sqlexecutor 模块的完整文档、配置文件和初始化文件 删除 businessQueries.json 配置模板和 .python-version 版本指定文件 更新 .gitignore 文件以清理 Python 缓存文件 ``` --- README.md | 68 - lzwcai_mcp_sqlexecutor/README.md | 138 --- .../__pycache__/main.cpython-312.pyc | Bin 820 -> 0 bytes .../lzwcai_mcp_sqlexecutor/.gitignore | 10 - .../lzwcai_mcp_sqlexecutor/.python-version | 1 - .../lzwcai_mcp_sqlexecutor/README.md | 154 --- .../lzwcai_mcp_sqlexecutor/__init__.py | 9 - .../businessQueries.json | 1 - .../logs/lzwcai_mcp_sqlexecutor.log | 289 ----- 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lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/workflow-execution-api.md delete mode 100644 lzwcai_workflow_to_mcp/main.py delete mode 100644 lzwcai_workflow_to_mcp/pyproject.toml diff --git a/README.md b/README.md deleted file mode 100644 index 38d5a1f..0000000 --- a/README.md +++ /dev/null @@ -1,68 +0,0 @@ -# lzwcai-mcp-server-package - -MCP (Model Context Protocol) 服务器工具集，为 AI 助手提供企业级业务能力扩展。 - -## 📦 包含模块 - -| 模块 | 版本 | 说明 | -|------|------|------| -| [lzwcai-mcp-iot](./lzwcai_mcp_iot) | 0.3.3 | IoT 设备控制服务器，支持设备查询、定位和控制 | -| [lzwcai-mcp-sqlexecutor](./lzwcai_mcp_sqlexecutor) | 0.1.8 | SQL 查询执行服务器，支持动态工具生成 | -| [lzwcai-mcp-api-converter](./lzwcai_mcp_api_converter) | 0.1.30 | API 转换服务器，将业务 API 转换为 MCP 工具 | -| [lzwcai-demp-tool-server-dify-to-mcp](./lzwcai_demp_tool_server_dify_to_mcp) | 0.1.4 | Dify 集成工具，将 Dify 模型部署到 MCP | -| [lzwcai-demp-tool-server-dify-to-mcp-test](./lzwcai_demp_tool_server_dify_to_mcp_test) | 0.1.0 | Dify 集成工具测试版 | - -## 🚀 快速安装 - -```bash -# IoT 设备控制 -pip install lzwcai-mcp-iot - -# SQL 查询执行 -pip install lzwcai-mcp-sqlexecutor - -# API 转换器 -pip install lzwcai-mcp-api-converter - -# Dify 集成 -pip install lzwcai-demp-tool-server-dify-to-mcp -``` - -## �️ 打包 与发布 - -```bash -# 进入子模块目录 -cd lzwcai_mcp_iot - -# 使用 uv 打包 -uv build - -# 上传到管理端技能广场 -# 将 dist/ 目录下的 .tar.gz 文件上传至技能广场 -``` - -## 🔧 MCP 客户端配置示例 - -```json -{ - "mcpServers": { - "iot": { - "command": "lzwcai-mcp-iot" - }, - "sql": { - "command": "lzwcai-mcp-sqlexecutor" - }, - "api": { - "command": "lzwcai-mcp-api-converter" - } - } -} -``` - -## 📄 许可证 - -专有软件 - 版权所有 © LZWCAI开发团队 - -## 📧 联系方式 - -- 邮箱：dev@lzwcai.com diff --git a/lzwcai_mcp_sqlexecutor/README.md b/lzwcai_mcp_sqlexecutor/README.md deleted file mode 100644 index 44ee71d..0000000 --- a/lzwcai_mcp_sqlexecutor/README.md +++ /dev/null @@ -1,138 +0,0 @@ -# lzwcai-mcp-sqlexecutor - -一个基于 MCP (Model Context Protocol) 的 SQL 查询执行服务器，支持从 JSON 配置文件动态生成查询工具。 - -## 功能特性 - -- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具 -- 🔧 灵活配置：支持自定义业务查询和参数验证 -- 📝 完整日志：详细的操作日志记录 -- 🌐 中文支持：工具名称自动转换为拼音 - -## 安装 - -### 使用 pip 安装 - -```bash -pip install lzwcai-mcp-sqlexecutor -``` - -### 从源码安装 - -```bash -git clone -cd lzwcai_mcp_sqlexecutor -pip install -e . -``` - -### 使用 uv 安装（推荐） - -```bash -uv pip install lzwcai-mcp-sqlexecutor -``` - -## 使用方法 - -### 命令行启动 - -安装后，可以直接通过命令启动： - -```bash -lzwcai-mcp-sqlexecutor -``` - -### 作为 Python 模块运行 - -```bash -python -m lzwcai_mcp_sqlexecutor.main -``` - -### 配置到 MCP 客户端 - -在你的 MCP 客户端配置文件中添加： - -```json -{ - "mcpServers": { - "lzwcai-sqlexecutor": { - "command": "lzwcai-mcp-sqlexecutor" - } - } -} -``` - -## 配置说明 - -### businessQueries.json - -在 `businessQueries.json` 中定义你的业务查询： - -```json -[ - { - "id": "query-001", - "businessName": "用户订单查询", - "businessDescription": "根据用户ID查询订单信息", - "sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}", - "parameters": { - "type": "object", - "required": ["userId"], - "properties": { - "userId": { - "type": "integer", - "description": "用户的唯一标识符", - "examples": [10086] - } - } - } - } -] -``` - -## 开发 - -### 依赖项 - -- Python >= 3.13 -- httpx >= 0.28.1 -- mcp[cli] >= 1.10.1 -- pypinyin >= 0.53.0 - -### 本地开发 - -```bash -# 克隆仓库 -git clone -cd lzwcai_mcp_sqlexecutor - -# 安装开发依赖 -pip install -e . - -# 运行服务器 -python -m lzwcai_mcp_sqlexecutor.main -``` - -## 构建与发布 - -### 使用 build 构建 - -```bash -pip install build -python -m build -``` - -### 发布到 PyPI - -```bash -pip install twine -twine upload dist/* -``` - -## 许可证 - -MIT License - -## 作者 - -lzwcai - diff --git a/lzwcai_mcp_sqlexecutor/__pycache__/main.cpython-312.pyc b/lzwcai_mcp_sqlexecutor/__pycache__/main.cpython-312.pyc deleted file mode 100644 index 7730fb54bd94f9743b969e74f53fda8638537ffd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 820 zcmbtS!EVz)5S_I*v1_$ZseqJn$ps0c#&t}aH~}HV0ca07AaTiM<$AX?&BhM410(_o zBr5oW@(G}R3YUN)AuI$!z=2yJH%{!jDWq3e$>W*ld2hx$^Lk;y0CA{xYWEWY_#%U& zqfDKNEu4Lz&;*L8!ch}Z^(izJrc&HK4-n}JFy&yHj-apf6*D)e8n;+Cj9W<<^-DbT zJznHqFYtF*RNM}uA|KrMy;iB?cT4eI&K@woCsHlryA*2pQ@QMgg;m8f`6$djBpE2p z?opF}5P{`pf~hMTKbYpCf8mQ?{V#rw=Ku1kMs==s@FPH=z(bh1Ixy!NsX@KO+wx+z zK~vRPvr{c@w>THkl4GyCZn;*gtl8D-deyOOZhB(N^V=+-*97Bs#0Q0)BySIafW zDpy>~v7C#Qx@)^`B(K0kDeic!z|5se7^fNw?zN&YNR5~!lx=%Go}_0FheO10$n0<= zFPb|PneU3S-W^6H7>n(J;xA45R=1z!WJVWwydlhE_=@w-HlJ+1!-Y4v@M7gP;4!jN{ z -cd lzwcai_mcp_sqlexecutor -pip install -e . -``` - -### 使用 uv 安装（推荐） - -```bash -uv pip install lzwcai-mcp-sqlexecutor -``` - -## 使用方法 - -### 命令行启动 - -安装后，可以直接通过命令启动： - -```bash -lzwcai-mcp-sqlexecutor -``` - -### 作为 Python 模块运行 - -```bash -python -m lzwcai_mcp_sqlexecutor.main -``` - -### 配置到 MCP 客户端 - -在你的 MCP 客户端配置文件中添加： - -```json -{ - "mcpServers": { - "lzwcai-sqlexecutor": { - "command": "lzwcai-mcp-sqlexecutor" - } - } -} -``` - -## 配置说明 - -### businessQueries.json - -在 `businessQueries.json` 中定义你的业务查询： - -```json -[ - { - "id": "query-001", - "businessName": "用户订单查询", - "businessDescription": "根据用户ID查询订单信息", - "sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}", - "parameters": { - "type": "object", - "required": ["userId"], - "properties": { - "userId": { - "type": "integer", - "description": "用户的唯一标识符", - "examples": [10086] - } - } - } - } -] -``` - -## 开发 - -### 依赖项 - -- Python >= 3.13 -- httpx >= 0.28.1 -- mcp[cli] >= 1.10.1 -- pypinyin >= 0.53.0 - -### 本地开发 - -```bash -# 克隆仓库 -git clone -cd lzwcai_mcp_sqlexecutor - -# 安装开发依赖 -pip install -e . - -# 运行服务器 -python -m lzwcai_mcp_sqlexecutor.main -``` - -## 构建与发布 - -### 使用 build 构建 - -```bash -pip install build -python -m build -``` - -### 发布到 PyPI - -```bash -pip install twine -twine upload dist/* -``` - -## 常见问题 - -### MCP Inspector 显示 JSON 解析错误 - -如果在使用 MCP Inspector 测试时遇到 `SyntaxError: Unexpected non-whitespace character after JSON` 错误，这是因为： - -1. **原因**：MCP 协议使用 stdio（标准输入输出）进行 JSON-RPC 通信，任何输出到 stdout 的内容（如 print 语句或控制台日志）都会破坏 JSON 格式。 - -2. **解决方案**：本服务器已将所有日志输出配置为仅写入文件（位于 `logs/` 目录），不输出到控制台。日志文件包括： - - `lzwcai_mcp_sqlexecutor.log` - 主日志文件 - - `lzwcai_mcp_sqlexecutor_error.log` - 错误日志 - - `lzwcai_mcp_sqlexecutor_daily.log` - 按日期滚动的日志 - - `mcp_services.log` - MCP 服务专用日志 - -3. **查看日志**：如果需要调试，请查看 `logs/` 目录下的日志文件。 - -## 许可证 - -MIT License - -## 作者 - -lzwcai - diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/__init__.py b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/__init__.py deleted file mode 100644 index 5df6d9c..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -""" -lzwcai-mcp-sqlexecutor - MCP server for executing business SQL queries -""" - -__version__ = "0.1.2" -__author__ = "lzwcai" - -__all__ = [] - diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/businessQueries.json b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/businessQueries.json deleted file mode 100644 index 0637a08..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/businessQueries.json +++ /dev/null @@ -1 +0,0 @@ -[] \ No newline at end of file diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor.log b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor.log deleted file mode 100644 index fd68929..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor.log +++ /dev/null @@ -1,289 +0,0 @@ -2026-05-25 16:05:46 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs -2026-05-25 16:05:46 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:352] - 开始运行 MCP SQL Executor 服务器 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:304] - ============================================================ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:305] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:306] - 版本: 0.1.0 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:307] - ============================================================ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:311] - 环境配置 - Database ID: 240 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:312] - 环境配置 - Skill ID: 2058819964077572098 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:313] - 环境配置 - Backend Base URL: http://192.168.2.236:8088 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:314] - ============================================================ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:319] - MCP 服务器已启动，等待客户端连接... -2026-05-25 16:05:49 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）... -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:283] - 调用第三方API，skill_id: 2058819964077572098 -2026-05-25 16:05:49 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:05:49 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 "HTTP/1.1 200 " -2026-05-25 16:05:49 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:85] - API调用成功: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:288] - 成功{'msg': '查询成功', 'code': 200, 'data': [{'id': '2058819964287287298', 'createBy': 'wxl06', 'createTime': '2026-05-25 15:58:25', 'updateBy': None, 'updateTime': None, 'serviceId': '2058819964085960705', 'uniqueName': '前50条导入数据明细查询', 'name': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'description': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'visualizable': 1, 'toolPrompt': '查询成功，返回 6 行数据，执行时间: 1ms', 'toolType': 'sql', 'datasourceId': '240', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'sqlParams': '{}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{"type":"object","properties":{},"required":[]}', 'outputJsonSchema': '{"type":"object","properties":{"text":{"type":"string"}},"additionalProperties":false}', 'lastExecutionTime': None}]} -2026-05-25 16:05:49 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:324] - 成功处理 1 条技能数据 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:293] - 成功获取并处理 1 条数据 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:128] - API配置: 1 条 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:129] - API配置数组: [{'id': '2058819964287287298', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'parameters': {}, 'datasourceId': '240'}] -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:165] - 成功生成 1 个 MCP 工具 -2026-05-25 16:05:52 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type CallToolRequest -2026-05-25 16:05:52 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: qian50tiaodaorushujumingxichaxun_32b1d628 -2026-05-25 16:05:52 - mcp_services - INFO - [main.py:230] - 正在调用测试SQL API... -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:132] - ================================================================================ -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:133] - test_sql_with_schema 接口接收到的数据: -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:134] - 数据类型: -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:135] - 数据内容: { - "datasourceId": "240", - "businessName": "qian50tiaodaorushujumingxichaxun_32b1d628", - "businessDescription": "qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。", - "sqlTemplate": "SELECT record_id AS \"记录ID\", task_id AS \"任务ID\", original_data AS \"原始数据\", process_result AS \"处理结果\", error_reason AS \"错误原因\", process_status AS \"处理状态\", target_table AS \"关联目标表\", created_time AS \"创建时间\" FROM import_record_detail LIMIT 50;", - "parameters": {}, - "testParams": {} -} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:136] - 数据源ID: 240 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:137] - 业务名称: qian50tiaodaorushujumingxichaxun_32b1d628 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:138] - 业务描述: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:139] - SQL模板: SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50; -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:140] - 参数定义: {} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:141] - 测试参数: {} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:144] - ================================================================================ -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:153] - 正在调用测试SQL API: http://192.168.2.236:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-05-25 16:05:52 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:170] - ================================================================================ -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:171] - test_sql_with_schema 接口返回的数据: -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:172] - HTTP状态码: 200 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:173] - 响应数据类型: -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:174] - 响应数据内容: { - "msg": "操作成功", - "code": 200, - "data": { - "resultCount": 6, - "data": [ - { - "记录ID": 80001, - "任务ID": "3320260418001", - "原始数据": "{\"name\": \"张三\", \"mobile\": \"13800000101\", \"dept\": \"销售部\", \"amount\": 1200}", - "处理结果": "导入成功", - "错误原因": null, - "处理状态": "SUCCESS", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:19:19.000+08:00" - }, - { - "记录ID": 80002, - "任务ID": "3320260418002", - "原始数据": "{\"name\": \"李四\", \"mobile\": \"13800000102\", \"dept\": \"财务部\", \"amount\": 980}", - "处理结果": "导入成功", - "错误原因": null, - "处理状态": "SUCCESS", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:19:34.000+08:00" - }, - { - "记录ID": 80003, - "任务ID": "3320260418003", - "原始数据": "{\"name\": \"王五\", \"mobile\": \"13800000103\", \"dept\": \"技术部\", \"amount\": 1500}", - "处理结果": "导入失败", - "错误原因": "字段校验失败：mobile格式不正确", - "处理状态": "FAILED", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:19:49.000+08:00" - }, - { - "记录ID": 80004, - "任务ID": "3320260418004", - "原始数据": "{\"name\": \"赵六\", \"mobile\": \"13800000104\", \"dept\": \"人事部\", \"amount\": 0}", - "处理结果": "导入成功", - "错误原因": null, - "处理状态": "SUCCESS", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:20:04.000+08:00" - }, - { - "记录ID": 80005, - "任务ID": "3320260418005", - "原始数据": "{\"name\": \"孙七\", \"mobile\": \"13800000105\", \"dept\": \"运营部\", \"amount\": 2000}", - "处理结果": "导入失败", - "错误原因": "目标表写入失败：唯一键冲突", - "处理状态": "FAILED", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:20:19.000+08:00" - }, - { - "记录ID": 80006, - "任务ID": "3320260418006", - "原始数据": "{\"name\": \"周八\", \"mobile\": \"13800000106\", \"dept\": \"法务部\", \"amount\": 300}", - "处理结果": "导入成功", - "错误原因": null, - "处理状态": "SUCCESS", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:20:34.000+08:00" - } - ], - "databaseName": "import_log_db_168", - "businessDescription": "qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。", - "originalTemplate": "SELECT record_id AS \"记录ID\", task_id AS \"任务ID\", original_data AS \"原始数据\", process_result AS \"处理结果\", error_reason AS \"错误原因\", process_status AS \"处理状态\", target_table AS \"关联目标表\", created_time AS \"创建时间\" FROM import_record_detail LIMIT 50;", - "convertedTemplate": "SELECT record_id AS \"记录ID\", task_id AS \"任务ID\", original_data AS \"原始数据\", process_result AS \"处理结果\", error_reason AS \"错误原因\", process_status AS \"处理状态\", target_table AS \"关联目标表\", created_time AS \"创建时间\" FROM import_record_detail LIMIT 50;", - "executionStatus": "success", - "businessName": "qian50tiaodaorushujumingxichaxun_32b1d628", - "testParams": {}, - "errorMessage": null, - "executionTime": 8, - "datasourceId": "240", - "logId": "2058821884796174336", - "executableSql": "SELECT record_id AS \"记录ID\", task_id AS \"任务ID\", original_data AS \"原始数据\", process_result AS \"处理结果\", error_reason AS \"错误原因\", process_status AS \"处理状态\", target_table AS \"关联目标表\", created_time AS \"创建时间\" FROM import_record_detail LIMIT 50;", - "datasourceName": "import_log_db_168" - } -} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:176] - 响应code: 200 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:177] - 响应msg: 操作成功 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:178] - 响应data: {'resultCount': 6, 'data': [{'记录ID': 80001, '任务ID': '3320260418001', '原始数据': '{"name": "张三", "mobile": "13800000101", "dept": "销售部", "amount": 1200}', '处理结果': '导入成功', '错误原因': None, '处理状态': 'SUCCESS', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:19:19.000+08:00'}, {'记录ID': 80002, '任务ID': '3320260418002', '原始数据': '{"name": "李四", "mobile": "13800000102", "dept": "财务部", "amount": 980}', '处理结果': '导入成功', '错误原因': None, '处理状态': 'SUCCESS', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:19:34.000+08:00'}, {'记录ID': 80003, '任务ID': '3320260418003', '原始数据': '{"name": "王五", "mobile": "13800000103", "dept": "技术部", "amount": 1500}', '处理结果': '导入失败', '错误原因': '字段校验失败：mobile格式不正确', '处理状态': 'FAILED', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:19:49.000+08:00'}, {'记录ID': 80004, '任务ID': '3320260418004', '原始数据': '{"name": "赵六", "mobile": "13800000104", "dept": "人事部", "amount": 0}', '处理结果': '导入成功', '错误原因': None, '处理状态': 'SUCCESS', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:20:04.000+08:00'}, {'记录ID': 80005, '任务ID': '3320260418005', '原始数据': '{"name": "孙七", "mobile": "13800000105", "dept": "运营部", "amount": 2000}', '处理结果': '导入失败', '错误原因': '目标表写入失败：唯一键冲突', '处理状态': 'FAILED', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:20:19.000+08:00'}, {'记录ID': 80006, '任务ID': '3320260418006', '原始数据': '{"name": "周八", "mobile": "13800000106", "dept": "法务部", "amount": 300}', '处理结果': '导入成功', '错误原因': None, '处理状态': 'SUCCESS', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:20:34.000+08:00'}], 'databaseName': 'import_log_db_168', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'originalTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'convertedTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'executionStatus': 'success', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'testParams': {}, 'errorMessage': None, 'executionTime': 8, 'datasourceId': '240', 'logId': '2058821884796174336', 'executableSql': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'datasourceName': 'import_log_db_168'} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:179] - ================================================================================ -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:181] - 测试SQL API调用成功 -2026-05-25 16:05:52 - mcp_services - INFO - [main.py:232] - 测试SQL API调用成功 -2026-05-25 16:15:08 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs -2026-05-25 16:15:08 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:246] - 开始运行 MCP SQL Executor 服务 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:204] - ============================================================ -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:205] - 正在启动 MCP 服务: lzwcai-mcp-sqlexecutor -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:206] - 版本: 0.1.0 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:207] - ============================================================ -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:210] - 环境配置 - Database ID: 240 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:211] - 环境配置 - Skill ID: 2058819964077572098 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:212] - 环境配置 - Backend Base URL: http://192.168.2.236:8088 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:213] - ============================================================ -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:218] - MCP 服务已启动，等待客户端连接... -2026-05-25 16:15:09 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:107] - 收到列出工具请求 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:77] - 初始化查询配置，数据源: api -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:189] - 调用第三方 API，skill_id: 2058819964077572098 -2026-05-25 16:15:09 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:15:09 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 "HTTP/1.1 200 " -2026-05-25 16:15:09 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:85] - API调用成功: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:191] - 成功获取原始响应: {'msg': '查询成功', 'code': 200, 'data': [{'id': '2058819964287287298', 'createBy': 'wxl06', 'createTime': '2026-05-25 15:58:25', 'updateBy': None, 'updateTime': None, 'serviceId': '2058819964085960705', 'uniqueName': '前50条导入数据明细查询', 'name': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'description': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'visualizable': 1, 'toolPrompt': '查询成功，返回 6 行数据，执行时间: 1ms', 'toolType': 'sql', 'datasourceId': '240', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'sqlParams': '{}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{"type":"object","properties":{},"required":[]}', 'outputJsonSchema': '{"type":"object","properties":{"text":{"type":"string"}},"additionalProperties":false}', 'lastExecutionTime': None}]} -2026-05-25 16:15:09 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:325] - 成功处理 1 条技能数据 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:194] - 成功处理 1 条数据 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:85] - API 配置: 1 条 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:86] - API 配置数组: [{'id': '2058819964287287298', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'parameters': {}, 'datasourceId': '240'}] -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:110] - 成功生成 1 个 MCP 工具 -2026-05-25 16:22:48 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs -2026-05-25 16:22:48 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:258] - 开始运行 MCP SQL Executor 服务 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:216] - ============================================================ -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:217] - 正在启动 MCP 服务: lzwcai-mcp-sqlexecutor -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:218] - 版本: 0.1.0 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:219] - ============================================================ -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:222] - 环境配置 - Database ID: 29 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:223] - 环境配置 - Skill ID: -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:224] - 环境配置 - Backend Base URL: http://lzwcai-demp-corp-manager:8086 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:225] - ============================================================ -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:230] - MCP 服务已启动，等待客户端连接... -2026-05-25 16:22:51 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:22:51 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:22:51 - mcp_services - INFO - [main.py:97] - 初始化查询配置，数据源: api -2026-05-25 16:22:51 - mcp_services - INFO - [main.py:201] - 调用第三方 API，skill_id: -2026-05-25 16:22:51 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:47] - 正在调用 API: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/ -2026-05-25 16:22:53 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:69] - API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -2026-05-25 16:22:53 - mcp_services - ERROR - [main.py:209] - API 调用失败: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -Traceback (most recent call last): - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 101, in map_httpcore_exceptions - yield - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 250, in handle_request - resp = self._pool.handle_request(req) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 256, in handle_request - raise exc from None - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 236, in handle_request - response = connection.handle_request( - ^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 101, in handle_request - raise exc - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 78, in handle_request - stream = self._connect(request) - ^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 124, in _connect - stream = self._network_backend.connect_tcp(**kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_backends\sync.py", line 207, in connect_tcp - with map_exceptions(exc_map): - File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__ - self.gen.throw(value) - File "D:\anaconda3\Lib\site-packages\httpcore\_exceptions.py", line 14, in map_exceptions - raise to_exc(exc) from exc -httpcore.ConnectError: [Errno 11001] getaddrinfo failed - -The above exception was the direct cause of the following exception: - -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 50, in get_skill_by_id - response = self.client.get(url, headers=self._get_headers()) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1053, in get - return self.request( - ^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 825, in request - return self.send(request, auth=auth, follow_redirects=follow_redirects) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 914, in send - response = self._send_handling_auth( - ^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 942, in _send_handling_auth - response = self._send_handling_redirects( - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 979, in _send_handling_redirects - response = self._send_single_request(request) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1014, in _send_single_request - response = transport.handle_request(request) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 249, in handle_request - with map_httpcore_exceptions(): - File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__ - self.gen.throw(value) - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 118, in map_httpcore_exceptions - raise mapped_exc(message) from exc -httpx.ConnectError: [Errno 11001] getaddrinfo failed - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 202, in call_third_party_api - raw_result = get_skill_by_id(skill_id) - ^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 154, in get_skill_by_id - return default_client.get_skill_by_id(skill_id) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 70, in get_skill_by_id - raise Exception(error_msg) -Exception: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -2026-05-25 16:22:53 - mcp_services - WARNING - [main.py:108] - API 获取失败，降级使用本地配置: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:58] - 成功加载 0 个业务查询配置 -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:130] - 成功生成 0 个 MCP 工具 -2026-05-25 16:22:53 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:130] - 成功生成 0 个 MCP 工具 -2026-05-25 16:23:09 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:23:09 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:23:09 - mcp_services - INFO - [main.py:130] - 成功生成 0 个 MCP 工具 -2026-05-25 16:26:37 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs -2026-05-25 16:26:37 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:258] - 开始运行 MCP SQL Executor 服务 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:216] - ============================================================ -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:217] - 正在启动 MCP 服务: lzwcai-mcp-sqlexecutor -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:218] - 版本: 0.1.0 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:219] - ============================================================ -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:222] - 环境配置 - Database ID: 240 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:223] - 环境配置 - Skill ID: 2058819964077572098 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:224] - 环境配置 - Backend Base URL: http://192.168.2.236:8088 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:225] - ============================================================ -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:230] - MCP 服务已启动，等待客户端连接... -2026-05-25 16:26:38 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:97] - 初始化查询配置，数据源: api -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:201] - 调用第三方 API，skill_id: 2058819964077572098 -2026-05-25 16:26:38 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:47] - 正在调用 API: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:26:38 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 "HTTP/1.1 200 " -2026-05-25 16:26:38 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:54] - API 调用成功: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:203] - 成功获取原始响应: {'msg': '查询成功', 'code': 200, 'data': [{'id': '2058819964287287298', 'createBy': 'wxl06', 'createTime': '2026-05-25 15:58:25', 'updateBy': None, 'updateTime': None, 'serviceId': '2058819964085960705', 'uniqueName': '前50条导入数据明细查询', 'name': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'description': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'visualizable': 1, 'toolPrompt': '查询成功，返回 6 行数据，执行时间: 1ms', 'toolType': 'sql', 'datasourceId': '240', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'sqlParams': '{}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{"type":"object","properties":{},"required":[]}', 'outputJsonSchema': '{"type":"object","properties":{"text":{"type":"string"}},"additionalProperties":false}', 'lastExecutionTime': None}]} -2026-05-25 16:26:38 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:205] - 成功处理 1 条技能数据 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:206] - 成功处理 1 条数据 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:105] - API 配置: 1 条 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:106] - API 配置数组: [{'id': '2058819964287287298', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'parameters': {}, 'datasourceId': '240'}] -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:130] - 成功生成 1 个 MCP 工具 diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor_daily.log b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor_daily.log deleted file mode 100644 index fd68929..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor_daily.log +++ /dev/null @@ -1,289 +0,0 @@ -2026-05-25 16:05:46 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs -2026-05-25 16:05:46 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:352] - 开始运行 MCP SQL Executor 服务器 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:304] - ============================================================ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:305] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:306] - 版本: 0.1.0 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:307] - ============================================================ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:311] - 环境配置 - Database ID: 240 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:312] - 环境配置 - Skill ID: 2058819964077572098 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:313] - 环境配置 - Backend Base URL: http://192.168.2.236:8088 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:314] - ============================================================ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:319] - MCP 服务器已启动，等待客户端连接... -2026-05-25 16:05:49 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）... -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:283] - 调用第三方API，skill_id: 2058819964077572098 -2026-05-25 16:05:49 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:05:49 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 "HTTP/1.1 200 " -2026-05-25 16:05:49 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:85] - API调用成功: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:288] - 成功{'msg': '查询成功', 'code': 200, 'data': [{'id': '2058819964287287298', 'createBy': 'wxl06', 'createTime': '2026-05-25 15:58:25', 'updateBy': None, 'updateTime': None, 'serviceId': '2058819964085960705', 'uniqueName': '前50条导入数据明细查询', 'name': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'description': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'visualizable': 1, 'toolPrompt': '查询成功，返回 6 行数据，执行时间: 1ms', 'toolType': 'sql', 'datasourceId': '240', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'sqlParams': '{}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{"type":"object","properties":{},"required":[]}', 'outputJsonSchema': '{"type":"object","properties":{"text":{"type":"string"}},"additionalProperties":false}', 'lastExecutionTime': None}]} -2026-05-25 16:05:49 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:324] - 成功处理 1 条技能数据 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:293] - 成功获取并处理 1 条数据 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:128] - API配置: 1 条 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:129] - API配置数组: [{'id': '2058819964287287298', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'parameters': {}, 'datasourceId': '240'}] -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:165] - 成功生成 1 个 MCP 工具 -2026-05-25 16:05:52 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type CallToolRequest -2026-05-25 16:05:52 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: qian50tiaodaorushujumingxichaxun_32b1d628 -2026-05-25 16:05:52 - mcp_services - INFO - [main.py:230] - 正在调用测试SQL API... -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:132] - ================================================================================ -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:133] - test_sql_with_schema 接口接收到的数据: -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:134] - 数据类型: -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:135] - 数据内容: { - "datasourceId": "240", - "businessName": "qian50tiaodaorushujumingxichaxun_32b1d628", - "businessDescription": "qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。", - "sqlTemplate": "SELECT record_id AS \"记录ID\", task_id AS \"任务ID\", original_data AS \"原始数据\", process_result AS \"处理结果\", error_reason AS \"错误原因\", process_status AS \"处理状态\", target_table AS \"关联目标表\", created_time AS \"创建时间\" FROM import_record_detail LIMIT 50;", - "parameters": {}, - "testParams": {} -} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:136] - 数据源ID: 240 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:137] - 业务名称: qian50tiaodaorushujumingxichaxun_32b1d628 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:138] - 业务描述: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:139] - SQL模板: SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50; -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:140] - 参数定义: {} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:141] - 测试参数: {} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:144] - ================================================================================ -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:153] - 正在调用测试SQL API: http://192.168.2.236:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-05-25 16:05:52 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:170] - ================================================================================ -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:171] - test_sql_with_schema 接口返回的数据: -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:172] - HTTP状态码: 200 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:173] - 响应数据类型: -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:174] - 响应数据内容: { - "msg": "操作成功", - "code": 200, - "data": { - "resultCount": 6, - "data": [ - { - "记录ID": 80001, - "任务ID": "3320260418001", - "原始数据": "{\"name\": \"张三\", \"mobile\": \"13800000101\", \"dept\": \"销售部\", \"amount\": 1200}", - "处理结果": "导入成功", - "错误原因": null, - "处理状态": "SUCCESS", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:19:19.000+08:00" - }, - { - "记录ID": 80002, - "任务ID": "3320260418002", - "原始数据": "{\"name\": \"李四\", \"mobile\": \"13800000102\", \"dept\": \"财务部\", \"amount\": 980}", - "处理结果": "导入成功", - "错误原因": null, - "处理状态": "SUCCESS", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:19:34.000+08:00" - }, - { - "记录ID": 80003, - "任务ID": "3320260418003", - "原始数据": "{\"name\": \"王五\", \"mobile\": \"13800000103\", \"dept\": \"技术部\", \"amount\": 1500}", - "处理结果": "导入失败", - "错误原因": "字段校验失败：mobile格式不正确", - "处理状态": "FAILED", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:19:49.000+08:00" - }, - { - "记录ID": 80004, - "任务ID": "3320260418004", - "原始数据": "{\"name\": \"赵六\", \"mobile\": \"13800000104\", \"dept\": \"人事部\", \"amount\": 0}", - "处理结果": "导入成功", - "错误原因": null, - "处理状态": "SUCCESS", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:20:04.000+08:00" - }, - { - "记录ID": 80005, - "任务ID": "3320260418005", - "原始数据": "{\"name\": \"孙七\", \"mobile\": \"13800000105\", \"dept\": \"运营部\", \"amount\": 2000}", - "处理结果": "导入失败", - "错误原因": "目标表写入失败：唯一键冲突", - "处理状态": "FAILED", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:20:19.000+08:00" - }, - { - "记录ID": 80006, - "任务ID": "3320260418006", - "原始数据": "{\"name\": \"周八\", \"mobile\": \"13800000106\", \"dept\": \"法务部\", \"amount\": 300}", - "处理结果": "导入成功", - "错误原因": null, - "处理状态": "SUCCESS", - "关联目标表": "employee_import", - "创建时间": "2026-04-18T15:20:34.000+08:00" - } - ], - "databaseName": "import_log_db_168", - "businessDescription": "qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。", - "originalTemplate": "SELECT record_id AS \"记录ID\", task_id AS \"任务ID\", original_data AS \"原始数据\", process_result AS \"处理结果\", error_reason AS \"错误原因\", process_status AS \"处理状态\", target_table AS \"关联目标表\", created_time AS \"创建时间\" FROM import_record_detail LIMIT 50;", - "convertedTemplate": "SELECT record_id AS \"记录ID\", task_id AS \"任务ID\", original_data AS \"原始数据\", process_result AS \"处理结果\", error_reason AS \"错误原因\", process_status AS \"处理状态\", target_table AS \"关联目标表\", created_time AS \"创建时间\" FROM import_record_detail LIMIT 50;", - "executionStatus": "success", - "businessName": "qian50tiaodaorushujumingxichaxun_32b1d628", - "testParams": {}, - "errorMessage": null, - "executionTime": 8, - "datasourceId": "240", - "logId": "2058821884796174336", - "executableSql": "SELECT record_id AS \"记录ID\", task_id AS \"任务ID\", original_data AS \"原始数据\", process_result AS \"处理结果\", error_reason AS \"错误原因\", process_status AS \"处理状态\", target_table AS \"关联目标表\", created_time AS \"创建时间\" FROM import_record_detail LIMIT 50;", - "datasourceName": "import_log_db_168" - } -} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:176] - 响应code: 200 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:177] - 响应msg: 操作成功 -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:178] - 响应data: {'resultCount': 6, 'data': [{'记录ID': 80001, '任务ID': '3320260418001', '原始数据': '{"name": "张三", "mobile": "13800000101", "dept": "销售部", "amount": 1200}', '处理结果': '导入成功', '错误原因': None, '处理状态': 'SUCCESS', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:19:19.000+08:00'}, {'记录ID': 80002, '任务ID': '3320260418002', '原始数据': '{"name": "李四", "mobile": "13800000102", "dept": "财务部", "amount": 980}', '处理结果': '导入成功', '错误原因': None, '处理状态': 'SUCCESS', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:19:34.000+08:00'}, {'记录ID': 80003, '任务ID': '3320260418003', '原始数据': '{"name": "王五", "mobile": "13800000103", "dept": "技术部", "amount": 1500}', '处理结果': '导入失败', '错误原因': '字段校验失败：mobile格式不正确', '处理状态': 'FAILED', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:19:49.000+08:00'}, {'记录ID': 80004, '任务ID': '3320260418004', '原始数据': '{"name": "赵六", "mobile": "13800000104", "dept": "人事部", "amount": 0}', '处理结果': '导入成功', '错误原因': None, '处理状态': 'SUCCESS', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:20:04.000+08:00'}, {'记录ID': 80005, '任务ID': '3320260418005', '原始数据': '{"name": "孙七", "mobile": "13800000105", "dept": "运营部", "amount": 2000}', '处理结果': '导入失败', '错误原因': '目标表写入失败：唯一键冲突', '处理状态': 'FAILED', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:20:19.000+08:00'}, {'记录ID': 80006, '任务ID': '3320260418006', '原始数据': '{"name": "周八", "mobile": "13800000106", "dept": "法务部", "amount": 300}', '处理结果': '导入成功', '错误原因': None, '处理状态': 'SUCCESS', '关联目标表': 'employee_import', '创建时间': '2026-04-18T15:20:34.000+08:00'}], 'databaseName': 'import_log_db_168', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'originalTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'convertedTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'executionStatus': 'success', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'testParams': {}, 'errorMessage': None, 'executionTime': 8, 'datasourceId': '240', 'logId': '2058821884796174336', 'executableSql': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'datasourceName': 'import_log_db_168'} -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:179] - ================================================================================ -2026-05-25 16:05:52 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:181] - 测试SQL API调用成功 -2026-05-25 16:05:52 - mcp_services - INFO - [main.py:232] - 测试SQL API调用成功 -2026-05-25 16:15:08 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs -2026-05-25 16:15:08 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:246] - 开始运行 MCP SQL Executor 服务 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:204] - ============================================================ -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:205] - 正在启动 MCP 服务: lzwcai-mcp-sqlexecutor -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:206] - 版本: 0.1.0 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:207] - ============================================================ -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:210] - 环境配置 - Database ID: 240 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:211] - 环境配置 - Skill ID: 2058819964077572098 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:212] - 环境配置 - Backend Base URL: http://192.168.2.236:8088 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:213] - ============================================================ -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:218] - MCP 服务已启动，等待客户端连接... -2026-05-25 16:15:09 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:107] - 收到列出工具请求 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:77] - 初始化查询配置，数据源: api -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:189] - 调用第三方 API，skill_id: 2058819964077572098 -2026-05-25 16:15:09 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:71] - 正在调用API: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:15:09 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 "HTTP/1.1 200 " -2026-05-25 16:15:09 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:85] - API调用成功: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:191] - 成功获取原始响应: {'msg': '查询成功', 'code': 200, 'data': [{'id': '2058819964287287298', 'createBy': 'wxl06', 'createTime': '2026-05-25 15:58:25', 'updateBy': None, 'updateTime': None, 'serviceId': '2058819964085960705', 'uniqueName': '前50条导入数据明细查询', 'name': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'description': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'visualizable': 1, 'toolPrompt': '查询成功，返回 6 行数据，执行时间: 1ms', 'toolType': 'sql', 'datasourceId': '240', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'sqlParams': '{}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{"type":"object","properties":{},"required":[]}', 'outputJsonSchema': '{"type":"object","properties":{"text":{"type":"string"}},"additionalProperties":false}', 'lastExecutionTime': None}]} -2026-05-25 16:15:09 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:325] - 成功处理 1 条技能数据 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:194] - 成功处理 1 条数据 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:85] - API 配置: 1 条 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:86] - API 配置数组: [{'id': '2058819964287287298', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'parameters': {}, 'datasourceId': '240'}] -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:110] - 成功生成 1 个 MCP 工具 -2026-05-25 16:22:48 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs -2026-05-25 16:22:48 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:258] - 开始运行 MCP SQL Executor 服务 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:216] - ============================================================ -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:217] - 正在启动 MCP 服务: lzwcai-mcp-sqlexecutor -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:218] - 版本: 0.1.0 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:219] - ============================================================ -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:222] - 环境配置 - Database ID: 29 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:223] - 环境配置 - Skill ID: -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:224] - 环境配置 - Backend Base URL: http://lzwcai-demp-corp-manager:8086 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:225] - ============================================================ -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:230] - MCP 服务已启动，等待客户端连接... -2026-05-25 16:22:51 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:22:51 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:22:51 - mcp_services - INFO - [main.py:97] - 初始化查询配置，数据源: api -2026-05-25 16:22:51 - mcp_services - INFO - [main.py:201] - 调用第三方 API，skill_id: -2026-05-25 16:22:51 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:47] - 正在调用 API: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/ -2026-05-25 16:22:53 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:69] - API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -2026-05-25 16:22:53 - mcp_services - ERROR - [main.py:209] - API 调用失败: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -Traceback (most recent call last): - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 101, in map_httpcore_exceptions - yield - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 250, in handle_request - resp = self._pool.handle_request(req) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 256, in handle_request - raise exc from None - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 236, in handle_request - response = connection.handle_request( - ^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 101, in handle_request - raise exc - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 78, in handle_request - stream = self._connect(request) - ^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 124, in _connect - stream = self._network_backend.connect_tcp(**kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_backends\sync.py", line 207, in connect_tcp - with map_exceptions(exc_map): - File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__ - self.gen.throw(value) - File "D:\anaconda3\Lib\site-packages\httpcore\_exceptions.py", line 14, in map_exceptions - raise to_exc(exc) from exc -httpcore.ConnectError: [Errno 11001] getaddrinfo failed - -The above exception was the direct cause of the following exception: - -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 50, in get_skill_by_id - response = self.client.get(url, headers=self._get_headers()) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1053, in get - return self.request( - ^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 825, in request - return self.send(request, auth=auth, follow_redirects=follow_redirects) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 914, in send - response = self._send_handling_auth( - ^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 942, in _send_handling_auth - response = self._send_handling_redirects( - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 979, in _send_handling_redirects - response = self._send_single_request(request) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1014, in _send_single_request - response = transport.handle_request(request) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 249, in handle_request - with map_httpcore_exceptions(): - File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__ - self.gen.throw(value) - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 118, in map_httpcore_exceptions - raise mapped_exc(message) from exc -httpx.ConnectError: [Errno 11001] getaddrinfo failed - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 202, in call_third_party_api - raw_result = get_skill_by_id(skill_id) - ^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 154, in get_skill_by_id - return default_client.get_skill_by_id(skill_id) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 70, in get_skill_by_id - raise Exception(error_msg) -Exception: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -2026-05-25 16:22:53 - mcp_services - WARNING - [main.py:108] - API 获取失败，降级使用本地配置: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:58] - 成功加载 0 个业务查询配置 -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:130] - 成功生成 0 个 MCP 工具 -2026-05-25 16:22:53 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:130] - 成功生成 0 个 MCP 工具 -2026-05-25 16:23:09 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:23:09 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:23:09 - mcp_services - INFO - [main.py:130] - 成功生成 0 个 MCP 工具 -2026-05-25 16:26:37 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\logs -2026-05-25 16:26:37 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:258] - 开始运行 MCP SQL Executor 服务 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:216] - ============================================================ -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:217] - 正在启动 MCP 服务: lzwcai-mcp-sqlexecutor -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:218] - 版本: 0.1.0 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:219] - ============================================================ -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:222] - 环境配置 - Database ID: 240 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:223] - 环境配置 - Skill ID: 2058819964077572098 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:224] - 环境配置 - Backend Base URL: http://192.168.2.236:8088 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:225] - ============================================================ -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:230] - MCP 服务已启动，等待客户端连接... -2026-05-25 16:26:38 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:97] - 初始化查询配置，数据源: api -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:201] - 调用第三方 API，skill_id: 2058819964077572098 -2026-05-25 16:26:38 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:47] - 正在调用 API: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:26:38 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 "HTTP/1.1 200 " -2026-05-25 16:26:38 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:54] - API 调用成功: http://192.168.2.236:8088/datasource/skill/getBySkillId/2058819964077572098 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:203] - 成功获取原始响应: {'msg': '查询成功', 'code': 200, 'data': [{'id': '2058819964287287298', 'createBy': 'wxl06', 'createTime': '2026-05-25 15:58:25', 'updateBy': None, 'updateTime': None, 'serviceId': '2058819964085960705', 'uniqueName': '前50条导入数据明细查询', 'name': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'description': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'visualizable': 1, 'toolPrompt': '查询成功，返回 6 行数据，执行时间: 1ms', 'toolType': 'sql', 'datasourceId': '240', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'sqlParams': '{}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{"type":"object","properties":{},"required":[]}', 'outputJsonSchema': '{"type":"object","properties":{"text":{"type":"string"}},"additionalProperties":false}', 'lastExecutionTime': None}]} -2026-05-25 16:26:38 - lzwcai_mcp_sqlexecutor.utils.api_client - INFO - [api_client.py:205] - 成功处理 1 条技能数据 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:206] - 成功处理 1 条数据 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:105] - API 配置: 1 条 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:106] - API 配置数组: [{'id': '2058819964287287298', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'parameters': {}, 'datasourceId': '240'}] -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:130] - 成功生成 1 个 MCP 工具 diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor_error.log b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor_error.log deleted file mode 100644 index 6e209b0..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/lzwcai_mcp_sqlexecutor_error.log +++ /dev/null @@ -1,73 +0,0 @@ -2026-05-25 16:22:53 - lzwcai_mcp_sqlexecutor.utils.api_client - ERROR - [api_client.py:69] - API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -2026-05-25 16:22:53 - mcp_services - ERROR - [main.py:209] - API 调用失败: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -Traceback (most recent call last): - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 101, in map_httpcore_exceptions - yield - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 250, in handle_request - resp = self._pool.handle_request(req) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 256, in handle_request - raise exc from None - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 236, in handle_request - response = connection.handle_request( - ^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 101, in handle_request - raise exc - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 78, in handle_request - stream = self._connect(request) - ^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 124, in _connect - stream = self._network_backend.connect_tcp(**kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_backends\sync.py", line 207, in connect_tcp - with map_exceptions(exc_map): - File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__ - self.gen.throw(value) - File "D:\anaconda3\Lib\site-packages\httpcore\_exceptions.py", line 14, in map_exceptions - raise to_exc(exc) from exc -httpcore.ConnectError: [Errno 11001] getaddrinfo failed - -The above exception was the direct cause of the following exception: - -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 50, in get_skill_by_id - response = self.client.get(url, headers=self._get_headers()) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1053, in get - return self.request( - ^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 825, in request - return self.send(request, auth=auth, follow_redirects=follow_redirects) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 914, in send - response = self._send_handling_auth( - ^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 942, in _send_handling_auth - response = self._send_handling_redirects( - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 979, in _send_handling_redirects - response = self._send_single_request(request) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1014, in _send_single_request - response = transport.handle_request(request) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 249, in handle_request - with map_httpcore_exceptions(): - File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__ - self.gen.throw(value) - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 118, in map_httpcore_exceptions - raise mapped_exc(message) from exc -httpx.ConnectError: [Errno 11001] getaddrinfo failed - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 202, in call_third_party_api - raw_result = get_skill_by_id(skill_id) - ^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 154, in get_skill_by_id - return default_client.get_skill_by_id(skill_id) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 70, in get_skill_by_id - raise Exception(error_msg) -Exception: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/mcp_services.log b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/mcp_services.log deleted file mode 100644 index 29d95f7..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/logs/mcp_services.log +++ /dev/null @@ -1,149 +0,0 @@ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:352] - 开始运行 MCP SQL Executor 服务器 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:304] - ============================================================ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:305] - 正在启动 MCP 服务器: lzwcai-mcp-sqlexecutor -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:306] - 版本: 0.1.0 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:307] - ============================================================ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:311] - 环境配置 - Database ID: 240 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:312] - 环境配置 - Skill ID: 2058819964077572098 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:313] - 环境配置 - Backend Base URL: http://192.168.2.236:8088 -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:314] - ============================================================ -2026-05-25 16:05:46 - mcp_services - INFO - [main.py:319] - MCP 服务器已启动，等待客户端连接... -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: api）... -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:283] - 调用第三方API，skill_id: 2058819964077572098 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:288] - 成功{'msg': '查询成功', 'code': 200, 'data': [{'id': '2058819964287287298', 'createBy': 'wxl06', 'createTime': '2026-05-25 15:58:25', 'updateBy': None, 'updateTime': None, 'serviceId': '2058819964085960705', 'uniqueName': '前50条导入数据明细查询', 'name': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'description': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'visualizable': 1, 'toolPrompt': '查询成功，返回 6 行数据，执行时间: 1ms', 'toolType': 'sql', 'datasourceId': '240', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'sqlParams': '{}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{"type":"object","properties":{},"required":[]}', 'outputJsonSchema': '{"type":"object","properties":{"text":{"type":"string"}},"additionalProperties":false}', 'lastExecutionTime': None}]} -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:293] - 成功获取并处理 1 条数据 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:128] - API配置: 1 条 -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:129] - API配置数组: [{'id': '2058819964287287298', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'parameters': {}, 'datasourceId': '240'}] -2026-05-25 16:05:49 - mcp_services - INFO - [main.py:165] - 成功生成 1 个 MCP 工具 -2026-05-25 16:05:52 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: qian50tiaodaorushujumingxichaxun_32b1d628 -2026-05-25 16:05:52 - mcp_services - INFO - [main.py:230] - 正在调用测试SQL API... -2026-05-25 16:05:52 - mcp_services - INFO - [main.py:232] - 测试SQL API调用成功 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:246] - 开始运行 MCP SQL Executor 服务 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:204] - ============================================================ -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:205] - 正在启动 MCP 服务: lzwcai-mcp-sqlexecutor -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:206] - 版本: 0.1.0 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:207] - ============================================================ -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:210] - 环境配置 - Database ID: 240 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:211] - 环境配置 - Skill ID: 2058819964077572098 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:212] - 环境配置 - Backend Base URL: http://192.168.2.236:8088 -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:213] - ============================================================ -2026-05-25 16:15:08 - mcp_services - INFO - [main.py:218] - MCP 服务已启动，等待客户端连接... -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:107] - 收到列出工具请求 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:77] - 初始化查询配置，数据源: api -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:189] - 调用第三方 API，skill_id: 2058819964077572098 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:191] - 成功获取原始响应: {'msg': '查询成功', 'code': 200, 'data': [{'id': '2058819964287287298', 'createBy': 'wxl06', 'createTime': '2026-05-25 15:58:25', 'updateBy': None, 'updateTime': None, 'serviceId': '2058819964085960705', 'uniqueName': '前50条导入数据明细查询', 'name': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'description': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'visualizable': 1, 'toolPrompt': '查询成功，返回 6 行数据，执行时间: 1ms', 'toolType': 'sql', 'datasourceId': '240', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'sqlParams': '{}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{"type":"object","properties":{},"required":[]}', 'outputJsonSchema': '{"type":"object","properties":{"text":{"type":"string"}},"additionalProperties":false}', 'lastExecutionTime': None}]} -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:194] - 成功处理 1 条数据 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:85] - API 配置: 1 条 -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:86] - API 配置数组: [{'id': '2058819964287287298', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'parameters': {}, 'datasourceId': '240'}] -2026-05-25 16:15:09 - mcp_services - INFO - [main.py:110] - 成功生成 1 个 MCP 工具 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:258] - 开始运行 MCP SQL Executor 服务 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:216] - ============================================================ -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:217] - 正在启动 MCP 服务: lzwcai-mcp-sqlexecutor -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:218] - 版本: 0.1.0 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:219] - ============================================================ -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:222] - 环境配置 - Database ID: 29 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:223] - 环境配置 - Skill ID: -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:224] - 环境配置 - Backend Base URL: http://lzwcai-demp-corp-manager:8086 -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:225] - ============================================================ -2026-05-25 16:22:48 - mcp_services - INFO - [main.py:230] - MCP 服务已启动，等待客户端连接... -2026-05-25 16:22:51 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:22:51 - mcp_services - INFO - [main.py:97] - 初始化查询配置，数据源: api -2026-05-25 16:22:51 - mcp_services - INFO - [main.py:201] - 调用第三方 API，skill_id: -2026-05-25 16:22:53 - mcp_services - ERROR - [main.py:209] - API 调用失败: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -Traceback (most recent call last): - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 101, in map_httpcore_exceptions - yield - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 250, in handle_request - resp = self._pool.handle_request(req) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 256, in handle_request - raise exc from None - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection_pool.py", line 236, in handle_request - response = connection.handle_request( - ^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 101, in handle_request - raise exc - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 78, in handle_request - stream = self._connect(request) - ^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_sync\connection.py", line 124, in _connect - stream = self._network_backend.connect_tcp(**kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpcore\_backends\sync.py", line 207, in connect_tcp - with map_exceptions(exc_map): - File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__ - self.gen.throw(value) - File "D:\anaconda3\Lib\site-packages\httpcore\_exceptions.py", line 14, in map_exceptions - raise to_exc(exc) from exc -httpcore.ConnectError: [Errno 11001] getaddrinfo failed - -The above exception was the direct cause of the following exception: - -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 50, in get_skill_by_id - response = self.client.get(url, headers=self._get_headers()) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1053, in get - return self.request( - ^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 825, in request - return self.send(request, auth=auth, follow_redirects=follow_redirects) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 914, in send - response = self._send_handling_auth( - ^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 942, in _send_handling_auth - response = self._send_handling_redirects( - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 979, in _send_handling_redirects - response = self._send_single_request(request) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_client.py", line 1014, in _send_single_request - response = transport.handle_request(request) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 249, in handle_request - with map_httpcore_exceptions(): - File "D:\anaconda3\Lib\contextlib.py", line 158, in __exit__ - self.gen.throw(value) - File "D:\anaconda3\Lib\site-packages\httpx\_transports\default.py", line 118, in map_httpcore_exceptions - raise mapped_exc(message) from exc -httpx.ConnectError: [Errno 11001] getaddrinfo failed - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\main.py", line 202, in call_third_party_api - raw_result = get_skill_by_id(skill_id) - ^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 154, in get_skill_by_id - return default_client.get_skill_by_id(skill_id) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcp_sqlexecutor\lzwcai_mcp_sqlexecutor\utils\api_client.py", line 70, in get_skill_by_id - raise Exception(error_msg) -Exception: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -2026-05-25 16:22:53 - mcp_services - WARNING - [main.py:108] - API 获取失败，降级使用本地配置: API 请求异常: http://lzwcai-demp-corp-manager:8086/datasource/skill/getBySkillId/, 错误: [Errno 11001] getaddrinfo failed -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:58] - 成功加载 0 个业务查询配置 -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:130] - 成功生成 0 个 MCP 工具 -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:22:53 - mcp_services - INFO - [main.py:130] - 成功生成 0 个 MCP 工具 -2026-05-25 16:23:09 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:23:09 - mcp_services - INFO - [main.py:130] - 成功生成 0 个 MCP 工具 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:258] - 开始运行 MCP SQL Executor 服务 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:216] - ============================================================ -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:217] - 正在启动 MCP 服务: lzwcai-mcp-sqlexecutor -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:218] - 版本: 0.1.0 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:219] - ============================================================ -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:222] - 环境配置 - Database ID: 240 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:223] - 环境配置 - Skill ID: 2058819964077572098 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:224] - 环境配置 - Backend Base URL: http://192.168.2.236:8088 -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:225] - ============================================================ -2026-05-25 16:26:37 - mcp_services - INFO - [main.py:230] - MCP 服务已启动，等待客户端连接... -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:127] - 收到列出工具请求 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:97] - 初始化查询配置，数据源: api -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:201] - 调用第三方 API，skill_id: 2058819964077572098 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:203] - 成功获取原始响应: {'msg': '查询成功', 'code': 200, 'data': [{'id': '2058819964287287298', 'createBy': 'wxl06', 'createTime': '2026-05-25 15:58:25', 'updateBy': None, 'updateTime': None, 'serviceId': '2058819964085960705', 'uniqueName': '前50条导入数据明细查询', 'name': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'description': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'visualizable': 1, 'toolPrompt': '查询成功，返回 6 行数据，执行时间: 1ms', 'toolType': 'sql', 'datasourceId': '240', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'sqlParams': '{}', 'resultType': 'list', 'sourceType': 'ai', 'trainingTaskId': None, 'tableMetadataIds': '', 'executionCount': 0, 'visualizationConfigs': None, 'inputJsonSchema': '{"type":"object","properties":{},"required":[]}', 'outputJsonSchema': '{"type":"object","properties":{"text":{"type":"string"}},"additionalProperties":false}', 'lastExecutionTime': None}]} -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:206] - 成功处理 1 条数据 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:105] - API 配置: 1 条 -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:106] - API 配置数组: [{'id': '2058819964287287298', 'businessName': 'qian50tiaodaorushujumingxichaxun_32b1d628', 'businessDescription': 'qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: qian50tiaodaorushujumingxichaxun_32b1d628: 查询数据导入明细表中的前50条记录，包含原始数据、处理状态、错误原因等关键信息，用于快速预览最近的导入情况。', 'sqlTemplate': 'SELECT record_id AS "记录ID", task_id AS "任务ID", original_data AS "原始数据", process_result AS "处理结果", error_reason AS "错误原因", process_status AS "处理状态", target_table AS "关联目标表", created_time AS "创建时间" FROM import_record_detail LIMIT 50;', 'parameters': {}, 'datasourceId': '240'}] -2026-05-25 16:26:38 - mcp_services - INFO - [main.py:130] - 成功生成 1 个 MCP 工具 diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/main.py b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/main.py deleted file mode 100644 index 05e84e7..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/main.py +++ /dev/null @@ -1,268 +0,0 @@ -from pathlib import Path -from typing import Any -import asyncio -import json -import logging - -try: - from .utils import load_json, generate_input_schema - from .utils import get_skill_by_id, process_skill_response, test_sql_with_schema - from .utils import get_skill_id, get_env_config - from .utils.logger_config import logger_config -except ImportError: - from utils import load_json, generate_input_schema - from utils import get_skill_by_id, process_skill_response, test_sql_with_schema - from utils import get_skill_id, get_env_config - from utils.logger_config import logger_config - -from mcp.server.models import InitializationOptions -from mcp.server import NotificationOptions, Server -import mcp.types as types - - -mcp_logger = logger_config.setup_mcp_logging() - -DATA_SOURCE_API = "api" -DATA_SOURCE_LOCAL = "local" -DATA_SOURCE_BOTH = "both" -DEFAULT_DATA_SOURCE = DATA_SOURCE_API - - -def _text_response(payload: dict[str, Any]) -> list[types.TextContent]: - """Build a JSON text response.""" - return [ - types.TextContent( - type="text", - text=json.dumps(payload, ensure_ascii=False, indent=2) - ) - ] - - -def _extract_user_id(arguments: dict[str, Any]) -> Any: - """Allow numeric 0, reject None and blank strings.""" - user_id = arguments.get("userId") - if user_id is None: - return None - if isinstance(user_id, str) and not user_id.strip(): - return None - return user_id - - -def get_queries(): - """Read local business query config.""" - try: - current_dir = Path(__file__).parent - json_path = current_dir / "businessQueries.json" - mcp_logger.debug(f"正在读取业务查询配置文件: {json_path}") - queries = load_json(json_path) - mcp_logger.info(f"成功加载 {len(queries)} 个业务查询配置") - return queries - except Exception as e: - mcp_logger.error(f"加载业务查询配置失败: {e}", exc_info=True) - raise - - -def generate_tool_schema_from_query(query: dict) -> types.Tool: - """Generate an MCP tool definition from one query config.""" - try: - parameters = query.get("parameters", {}) - input_schema = generate_input_schema(parameters) - tool_name = query["businessName"] - description = f"{query['businessName']}: {query['businessDescription']}" - - mcp_logger.debug(f"生成工具模式: {tool_name} - {query['businessName']}") - - return types.Tool( - name=tool_name, - description=description, - inputSchema=input_schema - ) - except Exception as e: - mcp_logger.error( - f"生成工具模式失败: {query.get('id', 'unknown')}, 错误: {e}", - exc_info=True - ) - raise - - -server = Server("lzwcai-mcp-sqlexecutor") -_queries_cache = None - - -async def get_queries_cache(source: str = None): - """Get or initialize query config cache.""" - global _queries_cache - if _queries_cache is None: - source = source or DEFAULT_DATA_SOURCE - mcp_logger.info(f"初始化查询配置，数据源: {source}") - - if source == DATA_SOURCE_LOCAL: - _queries_cache = get_queries() - mcp_logger.info(f"本地配置: {len(_queries_cache)} 条") - elif source == DATA_SOURCE_API: - try: - _queries_cache = await call_third_party_api() - mcp_logger.info(f"API 配置: {len(_queries_cache)} 条") - mcp_logger.info(f"API 配置数组: {_queries_cache}") - except Exception as e: - mcp_logger.warning(f"API 获取失败，降级使用本地配置: {e}") - _queries_cache = get_queries() - else: - local = get_queries() - try: - api = await call_third_party_api() - except Exception as e: - mcp_logger.warning(f"API 获取失败: {e}") - api = [] - _queries_cache = local + api - mcp_logger.info(f"配置总数: {len(_queries_cache)} 条，本地 {len(local)} + API {len(api)}") - - return _queries_cache - - -@server.list_tools() -async def handle_list_tools() -> list[types.Tool]: - """List all dynamically generated MCP tools.""" - try: - mcp_logger.info("收到列出工具请求") - queries = await get_queries_cache() - tools = [generate_tool_schema_from_query(query) for query in queries] - mcp_logger.info(f"成功生成 {len(tools)} 个 MCP 工具") - mcp_logger.debug(f"工具列表: {[tool.name for tool in tools]}") - return tools - except Exception as e: - mcp_logger.error(f"列出工具失败: {e}", exc_info=True) - raise - - -@server.call_tool() -async def handle_call_tool( - name: str, - arguments: dict[str, Any] | None -) -> list[types.TextContent]: - """Handle MCP tool invocation.""" - try: - mcp_logger.info(f"收到工具调用请求: {name}") - mcp_logger.debug(f"工具参数: {arguments}") - - queries = await get_queries_cache() - tool_item = next((query for query in queries if query["businessName"] == name), None) - - if not tool_item: - return _text_response({"error": f"未找到工具 {name} 对应的配置"}) - - normalized_parameters = generate_input_schema(tool_item.get("parameters")) - normalized_arguments = arguments or {} - user_id = _extract_user_id(normalized_arguments) - - if user_id is None: - error_msg = f"工具 {name} 缺少必填参数 userId" - mcp_logger.warning(error_msg) - return _text_response({"error": error_msg, "required": ["userId"]}) - - request_data = { - "datasourceId": tool_item.get("datasourceId"), - "businessName": tool_item.get("businessName"), - "businessDescription": tool_item.get("businessDescription"), - "sqlTemplate": tool_item.get("sqlTemplate"), - "parameters": normalized_parameters, - "testParams": normalized_arguments, - "userId": user_id - } - - target_database_name = normalized_arguments.get("targetDatabaseName") - if target_database_name: - request_data["targetDatabaseName"] = target_database_name - mcp_logger.debug(f"添加目标数据库名称: {target_database_name}") - - try: - mcp_logger.info("正在调用测试 SQL API...") - result_payload = test_sql_with_schema(request_data) - mcp_logger.info("测试 SQL API 调用成功") - except Exception as e: - error_msg = f"调用测试 SQL API 失败: {str(e)}" - mcp_logger.error(error_msg, exc_info=True) - result_payload = {"error": error_msg} - - mcp_logger.debug(f"工具调用结果: {result_payload}") - return _text_response(result_payload) - except Exception as e: - error_msg = f"工具调用失败: {name}, 错误: {e}" - mcp_logger.error(error_msg, exc_info=True) - return [types.TextContent(type="text", text=f"错误: {error_msg}")] - - -async def call_third_party_api(skill_id: str = None) -> list: - """Call backend API and map the response into query configs.""" - try: - if skill_id is None: - skill_id = get_skill_id() - - mcp_logger.info(f"调用第三方 API，skill_id: {skill_id}") - raw_result = get_skill_by_id(skill_id) - mcp_logger.info(f"成功获取原始响应: {raw_result}") - - processed_queries = process_skill_response(raw_result) - mcp_logger.info(f"成功处理 {len(processed_queries)} 条数据") - return processed_queries - except Exception as e: - mcp_logger.error(f"API 调用失败: {e}", exc_info=True) - raise - - -async def async_main(): - """Async entry for the MCP server.""" - try: - mcp_logger.info("=" * 60) - mcp_logger.info("正在启动 MCP 服务: lzwcai-mcp-sqlexecutor") - mcp_logger.info("版本: 0.1.0") - mcp_logger.info("=" * 60) - - env_config = get_env_config() - mcp_logger.info(f"环境配置 - Database ID: {env_config['database_id']}") - mcp_logger.info(f"环境配置 - Skill ID: {env_config['skill_id']}") - mcp_logger.info(f"环境配置 - Backend Base URL: {env_config['backend_base_url']}") - mcp_logger.info("=" * 60) - - from mcp.server.stdio import stdio_server - - async with stdio_server() as (read_stream, write_stream): - mcp_logger.info("MCP 服务已启动，等待客户端连接...") - await server.run( - read_stream, - write_stream, - InitializationOptions( - server_name="lzwcai-mcp-sqlexecutor", - server_version="0.1.0", - capabilities=server.get_capabilities( - notification_options=NotificationOptions(), - experimental_capabilities={}, - ), - ), - ) - - mcp_logger.info("MCP 服务已关闭") - except Exception as e: - mcp_logger.error(f"MCP 服务运行失败: {e}", exc_info=True) - raise - - -def main(): - """Console entrypoint.""" - try: - logger_config.setup_logging( - app_name="lzwcai_mcp_sqlexecutor", - log_level=logging.INFO, - console_output=False - ) - mcp_logger.info("开始运行 MCP SQL Executor 服务") - asyncio.run(async_main()) - except KeyboardInterrupt: - mcp_logger.info("收到中断信号，正在关闭服务...") - except Exception as e: - mcp_logger.error(f"程序运行失败: {e}", exc_info=True) - raise - - -if __name__ == "__main__": - main() diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/pyproject.toml b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/pyproject.toml deleted file mode 100644 index 583cab2..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/pyproject.toml +++ /dev/null @@ -1,35 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai-mcp-sqlexecutor" -version = "0.1.6" -description = "MCP server for executing business SQL queries with dynamic tool generation" -readme = "README.md" -requires-python = ">=3.10" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "sql", "executor", "server"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", - "pypinyin>=0.53.0", -] - -[project.scripts] -lzwcai-mcp-sqlexecutor = "lzwcai_mcp_sqlexecutor.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcp_sqlexecutor"] - -[tool.hatch.build.targets.wheel.force-include] -"lzwcai_mcp_sqlexecutor/businessQueries.json" = "lzwcai_mcp_sqlexecutor/businessQueries.json" diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/__init__.py b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/__init__.py deleted file mode 100644 index 4fdc6f7..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Utils package for lzwcai_mcp_sqlexecutor""" - -from .json_helper import load_json -from .name_helper import generate_tool_name -from .schema_helper import generate_input_schema, validate_input_schema -from .api_client import DataSourceAPIClient, get_skill_by_id, process_skill_response, test_sql_with_schema -from .env_config import get_database_id, get_skill_id, get_backend_base_url, get_env_config, set_env_variable - -__all__ = [ - 'load_json', - 'generate_tool_name', - 'generate_input_schema', - 'validate_input_schema', - 'DataSourceAPIClient', - 'get_skill_by_id', - 'process_skill_response', - 'test_sql_with_schema', - 'get_database_id', - 'get_skill_id', - 'get_backend_base_url', - 'get_env_config', - 'set_env_variable' -] - diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/api_client.py b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/api_client.py deleted file mode 100644 index fd0bc89..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/api_client.py +++ /dev/null @@ -1,209 +0,0 @@ -"""Backend API client helpers.""" - -import json -import logging -from typing import Any, Dict, List, Optional - -import httpx - -try: - from .env_config import get_backend_base_url -except ImportError: - from env_config import get_backend_base_url - - -logger = logging.getLogger(__name__) - - -class DataSourceAPIClient: - """HTTP client for backend datasource APIs.""" - - def __init__( - self, - base_url: Optional[str] = None, - token: Optional[str] = None - ): - if base_url is None: - base_url = get_backend_base_url() - - self.base_url = base_url.rstrip("/") - self.token = token or ( - "eyJhbGciOiJIUzUxMiJ9.eyJsb2dpbl91c2VyX2tleSI6IjJiYTk4ODllLWM2ZGItNDQ5YS1i" - "ZmFjLTQ2YzMxODFlODg5NCJ9.dvi8zm0LsWvJ_h9zD5blnHFRxa4z4_WBm1R487ekE7HlHzrN6dn" - "vqhK8askqT5b1EcE8myHwRzLVMoI8UOjOrw" - ) - self.client = httpx.Client(timeout=30.0) - - def _get_headers(self) -> Dict[str, str]: - return { - "Authorization": f"Bearer {self.token}", - } - - def get_skill_by_id(self, skill_id: str) -> Dict[str, Any]: - """Fetch skill details by skill id.""" - try: - url = f"{self.base_url}/datasource/skill/getBySkillId/{skill_id}" - - logger.info(f"正在调用 API: {url}") - logger.debug(f"请求参数 - skill_id: {skill_id}") - - response = self.client.get(url, headers=self._get_headers()) - response.raise_for_status() - data = response.json() - - logger.info(f"API 调用成功: {url}") - logger.debug(f"响应数据: {data}") - return data - - except httpx.TimeoutException: - error_msg = f"API 请求超时: {url}" - logger.error(error_msg) - raise Exception(error_msg) - except httpx.HTTPStatusError as e: - error_msg = f"API 请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(error_msg) - logger.error(f"错误响应: {e.response.text}") - raise Exception(error_msg) - except httpx.RequestError as e: - error_msg = f"API 请求异常: {url}, 错误: {str(e)}" - logger.error(error_msg) - raise Exception(error_msg) - except Exception as e: - error_msg = f"处理 API 响应时出错: {str(e)}" - logger.error(error_msg, exc_info=True) - raise Exception(error_msg) - - def test_sql_with_schema(self, request_data: Dict[str, Any]) -> Dict[str, Any]: - """Call backend SQL test endpoint.""" - try: - logger.info("=" * 80) - logger.info("test_sql_with_schema 接口接收到的数据:") - logger.info(f"数据类型: {type(request_data)}") - logger.info(f"数据内容: {json.dumps(request_data, ensure_ascii=False, indent=2)}") - logger.info(f"数据源 ID: {request_data.get('datasourceId')}") - logger.info(f"业务名称: {request_data.get('businessName')}") - logger.info(f"业务描述: {request_data.get('businessDescription')}") - logger.info(f"SQL 模板: {request_data.get('sqlTemplate')}") - logger.info(f"参数定义: {request_data.get('parameters')}") - logger.info(f"测试参数: {request_data.get('testParams')}") - if "userId" in request_data: - logger.info(f"用户 ID: {request_data.get('userId')}") - logger.info("=" * 80) - - url = f"{self.base_url}/datasource/sqlExecutionLog/testSqlWithSchema" - headers = self._get_headers() - headers["Content-Type"] = "application/json" - headers["Accept"] = "*/*" - - logger.info(f"正在调用测试 SQL API: {url}") - logger.debug(f"请求数据: {json.dumps(request_data, ensure_ascii=False, indent=2)}") - - response = self.client.post(url, headers=headers, json=request_data) - response.raise_for_status() - data = response.json() - - logger.info("=" * 80) - logger.info("test_sql_with_schema 接口返回的数据:") - logger.info(f"HTTP 状态码: {response.status_code}") - logger.info(f"响应数据类型: {type(data)}") - logger.info(f"响应数据内容: {json.dumps(data, ensure_ascii=False, indent=2)}") - if isinstance(data, dict): - logger.info(f"响应 code: {data.get('code')}") - logger.info(f"响应 msg: {data.get('msg')}") - logger.info(f"响应 data: {data.get('data')}") - logger.info("=" * 80) - - logger.info("测试 SQL API 调用成功") - return data - - except httpx.TimeoutException: - error_msg = f"测试 SQL API 请求超时: {url}" - logger.error(error_msg) - raise Exception(error_msg) - except httpx.HTTPStatusError as e: - error_msg = f"测试 SQL API 请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(error_msg) - logger.error(f"错误响应: {e.response.text}") - raise Exception(error_msg) - except httpx.RequestError as e: - error_msg = f"测试 SQL API 请求异常: {url}, 错误: {str(e)}" - logger.error(error_msg) - raise Exception(error_msg) - except Exception as e: - error_msg = f"处理测试 SQL API 响应时出错: {str(e)}" - logger.error(error_msg, exc_info=True) - raise Exception(error_msg) - - def close(self): - """Close the underlying HTTP client.""" - self.client.close() - - -default_client = DataSourceAPIClient() - - -def get_skill_by_id( - skill_id: str, - base_url: Optional[str] = None, - token: Optional[str] = None -) -> Dict[str, Any]: - """Convenience wrapper for skill lookup.""" - if base_url or token: - client = DataSourceAPIClient(base_url=base_url, token=token) - return client.get_skill_by_id(skill_id) - return default_client.get_skill_by_id(skill_id) - - -def test_sql_with_schema( - request_data: Dict[str, Any], - base_url: Optional[str] = None, - token: Optional[str] = None -) -> Dict[str, Any]: - """Convenience wrapper for SQL test endpoint.""" - if base_url or token: - client = DataSourceAPIClient(base_url=base_url, token=token) - return client.test_sql_with_schema(request_data) - return default_client.test_sql_with_schema(request_data) - - -def process_skill_response(response: Dict[str, Any]) -> List[Dict[str, Any]]: - """Map backend skill response into business query configs.""" - try: - data_list = response.get("data", []) - queries = [] - - for skill in data_list: - sql_params_raw = skill.get("sqlParams") - sql_params: Dict[str, Any] = {} - - if isinstance(sql_params_raw, dict): - sql_params = sql_params_raw - elif isinstance(sql_params_raw, str) and sql_params_raw.strip(): - try: - parsed_sql_params = json.loads(sql_params_raw) - if isinstance(parsed_sql_params, dict): - sql_params = parsed_sql_params - else: - logger.warning( - f"技能 {skill.get('name')} (ID: {skill.get('id')}) 的 sqlParams 不是对象，已回退为空对象" - ) - except json.JSONDecodeError: - logger.warning( - f"技能 {skill.get('name')} (ID: {skill.get('id')}) 的 sqlParams 不是合法 JSON，已回退为空对象" - ) - - query = { - "id": skill.get("id"), - "businessName": skill.get("name"), - "businessDescription": skill.get("description"), - "sqlTemplate": skill.get("sqlTemplate"), - "parameters": sql_params, - "datasourceId": skill.get("datasourceId"), - } - queries.append(query) - - logger.info(f"成功处理 {len(queries)} 条技能数据") - return queries - except Exception as e: - logger.error(f"处理 API 响应数据失败: {e}", exc_info=True) - raise diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/env_config.py b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/env_config.py deleted file mode 100644 index 418b0aa..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/env_config.py +++ /dev/null @@ -1,87 +0,0 @@ -"""环境变量配置模块""" - -import os - - -def get_database_id(default: str = "29") -> str: - """ - 获取数据库ID环境变量 - - Args: - default: 默认值（默认为 "29"） - - Returns: - str: 数据库ID - - Environment Variables: - databaseId: 数据库ID - """ - return os.environ.get("databaseId", default) - - -def get_skill_id(default: str = "") -> str: - """ - 获取技能ID环境变量 - - Args: - default: 默认值（默认为 ""） - - Returns: - str: 技能ID - - Environment Variables: - skillId: 技能ID - """ - return os.environ.get("skillId", default) - - -def get_backend_base_url(default: str = "http://lzwcai-demp-corp-manager:8086") -> str: - """ - 获取后端API基础URL环境变量 - - Args: - default: 默认值（默认为 "http://lzwcai-demp-corp-manager:8086"） - - Returns: - str: 后端API基础URL - - Environment Variables: - backendBaseUrl: 后端API基础URL - """ - return os.environ.get("backendBaseUrl", default) - - -def get_env_config() -> dict: - """ - 获取所有环境配置 - - Returns: - dict: 包含所有配置的字典 - - Example: - config = get_env_config() - print(config['database_id']) # 输出: "29" - print(config['skill_id']) # 输出: "" - print(config['backend_base_url']) # 输出: "http://lzwcai-demp-corp-manager:8086" - """ - return { - "database_id": get_database_id(), - "skill_id": get_skill_id(), - "backend_base_url": get_backend_base_url() - } - - -def set_env_variable(key: str, value: str) -> None: - """ - 设置环境变量（仅在当前进程中有效） - - Args: - key: 环境变量名 - value: 环境变量值 - - Example: - set_env_variable("databaseId", "30") - set_env_variable("skillId", "1234567890") - """ - os.environ[key] = value - diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/json_helper.py b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/json_helper.py deleted file mode 100644 index 1a6fc95..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/json_helper.py +++ /dev/null @@ -1,60 +0,0 @@ -"""JSON 文件读取工具""" - -import json -from pathlib import Path -from typing import Any, Union - - -def load_json(json_path: Union[str, Path]) -> Any: - """ - 读取 JSON 文件并返回其内容 - - Args: - json_path: JSON 文件的路径（支持字符串或 Path 对象） - - Returns: - JSON 文件中解析后的数据（可以是字典、列表或其他 JSON 类型） - - Raises: - FileNotFoundError: 当文件不存在时 - json.JSONDecodeError: 当 JSON 格式无效时 - Exception: 其他读取错误 - - Example: - >>> data = load_json('config.json') - >>> print(data) - {'key': 'value'} - - >>> data = load_json(Path('data/users.json')) - >>> print(data) - [{'id': 1, 'name': 'Alice'}] - """ - try: - # 转换为 Path 对象 - path = Path(json_path) - - # 检查文件是否存在 - if not path.exists(): - raise FileNotFoundError(f"JSON 文件不存在: {json_path}") - - # 检查是否为文件 - if not path.is_file(): - raise ValueError(f"路径不是一个文件: {json_path}") - - # 读取并解析 JSON 文件 - with open(path, 'r', encoding='utf-8') as f: - data = json.load(f) - - return data - - except json.JSONDecodeError as e: - raise json.JSONDecodeError( - f"JSON 格式错误: {e.msg}", - e.doc, - e.pos - ) - except FileNotFoundError: - raise - except Exception as e: - raise Exception(f"读取 JSON 文件时发生错误: {str(e)}") - diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/logger_config.py b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/logger_config.py deleted file mode 100644 index 07c411e..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/logger_config.py +++ /dev/null @@ -1,489 +0,0 @@ -# -*- coding: utf-8 -*- -""" -统一日志配置模块 -提供系统级别的日志配置和管理 -""" - -import os -import sys -import logging -from logging.handlers import RotatingFileHandler, TimedRotatingFileHandler -from datetime import datetime -from pathlib import Path - - -class LoggerConfig: - """日志配置管理类""" - - def __init__(self, logs_dir: str = None): - """初始化日志配置 - - Args: - logs_dir: 日志目录路径，默认为项目根目录下的logs文件夹 - """ - # 确定日志目录 - if logs_dir: - self.logs_dir = Path(logs_dir) - else: - # 获取项目根目录（logger_config.py 在 utils 目录下，需要上升两层到达项目根目录） - project_root = Path(__file__).parent.parent - self.logs_dir = project_root / "logs" - - # 创建日志目录（包括父目录） - self.logs_dir.mkdir(parents=True, exist_ok=True) - - # 日志格式 - self.log_format = '%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s' - self.date_format = '%Y-%m-%d %H:%M:%S' - - # 从环境变量获取日志级别，默认为INFO - self.log_level = self._get_log_level_from_env() - - # 是否已初始化 - self._initialized = False - - def _get_log_level_from_env(self) -> int: - """从环境变量获取日志级别 - - Returns: - int: 日志级别 - """ - log_level_str = os.getenv('LOG_LEVEL', 'INFO').upper() - - # 日志级别映射 - level_mapping = { - 'DEBUG': logging.DEBUG, - 'INFO': logging.INFO, - 'WARNING': logging.WARNING, - 'WARN': logging.WARNING, # 兼容性别名 - 'ERROR': logging.ERROR, - 'CRITICAL': logging.CRITICAL, - 'FATAL': logging.CRITICAL # 兼容性别名 - } - - return level_mapping.get(log_level_str, logging.INFO) - - def setup_logging(self, - app_name: str = "lzwcai_mcp_sqlexecutor", - log_level: int = logging.INFO, - max_file_size: int = 10 * 1024 * 1024, # 10MB - backup_count: int = 5, - console_output: bool = True) -> logging.Logger: - """设置系统日志配置 - - Args: - app_name: 应用名称，用于日志文件命名 - log_level: 日志级别 - max_file_size: 单个日志文件最大大小（字节） - backup_count: 保留的备份文件数量 - console_output: 是否输出到控制台 - - Returns: - logging.Logger: 配置好的根日志器 - """ - if self._initialized: - return logging.getLogger() - - # 设置根日志器 - root_logger = logging.getLogger() - root_logger.setLevel(log_level) - - # 清除现有的处理器 - for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) - - # 创建格式化器 - formatter = logging.Formatter(self.log_format, self.date_format) - - # 1. 主日志文件 - 按大小滚动 - main_log_file = self.logs_dir / f"{app_name}.log" - file_handler = RotatingFileHandler( - main_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - file_handler.setLevel(log_level) - file_handler.setFormatter(formatter) - root_logger.addHandler(file_handler) - - # 2. 错误日志文件 - 只记录ERROR及以上级别 - error_log_file = self.logs_dir / f"{app_name}_error.log" - error_handler = RotatingFileHandler( - error_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - error_handler.setLevel(logging.ERROR) - error_handler.setFormatter(formatter) - root_logger.addHandler(error_handler) - - # 3. 按日期滚动的日志文件 - daily_log_file = self.logs_dir / f"{app_name}_daily.log" - daily_handler = TimedRotatingFileHandler( - daily_log_file, - when='midnight', - interval=1, - backupCount=30, # 保留30天 - encoding='utf-8' - ) - daily_handler.setLevel(log_level) - daily_handler.setFormatter(formatter) - daily_handler.suffix = "%Y-%m-%d" - root_logger.addHandler(daily_handler) - - # 4. 控制台输出 - # 重要：MCP协议使用stdio时，必须将日志输出到stderr，stdout仅用于JSON-RPC通信 - if console_output: - console_handler = logging.StreamHandler(sys.stderr) - console_handler.setLevel(log_level) - console_formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s', - self.date_format - ) - console_handler.setFormatter(console_formatter) - root_logger.addHandler(console_handler) - - self._initialized = True - - # 记录初始化信息 - root_logger.info(f"日志系统初始化完成 - 日志目录: {self.logs_dir}") - root_logger.info(f"日志配置 - 级别: {logging.getLevelName(log_level)}, 文件大小限制: {max_file_size//1024//1024}MB, 备份数量: {backup_count}") - - return root_logger - - def get_module_logger(self, module_name: str) -> logging.Logger: - """获取模块专用日志器 - - Args: - module_name: 模块名称 - - Returns: - logging.Logger: 模块日志器 - """ - return logging.getLogger(module_name) - - def create_component_logger(self, - component_name: str, - log_file: str = None, - log_level: int = None) -> logging.Logger: - """为特定组件创建独立的日志器 - - Args: - component_name: 组件名称 - log_file: 独立日志文件名（可选） - log_level: 日志级别（可选） - - Returns: - logging.Logger: 组件日志器 - """ - logger = logging.getLogger(component_name) - - if log_file: - # 为组件创建独立的日志文件 - component_log_file = self.logs_dir / log_file - handler = RotatingFileHandler( - component_log_file, - maxBytes=5 * 1024 * 1024, # 5MB - backupCount=3, - encoding='utf-8' - ) - - formatter = logging.Formatter(self.log_format, self.date_format) - handler.setFormatter(formatter) - - if log_level: - handler.setLevel(log_level) - - logger.addHandler(handler) - logger.info(f"组件日志器创建完成: {component_name} -> {component_log_file}") - - return logger - - def setup_mqtt_logging(self) -> logging.Logger: - """设置MQTT专用日志 - - Returns: - logging.Logger: MQTT日志器 - """ - return self.create_component_logger( - "mqtt_communication", - "mqtt_communication.log", - logging.DEBUG - ) - - def setup_mcp_logging(self) -> logging.Logger: - """设置MCP专用日志 - - Returns: - logging.Logger: MCP日志器 - """ - return self.create_component_logger( - "mcp_services", - "mcp_services.log", - logging.DEBUG - ) - - def setup_api_logging(self) -> logging.Logger: - """设置API专用日志 - - Returns: - logging.Logger: API日志器 - """ - return self.create_component_logger( - "api_requests", - "api_requests.log", - logging.INFO - ) - - def get_logs_info(self) -> dict: - """获取日志系统信息 - - Returns: - dict: 日志系统信息 - """ - log_files = [] - if self.logs_dir.exists(): - for log_file in self.logs_dir.glob("*.log*"): - stat = log_file.stat() - log_files.append({ - "name": log_file.name, - "size": stat.st_size, - "modified": datetime.fromtimestamp(stat.st_mtime).isoformat() - }) - - return { - "logs_directory": str(self.logs_dir), - "initialized": self._initialized, - "log_files": log_files, - "total_files": len(log_files) - } - - def cleanup_old_logs(self, days: int = 30): - """清理旧日志文件 - - Args: - days: 保留天数 - """ - if not self.logs_dir.exists(): - return - - from datetime import timedelta - cutoff_time = datetime.now() - timedelta(days=days) - - cleaned_files = [] - for log_file in self.logs_dir.glob("*.log*"): - if log_file.stat().st_mtime < cutoff_time.timestamp(): - try: - log_file.unlink() - cleaned_files.append(log_file.name) - except Exception as e: - logging.error(f"清理日志文件失败: {log_file.name}, 错误: {e}") - - if cleaned_files: - logging.info(f"清理了 {len(cleaned_files)} 个旧日志文件: {cleaned_files}") - - def set_log_level(self, level: int, logger_name: str = None): - """动态调整日志级别 - - Args: - level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL) - logger_name: 指定日志器名称，None表示调整根日志器 - """ - if logger_name: - logger = logging.getLogger(logger_name) - else: - logger = logging.getLogger() - - old_level = logger.level - logger.setLevel(level) - - # 同时调整所有处理器的级别 - for handler in logger.handlers: - if not isinstance(handler, logging.StreamHandler) or handler.stream not in (sys.stdout, sys.stderr): - # 不调整控制台处理器的级别，保持原有设置 - handler.setLevel(level) - - level_name = logging.getLevelName(level) - old_level_name = logging.getLevelName(old_level) - target = logger_name or "根日志器" - - logger.info(f"日志级别已调整: {target} {old_level_name} -> {level_name}") - - def set_temporary_log_level(self, level: int, logger_name: str = None): - """临时调整日志级别（会保存原始级别用于恢复） - - Args: - level: 临时日志级别 - logger_name: 指定日志器名称，None表示调整根日志器 - """ - if not hasattr(self, '_original_levels'): - self._original_levels = {} - - target_name = logger_name or 'root' - - if logger_name: - logger = logging.getLogger(logger_name) - else: - logger = logging.getLogger() - - # 保存原始级别 - if target_name not in self._original_levels: - self._original_levels[target_name] = logger.level - - # 设置新级别 - self.set_log_level(level, logger_name) - - level_name = logging.getLevelName(level) - target = logger_name or "根日志器" - logger.info(f"临时调整日志级别: {target} -> {level_name} (可通过restore_log_level恢复)") - - def restore_log_level(self, logger_name: str = None): - """恢复日志级别到调整前的状态 - - Args: - logger_name: 指定日志器名称，None表示恢复根日志器 - """ - if not hasattr(self, '_original_levels'): - logging.warning("没有找到保存的原始日志级别") - return - - target_name = logger_name or 'root' - - if target_name not in self._original_levels: - logging.warning(f"没有找到 {target_name} 的原始日志级别") - return - - original_level = self._original_levels[target_name] - self.set_log_level(original_level, logger_name) - - # 清除保存的级别 - del self._original_levels[target_name] - - target = logger_name or "根日志器" - level_name = logging.getLevelName(original_level) - logging.info(f"已恢复日志级别: {target} -> {level_name}") - - def get_current_log_levels(self) -> dict: - """获取当前所有日志器的级别信息 - - Returns: - dict: 日志器级别信息 - """ - levels_info = {} - - # 根日志器 - root_logger = logging.getLogger() - levels_info['root'] = { - 'level': root_logger.level, - 'level_name': logging.getLevelName(root_logger.level), - 'handlers_count': len(root_logger.handlers) - } - - # 其他已创建的日志器 - for name, logger in logging.Logger.manager.loggerDict.items(): - if isinstance(logger, logging.Logger): - levels_info[name] = { - 'level': logger.level, - 'level_name': logging.getLevelName(logger.level), - 'handlers_count': len(logger.handlers) - } - - return levels_info - - -# 全局日志配置实例 -logger_config = LoggerConfig() - - -def setup_system_logging(app_name: str = "lzwcai_mcp_sqlexecutor", - log_level: int = logging.INFO) -> logging.Logger: - """系统日志初始化快捷函数 - - Args: - app_name: 应用名称 - log_level: 日志级别 - - Returns: - logging.Logger: 根日志器 - """ - return logger_config.setup_logging(app_name, log_level) - - -def get_logger(name: str) -> logging.Logger: - """获取日志器的快捷函数 - - Args: - name: 日志器名称 - - Returns: - logging.Logger: 日志器实例 - """ - return logger_config.get_module_logger(name) - - -def set_log_level(level: int, logger_name: str = None): - """动态调整日志级别的快捷函数 - - Args: - level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL) - logger_name: 指定日志器名称，None表示调整根日志器 - - Examples: - # 调整根日志器为DEBUG级别 - set_log_level(logging.DEBUG) - - # 调整特定模块日志器为WARNING级别 - set_log_level(logging.WARNING, "agent_ontology.core") - """ - logger_config.set_log_level(level, logger_name) - - -def set_temporary_log_level(level: int, logger_name: str = None): - """临时调整日志级别的快捷函数 - - Args: - level: 临时日志级别 - logger_name: 指定日志器名称，None表示调整根日志器 - - Examples: - # 临时调整为DEBUG级别进行调试 - set_temporary_log_level(logging.DEBUG) - # ... 进行调试 ... - # 恢复原始级别 - restore_log_level() - """ - logger_config.set_temporary_log_level(level, logger_name) - - -def restore_log_level(logger_name: str = None): - """恢复日志级别的快捷函数 - - Args: - logger_name: 指定日志器名称，None表示恢复根日志器 - """ - logger_config.restore_log_level(logger_name) - - -def get_current_log_levels() -> dict: - """获取当前日志级别信息的快捷函数 - - Returns: - dict: 日志器级别信息 - - Examples: - levels = get_current_log_levels() - print(f"根日志器级别: {levels['root']['level_name']}") - """ - return logger_config.get_current_log_levels() - - -# 便捷的日志级别常量 -class LogLevel: - """日志级别常量类""" - DEBUG = logging.DEBUG - INFO = logging.INFO - WARNING = logging.WARNING - ERROR = logging.ERROR - CRITICAL = logging.CRITICAL \ No newline at end of file diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/name_helper.py b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/name_helper.py deleted file mode 100644 index d66cf32..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/name_helper.py +++ /dev/null @@ -1,41 +0,0 @@ -# -*- coding: utf-8 -*- -""" -名称生成工具模块 -""" - -from pypinyin import lazy_pinyin, Style -import logging - -logger = logging.getLogger(__name__) - - -def generate_tool_name(business_name: str, tool_id: str) -> str: - """ - 根据业务名称和ID生成工具名称 - 格式: tool_拼音_id - - Args: - business_name: 业务名称（中文） - tool_id: 工具ID - - Returns: - str: 格式化的工具名称 - """ - try: - # 将中文转换为拼音（无音调，小写） - pinyin_list = lazy_pinyin(business_name, style=Style.NORMAL) - # 拼接拼音 - pinyin_str = ''.join(pinyin_list) - - # 将 ID 中的 '-' 替换为 '_' - formatted_id = tool_id.replace('-', '_') - - # 组合成最终的工具名称 - tool_name = f"tool_{pinyin_str}_{formatted_id}" - - return tool_name - except Exception as e: - logger.error(f"生成工具名称失败: {business_name}, {tool_id}, 错误: {e}", exc_info=True) - # 降级处理：如果拼音转换失败，使用 ID - return f"tool_{tool_id.replace('-', '_')}" - diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/schema_helper.py b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/schema_helper.py deleted file mode 100644 index 01f4bd4..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/utils/schema_helper.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Schema 生成工具模块。 -""" - -from copy import deepcopy -from typing import Any, Dict - - -def _normalize_schema(parameters: Dict[str, Any] | None) -> Dict[str, Any]: - """将任意参数定义归一化为 object schema。""" - if not isinstance(parameters, dict) or not parameters: - return { - "type": "object", - "properties": {}, - "required": [] - } - - input_schema = deepcopy(parameters) - input_schema["type"] = "object" - - if not isinstance(input_schema.get("properties"), dict): - input_schema["properties"] = {} - - if not isinstance(input_schema.get("required"), list): - input_schema["required"] = [] - - return input_schema - - -def generate_input_schema(parameters: Dict[str, Any] | None) -> Dict[str, Any]: - """ - 从查询配置的参数定义生成 MCP 工具的 inputSchema。 - - 会统一补齐： - - `targetDatabaseName`：可选 - - `userId`：必填 - """ - input_schema = _normalize_schema(parameters) - - if "targetDatabaseName" not in input_schema["properties"]: - input_schema["properties"]["targetDatabaseName"] = { - "type": "string", - "description": "目标数据库名称", - "default": "" - } - - if "userId" not in input_schema["properties"]: - input_schema["properties"]["userId"] = { - "type": "string", - "description": "当前 AI 平台用户 ID" - } - - if "userId" not in input_schema["required"]: - input_schema["required"].append("userId") - - return input_schema - - -def validate_input_schema(schema: Dict[str, Any]) -> tuple[bool, str]: - """ - 验证 inputSchema 是否符合基本的 JSON Schema 规范。 - """ - if not isinstance(schema, dict): - return False, "Schema 必须是一个字典对象" - - if schema.get("type") != "object": - return False, "Schema 的 type 字段必须是 'object'" - - if "properties" not in schema: - return False, "Schema 必须包含 properties 字段" - - if not isinstance(schema.get("properties"), dict): - return False, "Schema 的 properties 字段必须是一个字典对象" - - if "required" in schema: - required = schema["required"] - if not isinstance(required, list): - return False, "Schema 的 required 字段必须是一个列表" - - properties = schema["properties"] - for field in required: - if field not in properties: - return False, f"必填字段 '{field}' 未在 properties 中定义" - - for prop_name, prop_def in schema["properties"].items(): - if not isinstance(prop_def, dict): - return False, f"属性 '{prop_name}' 的定义必须是一个字典对象" - - if "type" not in prop_def: - return False, f"属性 '{prop_name}' 必须包含 type 字段" - - return True, "Schema 验证通过" diff --git a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/uv.lock b/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/uv.lock deleted file mode 100644 index c541de4..0000000 --- a/lzwcai_mcp_sqlexecutor/lzwcai_mcp_sqlexecutor/uv.lock +++ /dev/null @@ -1,497 +0,0 @@ -version = 1 -revision = 2 -requires-python = ">=3.13" - -[[package]] -name = "annotated-types" -version = "0.7.0" -source = { registry = "http://devpi.iepai.fun/lzwc/dev/+simple/" } -sdist = { url = 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a/lzwcai_mcp_sqlexecutor/pyproject.toml +++ /dev/null @@ -1,38 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai-mcp-sqlexecutor" -version = "0.1.13" -description = "MCP server for executing business SQL queries with dynamic tool generation" -readme = "README.md" -requires-python = ">=3.10" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "sql", "executor", "server"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", - "pypinyin>=0.53.0", -] - -[project.scripts] -lzwcai-mcp-sqlexecutor = "lzwcai_mcp_sqlexecutor.main:main" - 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客户端配置文件中添加： - -```json -{ - "mcpServers": { - "lzwcai-sqlexecutor": { - "command": "lzwcai-mcpskills-analyzeOrder" - } - } -} -``` - -## 配置说明 - -### businessQueries.json - -在 `businessQueries.json` 中定义你的业务查询： - -```json -[ - { - "id": "query-001", - "businessName": "用户订单查询", - "businessDescription": "根据用户ID查询订单信息", - "sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}", - "parameters": { - "type": "object", - "required": ["userId"], - "properties": { - "userId": { - "type": "integer", - "description": "用户的唯一标识符", - "examples": [10086] - } - } - } - } -] -``` - -## 开发 - -### 依赖项 - -- Python >= 3.13 -- httpx >= 0.28.1 -- mcp[cli] >= 1.10.1 -- pypinyin >= 0.53.0 - -### 本地开发 - -```bash -# 克隆仓库 -git clone -cd lzwcai_mcp_sqlexecutor - -# 安装开发依赖 -pip install -e . - -# 运行服务器 -python -m lzwcai_mcp_sqlexecutor.main -``` - -## 构建与发布 - -### 使用 build 构建 - -```bash -pip install build -python -m build -``` - -### 发布到 PyPI - -```bash -pip install twine -twine upload dist/* -``` - -## 许可证 - -MIT License - -## 作者 - -lzwcai - diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/.gitignore b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/.gitignore deleted file mode 100644 index 505a3b1..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/.gitignore +++ /dev/null @@ -1,10 +0,0 @@ -# Python-generated files -__pycache__/ -*.py[oc] -build/ -dist/ -wheels/ -*.egg-info - -# Virtual environments -.venv diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/.python-version b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/.python-version deleted file mode 100644 index 24ee5b1..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.13 diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/README.md b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/README.md deleted file mode 100644 index 0ec9671..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/README.md +++ /dev/null @@ -1,154 +0,0 @@ -# lzwcai-mcpskills-analyzeWorkOrder - -一个基于 MCP (Model Context Protocol) 的 SQL 查询执行服务器，支持从 JSON 配置文件动态生成查询工具。 - -## 功能特性 - -- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具 -- 🔧 灵活配置：支持自定义业务查询和参数验证 -- 📝 完整日志：详细的操作日志记录（仅输出到文件，不干扰MCP通信） -- 🌐 中文支持：工具名称自动转换为拼音 - -## 安装 - -### 使用 pip 安装 - -```bash -pip install lzwcai-mcpskills-analyzeWorkOrder -``` - -### 从源码安装 - -```bash -git clone -cd lzwcai_mcp_sqlexecutor -pip install -e . -``` - -### 使用 uv 安装（推荐） - -```bash -uv pip install lzwcai-mcpskills-analyzeWorkOrder -``` - -## 使用方法 - -### 命令行启动 - -安装后，可以直接通过命令启动： - -```bash -lzwcai-mcpskills-analyzeWorkOrder -``` - -### 作为 Python 模块运行 - -```bash -python -m lzwcai_mcp_sqlexecutor.main -``` - -### 配置到 MCP 客户端 - -在你的 MCP 客户端配置文件中添加： - -```json -{ - "mcpServers": { - "lzwcai-sqlexecutor": { - "command": "lzwcai-mcpskills-analyzeWorkOrder" - } - } -} -``` - -## 配置说明 - -### businessQueries.json - -在 `businessQueries.json` 中定义你的业务查询： - -```json -[ - { - "id": "query-001", - "businessName": "用户订单查询", - "businessDescription": "根据用户ID查询订单信息", - "sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}", - "parameters": { - "type": "object", - "required": ["userId"], - "properties": { - "userId": { - "type": "integer", - "description": "用户的唯一标识符", - "examples": [10086] - } - } - } - } -] -``` - -## 开发 - -### 依赖项 - -- Python >= 3.13 -- httpx >= 0.28.1 -- mcp[cli] >= 1.10.1 -- pypinyin >= 0.53.0 - -### 本地开发 - -```bash -# 克隆仓库 -git clone -cd lzwcai_mcp_sqlexecutor - -# 安装开发依赖 -pip install -e . - -# 运行服务器 -python -m lzwcai_mcp_sqlexecutor.main -``` - -## 构建与发布 - -### 使用 build 构建 - -```bash -pip install build -python -m build -``` - -### 发布到 PyPI - -```bash -pip install twine -twine upload dist/* -``` - -## 常见问题 - -### MCP Inspector 显示 JSON 解析错误 - -如果在使用 MCP Inspector 测试时遇到 `SyntaxError: Unexpected non-whitespace character after JSON` 错误，这是因为： - -1. **原因**：MCP 协议使用 stdio（标准输入输出）进行 JSON-RPC 通信，任何输出到 stdout 的内容（如 print 语句或控制台日志）都会破坏 JSON 格式。 - -2. **解决方案**：本服务器已将所有日志输出配置为仅写入文件（位于 `logs/` 目录），不输出到控制台。日志文件包括： - - `lzwcai_mcp_sqlexecutor.log` - 主日志文件 - - `lzwcai_mcp_sqlexecutor_error.log` - 错误日志 - - `lzwcai_mcp_sqlexecutor_daily.log` - 按日期滚动的日志 - - `mcp_services.log` - MCP 服务专用日志 - -3. **查看日志**：如果需要调试，请查看 `logs/` 目录下的日志文件。 - -## 许可证 - -MIT License - -## 作者 - -lzwcai - diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/__init__.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/__init__.py deleted file mode 100644 index 3e52bf0..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -""" -lzwcai-mcpskills-analyzeOrder - MCP server for executing business SQL queries -""" - -__version__ = "0.1.2" -__author__ = "lzwcai" - -__all__ = [] - diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json deleted file mode 100644 index a180e7e..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json +++ /dev/null @@ -1,35 +0,0 @@ -[ - { - "id": "2006300000000000006", - "businessName": "SalesOrderList", - "businessDescription": "历史订单列表：查询所有销售订单，包含客户信息、订单金额、收款状态、交货日期等关键信息", - "datasourceId": "19", - "sqlTemplate": "SELECT so.sales_order_id, so.sales_order_number, c.customer_name, so.contract_number, so.order_date_utc, so.delivery_date_utc, so.deal_amount, so.currency_code, so.payment_status, so.stage, so.result, so.transport_mode, so.production_doc_number, so.event_time_utc FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = true ORDER BY so.order_date_utc DESC", - "parameters": {} - }, - { - "id": "2006300000000000007", - "businessName": "SalesOrderDetail", - "businessDescription": "订单详情：根据订单编号查询订单详细信息，包含客户信息、销售员、合同、金额、交货日期、收款状态等完整信息", - "datasourceId": "19", - "sqlTemplate": "SELECT so.sales_order_id, so.sales_order_number, c.customer_name, c.customer_level, c.phone AS customer_phone, c.email AS customer_email, c.address AS customer_address, p.person_name AS sales_person_name, so.contract_number, ct.contract_name, ct.contract_amount, so.order_date_utc, so.delivery_date_utc, so.deal_amount, so.currency_code, so.fx_rate, so.payment_status, so.transport_mode, so.packaging, so.customs_number, so.production_doc_number, so.stage, so.result, so.event_time_utc FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = true LEFT JOIN dim_person p ON so.sales_person_id = p.person_id AND p.is_current = true LEFT JOIN dim_contract ct ON so.contract_id = ct.contract_id AND ct.is_current = true WHERE so.sales_order_number = '{salesOrderNumber}'", - "parameters": { - "salesOrderNumber": { - "type": "string", - "description": "销售订单编号", - "required": true, - "examples": [ - "SO20251225001" - ] - } - } - }, - { - "id": "2006300000000000008", - "businessName": "DeliveryRiskPrediction", - "businessDescription": "交付风险预测：基于历史订单的生产周期、物流延误、设备故障等特征，计算延迟概率与红/黄/绿预警等级", - "datasourceId": "19", - "sqlTemplate": "WITH global_production AS (SELECT COUNT(*) AS total_wo_count, AVG(CASE WHEN planned_qty > 0 THEN completed_qty / planned_qty ELSE 0 END) AS avg_completion_rate, SUM(CASE WHEN status IN ('OPEN', 'STARTED') THEN 1 ELSE 0 END) AS pending_wo_count, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_wo_count FROM fact_work_order), global_quality AS (SELECT COUNT(*) AS total_inspection_count, SUM(COALESCE(pass_qty, 0)) AS total_pass_qty, SUM(COALESCE(fail_qty, 0)) AS total_fail_qty, CASE WHEN SUM(COALESCE(pass_qty, 0) + COALESCE(fail_qty, 0)) > 0 THEN SUM(COALESCE(pass_qty, 0))::FLOAT / SUM(COALESCE(pass_qty, 0) + COALESCE(fail_qty, 0)) ELSE 1 END AS qc_pass_rate FROM fact_quality_inspection), global_operation AS (SELECT COUNT(*) AS total_task_count, SUM(COALESCE(good_qty, 0)) AS total_good_qty, SUM(COALESCE(bad_qty, 0)) AS total_bad_qty, CASE WHEN SUM(COALESCE(good_qty, 0) + COALESCE(bad_qty, 0)) > 0 THEN SUM(COALESCE(bad_qty, 0))::FLOAT / SUM(COALESCE(good_qty, 0) + COALESCE(bad_qty, 0)) ELSE 0 END AS operation_defect_rate FROM fact_operation_task), customer_shipment AS (SELECT customer_id, COUNT(*) AS shipment_count, SUM(COALESCE(amount, 0)) AS total_shipment_amount FROM fact_sales_shipment GROUP BY customer_id), customer_return AS (SELECT customer_id, COUNT(*) AS return_count, SUM(COALESCE(amount, 0)) AS total_return_amount FROM fact_sales_return GROUP BY customer_id), order_risk AS (SELECT so.sales_order_id, so.sales_order_number, c.customer_name, so.order_date_utc, so.deal_amount, so.payment_status, gp.avg_completion_rate AS production_completion_rate, gp.pending_wo_count, gp.total_wo_count AS work_order_count, gq.qc_pass_rate, gq.total_fail_qty, go.operation_defect_rate, COALESCE(cs.shipment_count, 0) AS shipment_count, COALESCE(cr.return_count, 0) AS return_count, CASE WHEN COALESCE(cs.shipment_count, 0) > 0 THEN COALESCE(cr.return_count, 0)::FLOAT / cs.shipment_count ELSE 0 END AS return_rate FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = true CROSS JOIN global_production gp CROSS JOIN global_quality gq CROSS JOIN global_operation go LEFT JOIN customer_shipment cs ON so.customer_id = cs.customer_id LEFT JOIN customer_return cr ON so.customer_id = cr.customer_id) SELECT sales_order_id, sales_order_number, customer_name, order_date_utc, deal_amount, payment_status, work_order_count, ROUND(production_completion_rate::NUMERIC, 2) AS production_completion_rate, pending_wo_count, ROUND(qc_pass_rate::NUMERIC, 2) AS qc_pass_rate, ROUND(operation_defect_rate::NUMERIC, 4) AS operation_defect_rate, return_count, ROUND(return_rate::NUMERIC, 4) AS return_rate, ROUND((CASE WHEN production_completion_rate < 0.3 THEN 0.30 WHEN production_completion_rate < 0.5 THEN 0.20 WHEN production_completion_rate < 0.8 THEN 0.10 ELSE 0 END + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 WHEN qc_pass_rate < 0.9 THEN 0.15 WHEN qc_pass_rate < 0.95 THEN 0.08 ELSE 0 END + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 WHEN operation_defect_rate > 0.05 THEN 0.12 WHEN operation_defect_rate > 0.02 THEN 0.05 ELSE 0 END + CASE WHEN return_rate > 0.1 THEN 0.15 WHEN return_rate > 0.05 THEN 0.08 WHEN return_rate > 0.02 THEN 0.03 ELSE 0 END + CASE WHEN payment_status = 'UNPAID' THEN 0.10 WHEN payment_status = 'PARTIAL' THEN 0.05 ELSE 0 END)::NUMERIC, 2) AS delay_probability, CASE WHEN (CASE WHEN production_completion_rate < 0.3 THEN 0.30 WHEN production_completion_rate < 0.5 THEN 0.20 WHEN production_completion_rate < 0.8 THEN 0.10 ELSE 0 END + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 WHEN qc_pass_rate < 0.9 THEN 0.15 WHEN qc_pass_rate < 0.95 THEN 0.08 ELSE 0 END + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 WHEN operation_defect_rate > 0.05 THEN 0.12 WHEN operation_defect_rate > 0.02 THEN 0.05 ELSE 0 END + CASE WHEN return_rate > 0.1 THEN 0.15 WHEN return_rate > 0.05 THEN 0.08 WHEN return_rate > 0.02 THEN 0.03 ELSE 0 END + CASE WHEN payment_status = 'UNPAID' THEN 0.10 WHEN payment_status = 'PARTIAL' THEN 0.05 ELSE 0 END) >= 0.50 THEN 'RED' WHEN (CASE WHEN production_completion_rate < 0.3 THEN 0.30 WHEN production_completion_rate < 0.5 THEN 0.20 WHEN production_completion_rate < 0.8 THEN 0.10 ELSE 0 END + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 WHEN qc_pass_rate < 0.9 THEN 0.15 WHEN qc_pass_rate < 0.95 THEN 0.08 ELSE 0 END + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 WHEN operation_defect_rate > 0.05 THEN 0.12 WHEN operation_defect_rate > 0.02 THEN 0.05 ELSE 0 END + CASE WHEN return_rate > 0.1 THEN 0.15 WHEN return_rate > 0.05 THEN 0.08 WHEN return_rate > 0.02 THEN 0.03 ELSE 0 END + CASE WHEN payment_status = 'UNPAID' THEN 0.10 WHEN payment_status = 'PARTIAL' THEN 0.05 ELSE 0 END) >= 0.25 THEN 'YELLOW' ELSE 'GREEN' END AS risk_level, CONCAT_WS(' | ', CASE WHEN production_completion_rate < 0.5 THEN '生产进度滞后' END, CASE WHEN qc_pass_rate < 0.9 THEN '质检通过率低' END, CASE WHEN operation_defect_rate > 0.05 THEN '工序不良率高' END, CASE WHEN return_rate > 0.05 THEN '历史退货率高' END, CASE WHEN payment_status = 'UNPAID' THEN '未付款' END) AS risk_reasons FROM order_risk ORDER BY delay_probability DESC, deal_amount DESC", - "parameters": {} - } -] \ No newline at end of file diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/main.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/main.py deleted file mode 100644 index 07f4694..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/main.py +++ /dev/null @@ -1,373 +0,0 @@ -from pathlib import Path -from typing import Any -import asyncio -import logging - -# 支持直接运行和模块导入两种方式 -try: - from .utils import load_json, generate_tool_name, generate_input_schema - from .utils import get_skill_by_id, DataSourceAPIClient, process_skill_response, test_sql_with_schema - from .utils import get_database_id, get_datasource_id, get_skill_id, get_env_config - from .utils.logger_config import logger_config -except ImportError: - from utils import load_json, generate_tool_name, generate_input_schema - from utils import get_skill_by_id, DataSourceAPIClient, process_skill_response, test_sql_with_schema - from utils import get_database_id, get_datasource_id, get_skill_id, get_env_config - from utils.logger_config import logger_config - -from mcp.server.models import InitializationOptions -from mcp.server import NotificationOptions, Server -import mcp.types as types - -# 初始化 MCP 专用日志器 -mcp_logger = logger_config.setup_mcp_logging() - -# ========== 数据源配置 ========== -# 数据源类型常量 -DATA_SOURCE_API = "api" # 仅使用API数据 -DATA_SOURCE_LOCAL = "local" # 仅使用本地JSON数据 -DATA_SOURCE_BOTH = "both" # 合并本地和API数据 - -# 默认数据源（可修改） -DEFAULT_DATA_SOURCE = DATA_SOURCE_LOCAL -# ================================ - - -def get_queries(): - """ - 获取业务查询配置 - - Returns: - list: 包含所有业务查询配置的列表 - """ - try: - # 获取当前文件所在目录 - current_dir = Path(__file__).parent - - # 构建 businessQueries.json 的路径 - json_path = current_dir / "businessQueries.json" - - mcp_logger.debug(f"正在读取业务查询配置文件: {json_path}") - - # 使用 load_json 方法读取 JSON 文件 - queries = load_json(json_path) - - mcp_logger.info(f"成功加载 {len(queries)} 个业务查询配置") - - return queries - except Exception as e: - mcp_logger.error(f"加载业务查询配置失败: {e}", exc_info=True) - raise - - -def generate_tool_schema_from_query(query: dict) -> types.Tool: - """ - 根据查询配置生成 MCP 工具模式 - - Args: - query: 单个查询配置字典 - - Returns: - types.Tool: MCP 工具对象 - """ - try: - # 获取参数定义并生成 inputSchema - parameters = query.get('parameters', {}) - input_schema = generate_input_schema(parameters) - - # 生成工具名称（格式: tool_拼音_id） - # tool_name = generate_tool_name(query['businessName'], query['id']) - tool_name = query['businessName'] - # 构建工具描述，包含业务名称和业务描述 - description = f"{query['businessName']}: {query['businessDescription']}" - - mcp_logger.debug(f"生成工具模式: {tool_name} - {query['businessName']}") - - return types.Tool( - name=tool_name, - description=description, - inputSchema=input_schema - ) - except Exception as e: - mcp_logger.error(f"生成工具模式失败: {query.get('id', 'unknown')}, 错误: {e}", exc_info=True) - raise - - -# 创建 MCP 服务器实例 -server = Server("lzwcai-mcpskills-analyzeOrder") - -# 缓存查询配置，避免重复加载 -_queries_cache = None - - -async def get_queries_cache(source: str = None): - """ - 获取或初始化查询配置缓存 - - Args: - source: 数据源类型（默认使用 DEFAULT_DATA_SOURCE） - - "api": 仅使用API数据 - - "local": 仅使用本地JSON数据 - - "both": 合并本地和API数据 - - Returns: - 查询配置列表 - """ - global _queries_cache - if _queries_cache is None: - source = source or DEFAULT_DATA_SOURCE - mcp_logger.info(f"初始化查询配置（数据源: {source}）...") - - if source == DATA_SOURCE_LOCAL: - _queries_cache = get_queries() - mcp_logger.info(f"本地配置: {len(_queries_cache)} 条") - - elif source == DATA_SOURCE_API: - try: - _queries_cache = await call_third_party_api() - mcp_logger.info(f"API配置: {len(_queries_cache)} 条") - mcp_logger.info(f"API配置数组: {_queries_cache}") - except Exception as e: - mcp_logger.warning(f"API获取失败，降级使用本地配置: {e}") - _queries_cache = get_queries() - - else: # DATA_SOURCE_BOTH - local = get_queries() - try: - api = await call_third_party_api() - except Exception as e: - mcp_logger.warning(f"API获取失败: {e}") - api = [] - _queries_cache = local + api - mcp_logger.info(f"配置总数: {len(_queries_cache)} 条（本地{len(local)}+API{len(api)}）") - - return _queries_cache - - -@server.list_tools() -async def handle_list_tools() -> list[types.Tool]: - """ - 列出所有动态生成的 MCP 工具 - - Returns: - list[types.Tool]: 所有可用的工具列表 - """ - try: - mcp_logger.info("收到列出工具请求") - - queries = await get_queries_cache() - tools = [] - - for query in queries: - tool = generate_tool_schema_from_query(query) - tools.append(tool) - - mcp_logger.info(f"成功生成 {len(tools)} 个 MCP 工具") - mcp_logger.debug(f"工具列表: {[tool.name for tool in tools]}") - - return tools - except Exception as e: - mcp_logger.error(f"列出工具失败: {e}", exc_info=True) - raise - - -@server.call_tool() -async def handle_call_tool( - name: str, - arguments: dict[str, Any] | None -) -> list[types.TextContent]: - """ - 处理工具调用请求 - - Args: - name: 工具名称 - arguments: 工具参数 - - Returns: - list[types.TextContent]: 工具执行结果（返回参数和对应的接口配置） - """ - try: - mcp_logger.info(f"收到工具调用请求: {name}") - mcp_logger.debug(f"工具参数: {arguments}") - - # 获取查询配置缓存 - queries = await get_queries_cache() - - # 根据工具名称查找对应的 item（接口配置） - tool_item = None - for query in queries: - # tool_name = generate_tool_name(query['businessName'], query['id']) - tool_name = query['businessName'] - if tool_name == name: - tool_item = query - break - - # 构建返回结果 - import json - - if tool_item: - request_data = { - "datasourceId": get_datasource_id(), - "businessName": tool_item.get("businessName"), - "businessDescription": tool_item.get("businessDescription"), - "sqlTemplate": tool_item.get("sqlTemplate"), - "parameters": tool_item.get("parameters"), - "testParams": arguments or {} - } - - # 如果 arguments 中有 targetDatabaseName 且有值，添加到 request_data - if arguments and arguments.get("targetDatabaseName"): - request_data["targetDatabaseName"] = arguments["targetDatabaseName"] - mcp_logger.debug(f"添加目标数据库名称: {arguments['targetDatabaseName']}") - - # 调用测试SQL API - try: - mcp_logger.info("正在调用测试SQL API...") - api_response = test_sql_with_schema(request_data) - mcp_logger.info("测试SQL API调用成功") - - # 返回包含 data 字段的结果 - result = { - "success": True, - "data": api_response - } - result_text = json.dumps(result, ensure_ascii=False, indent=2) - - except Exception as e: - error_msg = f"调用测试SQL API失败: {str(e)}" - mcp_logger.error(error_msg, exc_info=True) - result = { - "success": False, - "error": error_msg, - "data": None - } - result_text = json.dumps(result, ensure_ascii=False, indent=2) - else: - error_msg = f"未找到工具 {name} 对应的配置" - result = { - "success": False, - "error": error_msg, - "data": None - } - result_text = json.dumps(result, ensure_ascii=False, indent=2) - - mcp_logger.debug(f"工具调用结果: {result_text}") - - return [ - types.TextContent( - type="text", - text=result_text - ) - ] - except Exception as e: - error_msg = f"工具调用失败: {name}, 错误: {e}" - mcp_logger.error(error_msg, exc_info=True) - return [ - types.TextContent( - type="text", - text=f"错误: {error_msg}" - ) - ] - - -async def call_third_party_api(skill_id: str = None) -> list: - """ - 调用第三方API获取技能信息并返回处理后的数据 - - Args: - skill_id: 技能ID（默认从环境变量 SKILL_ID 读取，如果未设置则使用 1981000305474482178） - - Returns: - 处理后的查询配置列表（businessQueries格式） - - Example: - queries = await call_third_party_api() - # 返回: [{"id": "...", "businessName": "...", ...}, ...] - """ - try: - # 如果没有传入 skill_id，则从环境变量读取 - if skill_id is None: - skill_id = get_skill_id() - - mcp_logger.info(f"调用第三方API，skill_id: {skill_id}") - - # 获取原始数据 - raw_result = get_skill_by_id(skill_id) - - mcp_logger.info(f"成功{raw_result}") - - # 处理并返回 - processed_queries = process_skill_response(raw_result) - - mcp_logger.info(f"成功获取并处理 {len(processed_queries)} 条数据") - return processed_queries - - except Exception as e: - mcp_logger.error(f"API调用失败: {e}", exc_info=True) - raise - - -async def async_main(): - """MCP 服务器异步主函数""" - try: - mcp_logger.info("=" * 60) - mcp_logger.info("正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder") - mcp_logger.info("版本: 0.1.0") - mcp_logger.info("=" * 60) - - # 输出环境配置信息 - env_config = get_env_config() - mcp_logger.info(f"环境配置 - Database ID: {env_config['database_id']}") - mcp_logger.info(f"环境配置 - Datasource ID: {env_config['datasource_id']}") - mcp_logger.info(f"环境配置 - Skill ID: {env_config['skill_id']}") - mcp_logger.info(f"环境配置 - Backend Base URL: {env_config['backend_base_url']}") - mcp_logger.info("=" * 60) - - from mcp.server.stdio import stdio_server - - async with stdio_server() as (read_stream, write_stream): - mcp_logger.info("MCP 服务器已启动，等待客户端连接...") - - await server.run( - read_stream, - write_stream, - InitializationOptions( - server_name="lzwcai-mcpskills-analyzeOrder", - server_version="0.1.0", - capabilities=server.get_capabilities( - notification_options=NotificationOptions(), - experimental_capabilities={}, - ), - ), - ) - - mcp_logger.info("MCP 服务器已关闭") - - except Exception as e: - mcp_logger.error(f"MCP 服务器运行失败: {e}", exc_info=True) - raise - - -def main(): - """入口点函数（用于 console_scripts）""" - try: - # 初始化系统日志 - # MCP协议使用stdio通信，必须禁用控制台输出以避免干扰JSON-RPC通信 - logger_config.setup_logging( - app_name="lzwcai_mcp_sqlexecutor", - log_level=logging.INFO, - console_output=False # 禁用控制台输出 - ) - - mcp_logger.info("开始运行 MCP SQL Executor 服务器") - asyncio.run(async_main()) - - except KeyboardInterrupt: - mcp_logger.info("收到中断信号，正在关闭服务器...") - except Exception as e: - mcp_logger.error(f"程序运行失败: {e}", exc_info=True) - raise - - -if __name__ == "__main__": - main() diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml deleted file mode 100644 index c12ec43..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml +++ /dev/null @@ -1,35 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai-mcpskills-analyzeWorkOrder" -version = "0.1.10" -description = "MCP server for executing business SQL queries with dynamic tool generation" -readme = "README.md" -requires-python = ">=3.13" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "sql", "executor", "server"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.13", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", - "pypinyin>=0.53.0", -] - -[project.scripts] -lzwcai-mcpskills-analyzeWorkOrder = "lzwcai_mcpskills_analyzeWorkOrder.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcpskills_analyzeWorkOrder"] - -[tool.hatch.build.targets.wheel.force-include] -"lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json" = "lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json" diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/sql11 b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/sql11 deleted file mode 100644 index 39b7319..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/sql11 +++ /dev/null @@ -1,169 +0,0 @@ --- ===================================================== --- 交付风险预测：延迟概率与红/黄/绿预警等级 --- 基于历史订单的生产周期、物流延误、设备故障等特征 --- ===================================================== - -WITH --- 1. 全局生产特征（汇总所有工单） -global_production AS ( - SELECT - COUNT(*) AS total_wo_count, - AVG(CASE WHEN planned_qty > 0 THEN completed_qty / planned_qty ELSE 0 END) AS avg_completion_rate, - SUM(CASE WHEN status IN ('OPEN', 'STARTED') THEN 1 ELSE 0 END) AS pending_wo_count, - SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_wo_count - FROM fact_work_order -), - --- 2. 全局质检特征 -global_quality AS ( - SELECT - COUNT(*) AS total_inspection_count, - SUM(COALESCE(pass_qty, 0)) AS total_pass_qty, - SUM(COALESCE(fail_qty, 0)) AS total_fail_qty, - CASE WHEN SUM(COALESCE(pass_qty, 0) + COALESCE(fail_qty, 0)) > 0 - THEN SUM(COALESCE(pass_qty, 0))::FLOAT / SUM(COALESCE(pass_qty, 0) + COALESCE(fail_qty, 0)) - ELSE 1 END AS qc_pass_rate - FROM fact_quality_inspection -), - --- 3. 全局工序不良特征 -global_operation AS ( - SELECT - COUNT(*) AS total_task_count, - SUM(COALESCE(good_qty, 0)) AS total_good_qty, - SUM(COALESCE(bad_qty, 0)) AS total_bad_qty, - CASE WHEN SUM(COALESCE(good_qty, 0) + COALESCE(bad_qty, 0)) > 0 - THEN SUM(COALESCE(bad_qty, 0))::FLOAT / SUM(COALESCE(good_qty, 0) + COALESCE(bad_qty, 0)) - ELSE 0 END AS operation_defect_rate - FROM fact_operation_task -), - --- 4. 客户级别发货统计 -customer_shipment AS ( - SELECT - customer_id, - COUNT(*) AS shipment_count, - SUM(COALESCE(amount, 0)) AS total_shipment_amount - FROM fact_sales_shipment - GROUP BY customer_id -), - --- 5. 客户级别退货统计 -customer_return AS ( - SELECT - customer_id, - COUNT(*) AS return_count, - SUM(COALESCE(amount, 0)) AS total_return_amount - FROM fact_sales_return - GROUP BY customer_id -), - --- 6. 订单风险评估 -order_risk AS ( - SELECT - so.sales_order_id, - so.sales_order_number, - c.customer_name, - so.order_date_utc, - so.deal_amount, - so.payment_status, - - -- 全局生产指标 - gp.avg_completion_rate AS production_completion_rate, - gp.pending_wo_count, - gp.total_wo_count AS work_order_count, - - -- 全局质检指标 - gq.qc_pass_rate, - gq.total_fail_qty, - - -- 全局工序指标 - go.operation_defect_rate, - - -- 客户级别指标 - COALESCE(cs.shipment_count, 0) AS shipment_count, - COALESCE(cr.return_count, 0) AS return_count, - CASE WHEN COALESCE(cs.shipment_count, 0) > 0 - THEN COALESCE(cr.return_count, 0)::FLOAT / cs.shipment_count - ELSE 0 END AS return_rate - - FROM fact_sales_order so - LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = true - CROSS JOIN global_production gp - CROSS JOIN global_quality gq - CROSS JOIN global_operation go - LEFT JOIN customer_shipment cs ON so.customer_id = cs.customer_id - LEFT JOIN customer_return cr ON so.customer_id = cr.customer_id -) - --- 7. 最终输出 -SELECT - sales_order_id, - sales_order_number, - customer_name, - order_date_utc, - deal_amount, - payment_status, - - -- 风险特征 - work_order_count, - ROUND(production_completion_rate::NUMERIC, 2) AS production_completion_rate, - pending_wo_count, - ROUND(qc_pass_rate::NUMERIC, 2) AS qc_pass_rate, - ROUND(operation_defect_rate::NUMERIC, 4) AS operation_defect_rate, - return_count, - ROUND(return_rate::NUMERIC, 4) AS return_rate, - - -- 延迟概率 - ROUND(( - CASE WHEN production_completion_rate < 0.3 THEN 0.30 - WHEN production_completion_rate < 0.5 THEN 0.20 - WHEN production_completion_rate < 0.8 THEN 0.10 - ELSE 0 END - + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 - WHEN qc_pass_rate < 0.9 THEN 0.15 - WHEN qc_pass_rate < 0.95 THEN 0.08 - ELSE 0 END - + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 - WHEN operation_defect_rate > 0.05 THEN 0.12 - WHEN operation_defect_rate > 0.02 THEN 0.05 - ELSE 0 END - + CASE WHEN return_rate > 0.1 THEN 0.15 - WHEN return_rate > 0.05 THEN 0.08 - WHEN return_rate > 0.02 THEN 0.03 - ELSE 0 END - + CASE WHEN payment_status = 'UNPAID' THEN 0.10 - WHEN payment_status = 'PARTIAL' THEN 0.05 - ELSE 0 END - )::NUMERIC, 2) AS delay_probability, - - -- 红/黄/绿预警 - CASE - WHEN ( - CASE WHEN production_completion_rate < 0.3 THEN 0.30 WHEN production_completion_rate < 0.5 THEN 0.20 WHEN production_completion_rate < 0.8 THEN 0.10 ELSE 0 END - + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 WHEN qc_pass_rate < 0.9 THEN 0.15 WHEN qc_pass_rate < 0.95 THEN 0.08 ELSE 0 END - + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 WHEN operation_defect_rate > 0.05 THEN 0.12 WHEN operation_defect_rate > 0.02 THEN 0.05 ELSE 0 END - + CASE WHEN return_rate > 0.1 THEN 0.15 WHEN return_rate > 0.05 THEN 0.08 WHEN return_rate > 0.02 THEN 0.03 ELSE 0 END - + CASE WHEN payment_status = 'UNPAID' THEN 0.10 WHEN payment_status = 'PARTIAL' THEN 0.05 ELSE 0 END - ) >= 0.50 THEN 'RED' - WHEN ( - CASE WHEN production_completion_rate < 0.3 THEN 0.30 WHEN production_completion_rate < 0.5 THEN 0.20 WHEN production_completion_rate < 0.8 THEN 0.10 ELSE 0 END - + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 WHEN qc_pass_rate < 0.9 THEN 0.15 WHEN qc_pass_rate < 0.95 THEN 0.08 ELSE 0 END - + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 WHEN operation_defect_rate > 0.05 THEN 0.12 WHEN operation_defect_rate > 0.02 THEN 0.05 ELSE 0 END - + CASE WHEN return_rate > 0.1 THEN 0.15 WHEN return_rate > 0.05 THEN 0.08 WHEN return_rate > 0.02 THEN 0.03 ELSE 0 END - + CASE WHEN payment_status = 'UNPAID' THEN 0.10 WHEN payment_status = 'PARTIAL' THEN 0.05 ELSE 0 END - ) >= 0.25 THEN 'YELLOW' - ELSE 'GREEN' - END AS risk_level, - - -- 风险原因 - CONCAT_WS(' | ', - CASE WHEN production_completion_rate < 0.5 THEN '生产进度滞后' END, - CASE WHEN qc_pass_rate < 0.9 THEN '质检通过率低' END, - CASE WHEN operation_defect_rate > 0.05 THEN '工序不良率高' END, - CASE WHEN return_rate > 0.05 THEN '历史退货率高' END, - CASE WHEN payment_status = 'UNPAID' THEN '未付款' END - ) AS risk_reasons - -FROM order_risk -ORDER BY delay_probability DESC, deal_amount DESC; diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/__init__.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/__init__.py deleted file mode 100644 index 3df2b44..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Utils package for lzwcai_mcp_sqlexecutor""" - -from .json_helper import load_json -from .name_helper import generate_tool_name -from .schema_helper import generate_input_schema, validate_input_schema -from .api_client import DataSourceAPIClient, get_skill_by_id, process_skill_response, test_sql_with_schema -from .env_config import get_database_id, get_datasource_id, get_skill_id, get_backend_base_url, get_env_config, set_env_variable - -__all__ = [ - 'load_json', - 'generate_tool_name', - 'generate_input_schema', - 'validate_input_schema', - 'DataSourceAPIClient', - 'get_skill_by_id', - 'process_skill_response', - 'test_sql_with_schema', - 'get_database_id', - 'get_datasource_id', - 'get_skill_id', - 'get_backend_base_url', - 'get_env_config', - 'set_env_variable' -] - diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/api_client.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/api_client.py deleted file mode 100644 index cf49a0a..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/api_client.py +++ /dev/null @@ -1,318 +0,0 @@ -""" -第三方API调用客户端 -用于调用外部数据源接口 -""" - -import httpx -import logging -import json -from typing import Dict, Any, Optional, List - -# 支持直接运行和模块导入两种方式 -try: - from .env_config import get_backend_base_url -except ImportError: - from env_config import get_backend_base_url - -# 获取日志记录器 -logger = logging.getLogger(__name__) - - -class DataSourceAPIClient: - """数据源API客户端""" - - def __init__( - self, - base_url: Optional[str] = None, - token: Optional[str] = None - ): - """ - 初始化API客户端 - - Args: - base_url: API基础URL（默认从环境变量 BACKEND_BASE_URL 读取，如果未设置则使用 http://192.168.2.236:8088） - token: 认证令牌（Bearer Token） - """ - # 如果没有传入 base_url，则从环境变量读取 - if base_url is None: - base_url = get_backend_base_url() - - self.base_url = base_url.rstrip('/') - self.token = token or "eyJhbGciOiJIUzUxMiJ9.eyJsb2dpbl91c2VyX2tleSI6IjJiYTk4ODllLWM2ZGItNDQ5YS1iZmFjLTQ2YzMxODFlODg5NCJ9.dvi8zm0LsWvJ_h9zD5blnHFRxa4z4_WBm1R487ekE7HlHzrN6dnvqhK8askqT5b1EcE8myHwRzLVMoI8UOjOrw" - self.client = httpx.Client(timeout=30.0) - - def _get_headers(self) -> Dict[str, str]: - """ - 获取请求头 - - Returns: - 请求头字典 - """ - return { - 'Authorization': f'Bearer {self.token}', - } - - def get_skill_by_id(self, skill_id: str) -> Dict[str, Any]: - """ - 根据技能ID获取技能信息 - - Args: - skill_id: 技能ID - - Returns: - API响应数据 - - Raises: - Exception: 请求失败时抛出 - """ - try: - url = f"{self.base_url}/datasource/skill/getBySkillId/{skill_id}" - - logger.info(f"正在调用API: {url}") - logger.info(f"请求参数 - skill_id: {skill_id}") - - response = self.client.get( - url, - headers=self._get_headers() - ) - - # 检查HTTP状态码 - response.raise_for_status() - - # 解析JSON响应 - data = response.json() - - logger.info(f"API调用成功: {url}") - logger.debug(f"响应数据: {data}") - - return data - - except httpx.TimeoutException: - error_msg = f"API请求超时: {url}" - logger.error(error_msg) - raise Exception(error_msg) - - except httpx.HTTPStatusError as e: - error_msg = f"API请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(error_msg) - logger.error(f"错误响应: {e.response.text}") - raise Exception(error_msg) - - except httpx.RequestError as e: - error_msg = f"API请求异常: {url}, 错误: {str(e)}" - logger.error(error_msg) - raise Exception(error_msg) - - except Exception as e: - error_msg = f"处理API响应时出错: {str(e)}" - logger.error(error_msg, exc_info=True) - raise Exception(error_msg) - - def test_sql_with_schema(self, request_data: Dict[str, Any]) -> Dict[str, Any]: - """ - 测试SQL语句并返回执行结果 - - Args: - request_data: 请求数据，包含以下字段： - - datasourceId: 数据源ID - - businessName: 业务名称 - - businessDescription: 业务描述 - - sqlTemplate: SQL模板 - - parameters: 参数定义 - - testParams: 测试参数 - - Returns: - API响应数据 - - Raises: - Exception: 请求失败时抛出 - """ - try: - url = f"{self.base_url}/datasource/sqlExecutionLog/testSqlWithSchema" - - # 构建请求头（包含Content-Type） - headers = self._get_headers() - headers['Content-Type'] = 'application/json' - headers['Accept'] = '*/*' - - logger.info(f"正在调用测试SQL API: {url}") - logger.info(f"请求参数: {json.dumps(request_data, ensure_ascii=False, indent=2)}") - - # 发送POST请求 - response = self.client.post( - url, - headers=headers, - json=request_data - ) - - # 检查HTTP状态码 - response.raise_for_status() - - # 解析JSON响应 - result = response.json() - - logger.info(f"测试SQL API调用成功") - logger.debug(f"响应数据: {json.dumps(result, ensure_ascii=False, indent=2)}") - - # 处理返回数据结构: {code, data: {errorMessage, data}, msg} - # 检查外层 code - if result.get("code") != 200: - error_msg = result.get("msg", "接口返回错误") - logger.error(f"接口返回错误: code={result.get('code')}, msg={error_msg}") - raise Exception(error_msg) - - # 检查内层 errorMessage - inner_data = result.get("data", {}) - if inner_data.get("errorMessage"): - error_msg = inner_data.get("errorMessage") - logger.error(f"接口业务错误: {error_msg}") - raise Exception(error_msg) - - # 返回 data.data - return inner_data.get("data") - - except httpx.TimeoutException: - error_msg = f"测试SQL API请求超时: {url}" - logger.error(error_msg) - raise Exception(error_msg) - - except httpx.HTTPStatusError as e: - error_msg = f"测试SQL API请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(error_msg) - logger.error(f"错误响应: {e.response.text}") - raise Exception(error_msg) - - except httpx.RequestError as e: - error_msg = f"测试SQL API请求异常: {url}, 错误: {str(e)}" - logger.error(error_msg) - raise Exception(error_msg) - - except Exception as e: - error_msg = f"处理测试SQL API响应时出错: {str(e)}" - logger.error(error_msg, exc_info=True) - raise Exception(error_msg) - - def close(self): - """关闭HTTP客户端""" - self.client.close() - - -# 创建默认客户端实例 -default_client = DataSourceAPIClient() - - -def get_skill_by_id(skill_id: str, base_url: Optional[str] = None, token: Optional[str] = None) -> Dict[str, Any]: - """ - 便捷函数：根据技能ID获取技能信息 - - Args: - skill_id: 技能ID - base_url: API基础URL（可选，默认从环境变量 BACKEND_BASE_URL 读取） - token: 认证令牌（可选，使用默认值） - - Returns: - API响应数据 - """ - if base_url or token: - client = DataSourceAPIClient( - base_url=base_url, - token=token - ) - return client.get_skill_by_id(skill_id) - else: - return default_client.get_skill_by_id(skill_id) - - -def test_sql_with_schema(request_data: Dict[str, Any], base_url: Optional[str] = None, token: Optional[str] = None) -> Dict[str, Any]: - """ - 便捷函数：测试SQL语句并返回执行结果 - - Args: - request_data: 请求数据，包含以下字段： - - datasourceId: 数据源ID - - businessName: 业务名称 - - businessDescription: 业务描述 - - sqlTemplate: SQL模板 - - parameters: 参数定义 - - testParams: 测试参数 - base_url: API基础URL（可选，默认从环境变量 BACKEND_BASE_URL 读取） - token: 认证令牌（可选，使用默认值） - - Returns: - API响应数据 - """ - if base_url or token: - client = DataSourceAPIClient( - base_url=base_url, - token=token - ) - return client.test_sql_with_schema(request_data) - else: - return default_client.test_sql_with_schema(request_data) - - -def process_skill_response(response: Dict[str, Any]) -> List[Dict[str, Any]]: - """ - 处理API响应数据，映射为businessQueries格式 - - Args: - response: API原始响应数据 - - Returns: - 处理后的查询配置列表 - """ - try: - # 提取data数组 - data_list = response.get("data", []) - - # 默认的员工ID参数schema - default_employee_schema = { - "type": "object", - "required": ["employeeId"], - "properties": { - "employeeId": { - "type": "number", - "description": "员工ID，用于标识员工的唯一数字标识符", - "examples": [1001, 2002] - } - } - } - - # 映射每个skill为businessQuery格式 - queries = [] - for skill in data_list: - # 解析sqlParams字符串为JSON对象 - sql_params = json.loads(skill.get("sqlParams", "{}")) - - # 判断sqlParams是否为空对象 - is_empty_params = ( - not sql_params.get("properties") or - len(sql_params.get("properties", {})) == 0 - ) and ( - not sql_params.get("required") or - len(sql_params.get("required", [])) == 0 - ) - - # 如果是空对象，使用默认的员工ID参数 - if is_empty_params: - logger.info(f"技能 {skill.get('name')} (ID: {skill.get('id')}) 的sqlParams为空，使用默认员工ID参数") - sql_params = default_employee_schema - - # 映射字段 - query = { - "id": skill.get("id"), - "businessName": skill.get("name"), - "businessDescription": skill.get("description"), - "sqlTemplate": skill.get("sqlTemplate"), - "parameters": sql_params, - "datasourceId": skill.get("datasourceId") - } - queries.append(query) - - logger.info(f"成功处理 {len(queries)} 条技能数据") - return queries - - except Exception as e: - logger.error(f"处理API响应数据失败: {e}", exc_info=True) - raise - diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/env_config.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/env_config.py deleted file mode 100644 index 585b0a6..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/env_config.py +++ /dev/null @@ -1,106 +0,0 @@ -"""环境变量配置模块""" - -import os -from typing import Optional - - -def get_database_id(default: str = "29") -> str: - """ - 获取数据库ID环境变量 - - Args: - default: 默认值（默认为 "29"） - - Returns: - str: 数据库ID - - Environment Variables: - databaseId: 数据库ID - """ - return os.environ.get("databaseId", default) - - -def get_datasource_id(default: str = "") -> str: - """ - 获取数据源ID环境变量 - - Args: - default: 默认值（默认为 ""） - - Returns: - str: 数据源ID - - Environment Variables: - datasourceId: 数据源ID - """ - return os.environ.get("datasourceId", default) - - -def get_skill_id(default: str = "") -> str: - """ - 获取技能ID环境变量 - - Args: - default: 默认值（默认为 ""） - - Returns: - str: 技能ID - - Environment Variables: - skillId: 技能ID - """ - return os.environ.get("skillId", default) - - -def get_backend_base_url(default: str = "http://lzwcai-demp-corp-manager:8086") -> str: - """ - 获取后端API基础URL环境变量 - - Args: - default: 默认值（默认为 "http://lzwcai-demp-corp-manager:8086"） - - Returns: - str: 后端API基础URL - - Environment Variables: - backendBaseUrl: 后端API基础URL - """ - return os.environ.get("backendBaseUrl", default) - - -def get_env_config() -> dict: - """ - 获取所有环境配置 - - Returns: - dict: 包含所有配置的字典 - - Example: - config = get_env_config() - print(config['database_id']) # 输出: "29" - print(config['datasource_id']) # 输出: "" - print(config['skill_id']) # 输出: "" - print(config['backend_base_url']) # 输出: "http://lzwcai-demp-corp-manager:8086" - """ - return { - "database_id": get_database_id(), - "datasource_id": get_datasource_id(), - "skill_id": get_skill_id(), - "backend_base_url": get_backend_base_url() - } - - -def set_env_variable(key: str, value: str) -> None: - """ - 设置环境变量（仅在当前进程中有效） - - Args: - key: 环境变量名 - value: 环境变量值 - - Example: - set_env_variable("databaseId", "30") - set_env_variable("skillId", "1234567890") - """ - os.environ[key] = value - diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/json_helper.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/json_helper.py deleted file mode 100644 index 1a6fc95..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/json_helper.py +++ /dev/null @@ -1,60 +0,0 @@ -"""JSON 文件读取工具""" - -import json -from pathlib import Path -from typing import Any, Union - - -def load_json(json_path: Union[str, Path]) -> Any: - """ - 读取 JSON 文件并返回其内容 - - Args: - json_path: JSON 文件的路径（支持字符串或 Path 对象） - - Returns: - JSON 文件中解析后的数据（可以是字典、列表或其他 JSON 类型） - - Raises: - FileNotFoundError: 当文件不存在时 - json.JSONDecodeError: 当 JSON 格式无效时 - Exception: 其他读取错误 - - Example: - >>> data = load_json('config.json') - >>> print(data) - {'key': 'value'} - - >>> data = load_json(Path('data/users.json')) - >>> print(data) - [{'id': 1, 'name': 'Alice'}] - """ - try: - # 转换为 Path 对象 - path = Path(json_path) - - # 检查文件是否存在 - if not path.exists(): - raise FileNotFoundError(f"JSON 文件不存在: {json_path}") - - # 检查是否为文件 - if not path.is_file(): - raise ValueError(f"路径不是一个文件: {json_path}") - - # 读取并解析 JSON 文件 - with open(path, 'r', encoding='utf-8') as f: - data = json.load(f) - - return data - - except json.JSONDecodeError as e: - raise json.JSONDecodeError( - f"JSON 格式错误: {e.msg}", - e.doc, - e.pos - ) - except FileNotFoundError: - raise - except Exception as e: - raise Exception(f"读取 JSON 文件时发生错误: {str(e)}") - diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/logger_config.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/logger_config.py deleted file mode 100644 index 290964f..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/logger_config.py +++ /dev/null @@ -1,489 +0,0 @@ -# -*- coding: utf-8 -*- -""" -统一日志配置模块 -提供系统级别的日志配置和管理 -""" - -import os -import sys -import logging -from logging.handlers import RotatingFileHandler, TimedRotatingFileHandler -from datetime import datetime -from pathlib import Path - - -class LoggerConfig: - """日志配置管理类""" - - def __init__(self, logs_dir: str = None): - """初始化日志配置 - - Args: - logs_dir: 日志目录路径，默认为项目根目录下的logs文件夹 - """ - # 确定日志目录 - if logs_dir: - self.logs_dir = Path(logs_dir) - else: - # 获取项目根目录（logger_config.py 在 utils 目录下，需要上升两层到达项目根目录） - project_root = Path(__file__).parent.parent - self.logs_dir = project_root / "logs" - - # 创建日志目录 - self.logs_dir.mkdir(exist_ok=True) - - # 日志格式 - self.log_format = '%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s' - self.date_format = '%Y-%m-%d %H:%M:%S' - - # 从环境变量获取日志级别，默认为INFO - self.log_level = self._get_log_level_from_env() - - # 是否已初始化 - self._initialized = False - - def _get_log_level_from_env(self) -> int: - """从环境变量获取日志级别 - - Returns: - int: 日志级别 - """ - log_level_str = os.getenv('LOG_LEVEL', 'INFO').upper() - - # 日志级别映射 - level_mapping = { - 'DEBUG': logging.DEBUG, - 'INFO': logging.INFO, - 'WARNING': logging.WARNING, - 'WARN': logging.WARNING, # 兼容性别名 - 'ERROR': logging.ERROR, - 'CRITICAL': logging.CRITICAL, - 'FATAL': logging.CRITICAL # 兼容性别名 - } - - return level_mapping.get(log_level_str, logging.INFO) - - def setup_logging(self, - app_name: str = "lzwcai_mcp_sqlexecutor", - log_level: int = logging.INFO, - max_file_size: int = 10 * 1024 * 1024, # 10MB - backup_count: int = 5, - console_output: bool = True) -> logging.Logger: - """设置系统日志配置 - - Args: - app_name: 应用名称，用于日志文件命名 - log_level: 日志级别 - max_file_size: 单个日志文件最大大小（字节） - backup_count: 保留的备份文件数量 - console_output: 是否输出到控制台 - - Returns: - logging.Logger: 配置好的根日志器 - """ - if self._initialized: - return logging.getLogger() - - # 设置根日志器 - root_logger = logging.getLogger() - root_logger.setLevel(log_level) - - # 清除现有的处理器 - for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) - - # 创建格式化器 - formatter = logging.Formatter(self.log_format, self.date_format) - - # 1. 主日志文件 - 按大小滚动 - main_log_file = self.logs_dir / f"{app_name}.log" - file_handler = RotatingFileHandler( - main_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - file_handler.setLevel(log_level) - file_handler.setFormatter(formatter) - root_logger.addHandler(file_handler) - - # 2. 错误日志文件 - 只记录ERROR及以上级别 - error_log_file = self.logs_dir / f"{app_name}_error.log" - error_handler = RotatingFileHandler( - error_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - error_handler.setLevel(logging.ERROR) - error_handler.setFormatter(formatter) - root_logger.addHandler(error_handler) - - # 3. 按日期滚动的日志文件 - daily_log_file = self.logs_dir / f"{app_name}_daily.log" - daily_handler = TimedRotatingFileHandler( - daily_log_file, - when='midnight', - interval=1, - backupCount=30, # 保留30天 - encoding='utf-8' - ) - daily_handler.setLevel(log_level) - daily_handler.setFormatter(formatter) - daily_handler.suffix = "%Y-%m-%d" - root_logger.addHandler(daily_handler) - - # 4. 控制台输出 - # 重要：MCP协议使用stdio时，必须将日志输出到stderr，stdout仅用于JSON-RPC通信 - if console_output: - console_handler = logging.StreamHandler(sys.stderr) - console_handler.setLevel(log_level) - console_formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s', - self.date_format - ) - console_handler.setFormatter(console_formatter) - root_logger.addHandler(console_handler) - - self._initialized = True - - # 记录初始化信息 - root_logger.info(f"日志系统初始化完成 - 日志目录: {self.logs_dir}") - root_logger.info(f"日志配置 - 级别: {logging.getLevelName(log_level)}, 文件大小限制: {max_file_size//1024//1024}MB, 备份数量: {backup_count}") - - return root_logger - - def get_module_logger(self, module_name: str) -> logging.Logger: - """获取模块专用日志器 - - Args: - module_name: 模块名称 - - Returns: - logging.Logger: 模块日志器 - """ - return logging.getLogger(module_name) - - def create_component_logger(self, - component_name: str, - log_file: str = None, - log_level: int = None) -> logging.Logger: - """为特定组件创建独立的日志器 - - Args: - component_name: 组件名称 - log_file: 独立日志文件名（可选） - log_level: 日志级别（可选） - - Returns: - logging.Logger: 组件日志器 - """ - logger = logging.getLogger(component_name) - - if log_file: - # 为组件创建独立的日志文件 - component_log_file = self.logs_dir / log_file - handler = RotatingFileHandler( - component_log_file, - maxBytes=5 * 1024 * 1024, # 5MB - backupCount=3, - encoding='utf-8' - ) - - formatter = logging.Formatter(self.log_format, self.date_format) - handler.setFormatter(formatter) - - if log_level: - handler.setLevel(log_level) - - logger.addHandler(handler) - logger.info(f"组件日志器创建完成: {component_name} -> {component_log_file}") - - return logger - - def setup_mqtt_logging(self) -> logging.Logger: - """设置MQTT专用日志 - - Returns: - logging.Logger: MQTT日志器 - """ - return self.create_component_logger( - "mqtt_communication", - "mqtt_communication.log", - logging.DEBUG - ) - - def setup_mcp_logging(self) -> logging.Logger: - """设置MCP专用日志 - - Returns: - logging.Logger: MCP日志器 - """ - return self.create_component_logger( - "mcp_services", - "mcp_services.log", - logging.DEBUG - ) - - def setup_api_logging(self) -> logging.Logger: - """设置API专用日志 - - Returns: - logging.Logger: API日志器 - """ - return self.create_component_logger( - "api_requests", - "api_requests.log", - logging.INFO - ) - - def get_logs_info(self) -> dict: - """获取日志系统信息 - - Returns: - dict: 日志系统信息 - """ - log_files = [] - if self.logs_dir.exists(): - for log_file in self.logs_dir.glob("*.log*"): - stat = log_file.stat() - log_files.append({ - "name": log_file.name, - "size": stat.st_size, - "modified": datetime.fromtimestamp(stat.st_mtime).isoformat() - }) - - return { - "logs_directory": str(self.logs_dir), - "initialized": self._initialized, - "log_files": log_files, - "total_files": len(log_files) - } - - def cleanup_old_logs(self, days: int = 30): - """清理旧日志文件 - - Args: - days: 保留天数 - """ - if not self.logs_dir.exists(): - return - - from datetime import timedelta - cutoff_time = datetime.now() - timedelta(days=days) - - cleaned_files = [] - for log_file in self.logs_dir.glob("*.log*"): - if log_file.stat().st_mtime < cutoff_time.timestamp(): - try: - log_file.unlink() - cleaned_files.append(log_file.name) - except Exception as e: - logging.error(f"清理日志文件失败: {log_file.name}, 错误: {e}") - - if cleaned_files: - logging.info(f"清理了 {len(cleaned_files)} 个旧日志文件: {cleaned_files}") - - def set_log_level(self, level: int, logger_name: str = None): - """动态调整日志级别 - - Args: - level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL) - logger_name: 指定日志器名称，None表示调整根日志器 - """ - if logger_name: - logger = logging.getLogger(logger_name) - else: - logger = logging.getLogger() - - old_level = logger.level - logger.setLevel(level) - - # 同时调整所有处理器的级别 - for handler in logger.handlers: - if not isinstance(handler, logging.StreamHandler) or handler.stream not in (sys.stdout, sys.stderr): - # 不调整控制台处理器的级别，保持原有设置 - handler.setLevel(level) - - level_name = logging.getLevelName(level) - old_level_name = logging.getLevelName(old_level) - target = logger_name or "根日志器" - - logger.info(f"日志级别已调整: {target} {old_level_name} -> {level_name}") - - def set_temporary_log_level(self, level: int, logger_name: str = None): - """临时调整日志级别（会保存原始级别用于恢复） - - Args: - level: 临时日志级别 - logger_name: 指定日志器名称，None表示调整根日志器 - """ - if not hasattr(self, '_original_levels'): - self._original_levels = {} - - target_name = logger_name or 'root' - - if logger_name: - logger = logging.getLogger(logger_name) - else: - logger = logging.getLogger() - - # 保存原始级别 - if target_name not in self._original_levels: - self._original_levels[target_name] = logger.level - - # 设置新级别 - self.set_log_level(level, logger_name) - - level_name = logging.getLevelName(level) - target = logger_name or "根日志器" - logger.info(f"临时调整日志级别: {target} -> {level_name} (可通过restore_log_level恢复)") - - def restore_log_level(self, logger_name: str = None): - """恢复日志级别到调整前的状态 - - Args: - logger_name: 指定日志器名称，None表示恢复根日志器 - """ - if not hasattr(self, '_original_levels'): - logging.warning("没有找到保存的原始日志级别") - return - - target_name = logger_name or 'root' - - if target_name not in self._original_levels: - logging.warning(f"没有找到 {target_name} 的原始日志级别") - return - - original_level = self._original_levels[target_name] - self.set_log_level(original_level, logger_name) - - # 清除保存的级别 - del self._original_levels[target_name] - - target = logger_name or "根日志器" - level_name = logging.getLevelName(original_level) - logging.info(f"已恢复日志级别: {target} -> {level_name}") - - def get_current_log_levels(self) -> dict: - """获取当前所有日志器的级别信息 - - Returns: - dict: 日志器级别信息 - """ - levels_info = {} - - # 根日志器 - root_logger = logging.getLogger() - levels_info['root'] = { - 'level': root_logger.level, - 'level_name': logging.getLevelName(root_logger.level), - 'handlers_count': len(root_logger.handlers) - } - - # 其他已创建的日志器 - for name, logger in logging.Logger.manager.loggerDict.items(): - if isinstance(logger, logging.Logger): - levels_info[name] = { - 'level': logger.level, - 'level_name': logging.getLevelName(logger.level), - 'handlers_count': len(logger.handlers) - } - - return levels_info - - -# 全局日志配置实例 -logger_config = LoggerConfig() - - -def setup_system_logging(app_name: str = "lzwcai_mcp_sqlexecutor", - log_level: int = logging.INFO) -> logging.Logger: - """系统日志初始化快捷函数 - - Args: - app_name: 应用名称 - log_level: 日志级别 - - Returns: - logging.Logger: 根日志器 - """ - return logger_config.setup_logging(app_name, log_level) - - -def get_logger(name: str) -> logging.Logger: - """获取日志器的快捷函数 - - Args: - name: 日志器名称 - - Returns: - logging.Logger: 日志器实例 - """ - return logger_config.get_module_logger(name) - - -def set_log_level(level: int, logger_name: str = None): - """动态调整日志级别的快捷函数 - - Args: - level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL) - logger_name: 指定日志器名称，None表示调整根日志器 - - Examples: - # 调整根日志器为DEBUG级别 - set_log_level(logging.DEBUG) - - # 调整特定模块日志器为WARNING级别 - set_log_level(logging.WARNING, "agent_ontology.core") - """ - logger_config.set_log_level(level, logger_name) - - -def set_temporary_log_level(level: int, logger_name: str = None): - """临时调整日志级别的快捷函数 - - Args: - level: 临时日志级别 - logger_name: 指定日志器名称，None表示调整根日志器 - - Examples: - # 临时调整为DEBUG级别进行调试 - set_temporary_log_level(logging.DEBUG) - # ... 进行调试 ... - # 恢复原始级别 - restore_log_level() - """ - logger_config.set_temporary_log_level(level, logger_name) - - -def restore_log_level(logger_name: str = None): - """恢复日志级别的快捷函数 - - Args: - logger_name: 指定日志器名称，None表示恢复根日志器 - """ - logger_config.restore_log_level(logger_name) - - -def get_current_log_levels() -> dict: - """获取当前日志级别信息的快捷函数 - - Returns: - dict: 日志器级别信息 - - Examples: - levels = get_current_log_levels() - print(f"根日志器级别: {levels['root']['level_name']}") - """ - return logger_config.get_current_log_levels() - - -# 便捷的日志级别常量 -class LogLevel: - """日志级别常量类""" - DEBUG = logging.DEBUG - INFO = logging.INFO - WARNING = logging.WARNING - ERROR = logging.ERROR - CRITICAL = logging.CRITICAL \ No newline at end of file diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/name_helper.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/name_helper.py deleted file mode 100644 index d66cf32..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/name_helper.py +++ /dev/null @@ -1,41 +0,0 @@ -# -*- coding: utf-8 -*- -""" -名称生成工具模块 -""" - -from pypinyin import lazy_pinyin, Style -import logging - -logger = logging.getLogger(__name__) - - -def generate_tool_name(business_name: str, tool_id: str) -> str: - """ - 根据业务名称和ID生成工具名称 - 格式: tool_拼音_id - - Args: - business_name: 业务名称（中文） - tool_id: 工具ID - - Returns: - str: 格式化的工具名称 - """ - try: - # 将中文转换为拼音（无音调，小写） - pinyin_list = lazy_pinyin(business_name, style=Style.NORMAL) - # 拼接拼音 - pinyin_str = ''.join(pinyin_list) - - # 将 ID 中的 '-' 替换为 '_' - formatted_id = tool_id.replace('-', '_') - - # 组合成最终的工具名称 - tool_name = f"tool_{pinyin_str}_{formatted_id}" - - return tool_name - except Exception as e: - logger.error(f"生成工具名称失败: {business_name}, {tool_id}, 错误: {e}", exc_info=True) - # 降级处理：如果拼音转换失败，使用 ID - return f"tool_{tool_id.replace('-', '_')}" - diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/schema_helper.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/schema_helper.py deleted file mode 100644 index 626559f..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/utils/schema_helper.py +++ /dev/null @@ -1,166 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Schema 生成工具模块 -""" - -from typing import Any, Dict, List - - -def generate_input_schema(parameters: Dict[str, Any]) -> Dict[str, Any]: - """ - 从查询配置的参数定义生成 MCP 工具的 inputSchema - - 此函数会保留完整的 JSON Schema 信息，包括： - - type: Schema 类型（通常是 "object"） - - required: 必填字段列表 - - properties: 属性定义（包括每个属性的 type, description, format, examples 等） - - description: Schema 的整体描述（如果有） - - 以及其他任何 JSON Schema 标准字段 - - 此函数还会自动添加以下字段（如果原始 parameters 中未定义）： - - targetDatabaseName: 目标数据库名称（非必填，默认为空字符串） - - Args: - parameters: 查询配置中的参数定义字典，应该是一个完整的 JSON Schema 对象 - - Returns: - Dict[str, Any]: 符合 JSON Schema 规范的 inputSchema 对象 - - Example: - >>> params = { - ... "type": "object", - ... "required": ["userId", "startTime"], - ... "properties": { - ... "userId": { - ... "type": "integer", - ... "description": "用户的唯一标识符", - ... "examples": [10086] - ... }, - ... "startTime": { - ... "type": "string", - ... "format": "date-time", - ... "description": "查询的起始时间", - ... "examples": ["2023-01-01 00:00:00"] - ... } - ... } - ... } - >>> schema = generate_input_schema(params) - >>> # schema 将包含所有原始信息，包括 format 和 examples - >>> # 同时会自动添加 targetDatabaseName 字段 - """ - # 如果 parameters 本身就是一个完整的 JSON Schema 对象，直接使用 - # 但确保至少包含 type 和 properties - if not parameters: - # 如果 parameters 为空，返回一个空的 object schema - return { - "type": "object", - "properties": {}, - "required": [] - } - - # 检查 parameters 是否已经是完整的 JSON Schema 格式 - # 完整格式应该有 "type": "object" 和 "properties" 字段 - if "type" in parameters and parameters.get("type") == "object" and "properties" in parameters: - # 已经是完整的 JSON Schema 格式，直接使用 - input_schema = dict(parameters) - else: - # parameters 是简化格式（直接是参数定义），需要转换为 JSON Schema 格式 - # 例如: {"workOrderNumber": {"type": "string", "description": "...", "required": true}} - properties = {} - required = [] - - for param_name, param_def in parameters.items(): - if isinstance(param_def, dict): - # 提取 required 标记 - if param_def.get("required", False): - required.append(param_name) - - # 复制参数定义，但移除 required 字段（它应该在顶层的 required 数组中） - prop_def = {k: v for k, v in param_def.items() if k != "required"} - properties[param_name] = prop_def - - input_schema = { - "type": "object", - "properties": properties, - "required": required - } - - # 确保必需的字段存在 - if "type" not in input_schema: - input_schema["type"] = "object" - - if "properties" not in input_schema: - input_schema["properties"] = {} - - if "required" not in input_schema: - input_schema["required"] = [] - - # 添加 targetDatabaseName 字段（如果不存在） - if "targetDatabaseName" not in input_schema["properties"]: - input_schema["properties"]["targetDatabaseName"] = { - "type": "string", - "description": "目标数据库名称", - "default": "" - } - - # 保留所有其他字段，如 description, examples, format 等 - # JSON Schema 标准支持的字段都会被保留： - # - additionalProperties - # - patternProperties - # - minProperties / maxProperties - # - dependencies - # - 等等 - - return input_schema - - -def validate_input_schema(schema: Dict[str, Any]) -> tuple[bool, str]: - """ - 验证 inputSchema 是否符合基本的 JSON Schema 规范 - - Args: - schema: 要验证的 schema 对象 - - Returns: - tuple[bool, str]: (是否有效, 错误消息或成功消息) - - Example: - >>> schema = {"type": "object", "properties": {"id": {"type": "string"}}} - >>> is_valid, msg = validate_input_schema(schema) - >>> print(is_valid, msg) - True, "Schema 验证通过" - """ - if not isinstance(schema, dict): - return False, "Schema 必须是一个字典对象" - - if schema.get("type") != "object": - return False, "Schema 的 type 字段必须是 'object'" - - if "properties" not in schema: - return False, "Schema 必须包含 properties 字段" - - if not isinstance(schema.get("properties"), dict): - return False, "Schema 的 properties 字段必须是一个字典对象" - - # 验证 required 字段（如果存在） - if "required" in schema: - required = schema["required"] - if not isinstance(required, list): - return False, "Schema 的 required 字段必须是一个列表" - - # 验证所有 required 的字段都在 properties 中定义 - properties = schema["properties"] - for field in required: - if field not in properties: - return False, f"必填字段 '{field}' 未在 properties 中定义" - 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{ url = "http://devpi.iepai.fun/root/pypi/+f/913/b2b8867234373/uvicorn-0.37.0-py3-none-any.whl", hash = "sha256:913b2b88672343739927ce381ff9e2ad62541f9f8289664fa1d1d3803fa2ce6c" }, -] diff --git a/lzwcai_mcpskills_analyzeWorkOrder/main.py b/lzwcai_mcpskills_analyzeWorkOrder/main.py deleted file mode 100644 index 015f7d0..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/main.py +++ /dev/null @@ -1,13 +0,0 @@ -""" -Entry point for lzwcai-mcpskills-analyzeOrder -Runs the MCP server for SQL query execution -""" -import os - -os.environ["databaseId"] = "19" -os.environ["datasourceId"] = "19" -os.environ["backendBaseUrl"] = "http://192.168.11.24:8088" -if __name__ == "__main__": - # Import and run the actual MCP server - from lzwcai_mcpskills_analyzeOrder.main import main - main() diff --git a/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml b/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml deleted file mode 100644 index bf24164..0000000 --- a/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml +++ /dev/null @@ -1,38 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai-mcpskills-analyzeWorkOrder" -version = "0.1.12" -description = "MCP server for executing business SQL queries with dynamic tool generation" -readme = "README.md" -requires-python = ">=3.10" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "sql", "executor", "server"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", - "pypinyin>=0.53.0", -] - -[project.scripts] -lzwcai-mcpskills-analyzeWorkOrder = "lzwcai_mcpskills_analyzeWorkOrder.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcpskills_analyzeWorkOrder"] - -[tool.hatch.build.targets.wheel.force-include] -"lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json" = "lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json" diff --git a/lzwcai_mcpskills_mfg_data_agent/README.md b/lzwcai_mcpskills_mfg_data_agent/README.md deleted file mode 100644 index 46ac7be..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/README.md +++ /dev/null @@ -1,138 +0,0 @@ -# lzwcai-mcpskills-mfg-data-agent (制造业数据智能体) - -一个基于 MCP (Model Context Protocol) 的制造业数据智能体服务器，支持从 JSON 配置文件动态生成查询工具。 - -## 功能特性 - -- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具 -- 🔧 灵活配置：支持自定义业务查询和参数验证 -- 📝 完整日志：详细的操作日志记录 -- 🌐 中文支持：工具名称自动转换为拼音 -- 🏭 制造业场景：专注于制造业数据分析与智能决策 - -## 安装 - -### 使用 pip 安装 - -```bash -pip install lzwcai-mcpskills-mfg-data-agent -``` - -### 从源码安装 - -```bash -git clone -cd lzwcai_mcpskills_mfg_data_agent -pip install -e . -``` - -### 使用 uv 安装（推荐） - -```bash -uv pip install lzwcai-mcpskills-mfg-data-agent -``` - -## 使用方法 - -### 命令行启动 - -安装后，可以直接通过命令启动： - -```bash -lzwcai-mcpskills-mfg-data-agent -``` - -### 作为 Python 模块运行 - -```bash -python -m lzwcai_mcpskills_mfg_data_agent.main -``` - -### 配置到 MCP 客户端 - -在你的 MCP 客户端配置文件中添加： - -```json -{ - "mcpServers": { - "mfg-data-agent": { - "command": "lzwcai-mcpskills-mfg-data-agent" - } - } -} -``` - -## 配置说明 - -### businessQueries.json - -在 `businessQueries.json` 中定义你的业务查询： - -```json -[ - { - "id": "query-001", - "businessName": "生产订单查询", - "businessDescription": "根据订单ID查询生产订单信息", - "sqlTemplate": "SELECT * FROM production_orders WHERE order_id = {{orderId}}", - "parameters": { - "type": "object", - "required": ["orderId"], - "properties": { - "orderId": { - "type": "string", - "description": "生产订单的唯一标识符", - "examples": ["PO-2024-001"] - } - } - } - } -] -``` - -## 开发 - -### 依赖项 - -- Python >= 3.13 -- httpx >= 0.28.1 -- mcp[cli] >= 1.10.1 -- pypinyin >= 0.53.0 - -### 本地开发 - -```bash -# 克隆仓库 -git clone -cd lzwcai_mcpskills_mfg_data_agent - -# 安装开发依赖 -pip install -e . - -# 运行服务器 -python -m lzwcai_mcpskills_mfg_data_agent.main -``` - -## 构建与发布 - -### 使用 build 构建 - -```bash -pip install build -python -m build -``` - -### 发布到 PyPI - -```bash -pip install twine -twine upload dist/* -``` - -## 许可证 - -MIT License - -## 作者 - -lzwcai diff --git a/lzwcai_mcpskills_mfg_data_agent/html/EfficiencyOutputLossDashboard.html b/lzwcai_mcpskills_mfg_data_agent/html/EfficiencyOutputLossDashboard.html deleted file mode 100644 index 49013b8..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/html/EfficiencyOutputLossDashboard.html +++ /dev/null @@ -1,543 +0,0 @@ - - - - - - 人效-产值-损耗三维模型仪表盘 - - - - -
- - - diff --git a/lzwcai_mcpskills_mfg_data_agent/html/MetricTrendAndTurningPointWarning.html b/lzwcai_mcpskills_mfg_data_agent/html/MetricTrendAndTurningPointWarning.html deleted file mode 100644 index 4667d07..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/html/MetricTrendAndTurningPointWarning.html +++ /dev/null @@ -1,791 +0,0 @@ - - - - - - 指标趋势分析与拐点预警 - - - - - - - - -
- - diff --git a/lzwcai_mcpskills_mfg_data_agent/html/OnePageDecisionBrief.html b/lzwcai_mcpskills_mfg_data_agent/html/OnePageDecisionBrief.html deleted file mode 100644 index 6ea2528..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/html/OnePageDecisionBrief.html +++ /dev/null @@ -1,567 +0,0 @@ - - - - - - 一页式决策简报 - - - - -
- - - \ No newline at end of file diff --git a/lzwcai_mcpskills_mfg_data_agent/html/OrderDelayWarningAnalysis.html b/lzwcai_mcpskills_mfg_data_agent/html/OrderDelayWarningAnalysis.html deleted file mode 100644 index 0fa2b22..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/html/OrderDelayWarningAnalysis.html +++ /dev/null @@ -1,626 +0,0 @@ - - - - - - 订单延迟预警分析 - - - - - - - - -
- - diff --git a/lzwcai_mcpskills_mfg_data_agent/html/SupplyChainRiskWarning.html b/lzwcai_mcpskills_mfg_data_agent/html/SupplyChainRiskWarning.html deleted file mode 100644 index 3aa9caa..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/html/SupplyChainRiskWarning.html +++ /dev/null @@ -1,680 +0,0 @@ - - - - - - 供应链风险预警 - - - - - - - - - - - - -
- - diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/.gitignore b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/.gitignore deleted file mode 100644 index 505a3b1..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/.gitignore +++ /dev/null @@ -1,10 +0,0 @@ -# Python-generated files -__pycache__/ -*.py[oc] -build/ -dist/ -wheels/ -*.egg-info - -# Virtual environments -.venv diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/.python-version b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/.python-version deleted file mode 100644 index 24ee5b1..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.13 diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/README.md b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/README.md deleted file mode 100644 index 0ec9671..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/README.md +++ /dev/null @@ -1,154 +0,0 @@ -# lzwcai-mcpskills-analyzeWorkOrder - -一个基于 MCP (Model Context Protocol) 的 SQL 查询执行服务器，支持从 JSON 配置文件动态生成查询工具。 - -## 功能特性 - -- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具 -- 🔧 灵活配置：支持自定义业务查询和参数验证 -- 📝 完整日志：详细的操作日志记录（仅输出到文件，不干扰MCP通信） -- 🌐 中文支持：工具名称自动转换为拼音 - -## 安装 - -### 使用 pip 安装 - -```bash -pip install lzwcai-mcpskills-analyzeWorkOrder -``` - -### 从源码安装 - -```bash -git clone -cd lzwcai_mcp_sqlexecutor -pip install -e . -``` - -### 使用 uv 安装（推荐） - -```bash -uv pip install lzwcai-mcpskills-analyzeWorkOrder -``` - -## 使用方法 - -### 命令行启动 - -安装后，可以直接通过命令启动： - -```bash -lzwcai-mcpskills-analyzeWorkOrder -``` - -### 作为 Python 模块运行 - -```bash -python -m lzwcai_mcp_sqlexecutor.main -``` - -### 配置到 MCP 客户端 - -在你的 MCP 客户端配置文件中添加： - -```json -{ - "mcpServers": { - "lzwcai-sqlexecutor": { - "command": "lzwcai-mcpskills-analyzeWorkOrder" - } - } -} -``` - -## 配置说明 - -### businessQueries.json - -在 `businessQueries.json` 中定义你的业务查询： - -```json -[ - { - "id": "query-001", - "businessName": "用户订单查询", - "businessDescription": "根据用户ID查询订单信息", - "sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}", - "parameters": { - "type": "object", - "required": ["userId"], - "properties": { - "userId": { - "type": "integer", - "description": "用户的唯一标识符", - "examples": [10086] - } - } - } - } -] -``` - -## 开发 - -### 依赖项 - -- Python >= 3.13 -- httpx >= 0.28.1 -- mcp[cli] >= 1.10.1 -- pypinyin >= 0.53.0 - -### 本地开发 - -```bash -# 克隆仓库 -git clone -cd lzwcai_mcp_sqlexecutor - -# 安装开发依赖 -pip install -e . - -# 运行服务器 -python -m lzwcai_mcp_sqlexecutor.main -``` - -## 构建与发布 - -### 使用 build 构建 - -```bash -pip install build -python -m build -``` - -### 发布到 PyPI - -```bash -pip install twine -twine upload dist/* -``` - -## 常见问题 - -### MCP Inspector 显示 JSON 解析错误 - -如果在使用 MCP Inspector 测试时遇到 `SyntaxError: Unexpected non-whitespace character after JSON` 错误，这是因为： - -1. **原因**：MCP 协议使用 stdio（标准输入输出）进行 JSON-RPC 通信，任何输出到 stdout 的内容（如 print 语句或控制台日志）都会破坏 JSON 格式。 - -2. **解决方案**：本服务器已将所有日志输出配置为仅写入文件（位于 `logs/` 目录），不输出到控制台。日志文件包括： - - `lzwcai_mcp_sqlexecutor.log` - 主日志文件 - - `lzwcai_mcp_sqlexecutor_error.log` - 错误日志 - - `lzwcai_mcp_sqlexecutor_daily.log` - 按日期滚动的日志 - - `mcp_services.log` - MCP 服务专用日志 - -3. **查看日志**：如果需要调试，请查看 `logs/` 目录下的日志文件。 - -## 许可证 - -MIT License - -## 作者 - -lzwcai - diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/__init__.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/__init__.py deleted file mode 100644 index 3e52bf0..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -""" -lzwcai-mcpskills-analyzeOrder - MCP server for executing business SQL queries -""" - -__version__ = "0.1.2" -__author__ = "lzwcai" - -__all__ = [] - diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/businessQueries.json b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/businessQueries.json deleted file mode 100644 index 2498d8b..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/businessQueries.json +++ /dev/null @@ -1,50 +0,0 @@ -[ - { - "id": "2006300000000000001", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "datasourceId": "19", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED'), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 5 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.5 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 10 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 80 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 5 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 10 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 30 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 20 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id LEFT JOIN global_metrics gm ON 1=1 LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000002", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "datasourceId": "19", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 20 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 2 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC LIMIT 15 ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END LIMIT 15 ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 36 OR last_report_time IS NULL OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 36) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC LIMIT 15 ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 15 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 15 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 72), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 60 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 30 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 15 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000003", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "datasourceId": "19", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 120 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 90 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 40 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 60 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 75 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 88 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 90 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 60 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name LIMIT 15 ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 90 AND sri.return_count > 3 THEN 'HIGH' WHEN sri.avg_delivery_days > 75 OR sri.return_count > 2 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC LIMIT 15 ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 20 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 12 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC LIMIT 15 ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC LIMIT 15 ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 20 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 12 AND 20 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 12 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 8 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 20 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 15 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs LIMIT 15 ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 20 THEN 'HIGH_RISK' WHEN return_rate > 12 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000004", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "datasourceId": "19", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 20 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 30 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 12 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC LIMIT 15 ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC LIMIT 15 ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category LIMIT 15 ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 20 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 12 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST LIMIT 15 ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000005", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "datasourceId": "19", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 88 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 65 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 12 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 80 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 40 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 15 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr LIMIT 15 ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month LIMIT 15 ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 15 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC LIMIT 15 ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 200000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 200000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 12, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 12 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 40, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 40 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 12, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 12 THEN 'EXCEEDED' ELSE 'NORMAL' END LIMIT 15 ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000006", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "datasourceId": "19", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.5 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.5 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 5 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -5 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.5 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.5 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.4 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.4 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.8 AND defect_rate > 8 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC LIMIT 15 ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.15 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.85 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.15 THEN 'RISING' WHEN total_output < prev_output * 0.85 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.3 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.7 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC LIMIT 15 ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.08 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.92 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.25 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.08 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.92 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.25 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.15 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.85 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 12 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.4 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs LIMIT 15 ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 15", - "parameters": {} - } -] \ No newline at end of file diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log deleted file mode 100644 index 75e57ee..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log +++ /dev/null @@ -1,922 +0,0 @@ -2026-01-08 00:15:16 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_analyzeOrder\lzwcai_mcpskills_analyzeOrder\logs -2026-01-08 00:15:16 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:15:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:15:19 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:15:19 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 00:15:19 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:19 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:15:19 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS \"订单编号\", c.customer_name AS \"客户名称\", so.order_date_utc::DATE AS \"订单日期\", so.deal_amount AS \"订单金额\", so.payment_status AS \"付款状态\", ROUND(gm.avg_production_days::NUMERIC, 1) AS \"历史平均生产周期(天)\", ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS \"历史平均物流延迟(天)\", COALESCE(lds.delay_count, 0)::INT AS \"历史延误次数\", ROUND(gm.defect_rate_pct::NUMERIC, 2) AS \"产品不良率%\", ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS \"报废率%\", gm.active_wo_count::INT AS \"进行中工单数\", gm.lagging_wo_count::INT AS \"滞后工单数\", ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS \"延迟概率%\", CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN '红色' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN '黄色' ELSE '绿色' END AS \"预警等级\", CASE WHEN gm.lagging_wo_count >= 2 THEN '生产严重滞后' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN '物流延误风险高' WHEN gm.avg_production_days > 15 THEN '生产周期过长' WHEN gm.defect_rate_pct > 10 THEN '质量问题突出' ELSE '正常' END AS \"主要风险因素\" FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY \"延迟概率%\" DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:15:19 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:15:19 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:15:19 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:15:27 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:15:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:15:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:27 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:15:27 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→待生产, STARTED→生产中, CLOSED→已完成），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN '待生产' WHEN 'STARTED' THEN '生产中' WHEN 'CLOSED' THEN '已完成' ELSE '未知' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN '进度严重滞后' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN '进度滞后' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN '质量异常' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '报工停滞' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN '无报工记录' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN '效率偏低' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number AS \"工单号\", product_name AS \"产品名称\", product_category AS \"产品类别\", status_name AS \"状态\", planned_qty AS \"计划数量\", completed_qty AS \"完成数量\", remaining_qty AS \"剩余数量\", completion_rate AS \"完成率(%)\", worker_count AS \"参与人数\", ROUND(total_work_minutes / 60.0, 1) AS \"累计工时(小时)\", elapsed_hours AS \"已用时间(小时)\", COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS \"异常标记\", CASE WHEN progress_anomaly = '进度严重滞后' OR quality_anomaly IS NOT NULL THEN '高' WHEN progress_anomaly = '进度滞后' OR labor_anomaly IS NOT NULL THEN '中' WHEN efficiency_anomaly IS NOT NULL THEN '低' ELSE '-' END AS \"风险等级\" FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:15:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:15:28 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:15:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:15:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:15:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:15:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:37 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:15:37 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT '经营决策简报' AS \"报告标题\", CURRENT_DATE AS \"报告日期\", ss.total_orders AS \"销售订单总数\", ROUND(ss.total_sales_amount, 2) AS \"销售总金额(元)\", ROUND(ss.avg_order_amount, 2) AS \"平均订单金额(元)\", ss.paid_orders AS \"已付款订单\", ss.partial_orders AS \"部分付款订单\", ss.unpaid_orders AS \"未付款订单\", ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS \"付款完成率(%)\", ROUND(ss.unpaid_amount, 2) AS \"待收款金额(元)\", ps.total_work_orders AS \"工单总数\", ps.closed_orders AS \"已完成工单\", ps.started_orders AS \"进行中工单\", ps.open_orders AS \"待开始工单\", ROUND(ps.total_planned_qty, 0) AS \"计划产量\", ROUND(ps.total_completed_qty, 0) AS \"完成产量\", ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS \"生产完成率(%)\", ls.worker_count AS \"活跃员工数\", ROUND(ls.total_work_minutes / 60.0, 1) AS \"总工时(小时)\", ROUND(ls.total_output_qty, 0) AS \"总产出量\", ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS \"人均产量\", ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS \"时均产量(件/小时)\", qs.inspection_count AS \"质检批次\", ROUND(qs.total_pass_qty, 0) AS \"合格数量\", ROUND(qs.total_fail_qty, 0) AS \"不合格数量\", ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS \"合格率(%)\", ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS \"不良率(%)\", scr.scrap_count AS \"报废记录数\", ar.receipt_count AS \"收款笔数\", ROUND(ar.total_receipt_amount, 2) AS \"收款总额(元)\", ap.payment_count AS \"付款笔数\", ROUND(ap.total_payment_amount, 2) AS \"付款总额(元)\", ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS \"净现金流(元)\", inv.invoice_count AS \"开票数量\", ROUND(inv.total_invoice_amount, 2) AS \"开票总额(元)\", sh.shipment_count AS \"发货单数\", ROUND(sh.total_shipment_amount, 2) AS \"发货总金额(元)\", pur.purchase_order_count AS \"采购订单数\", ret.return_count AS \"退货单数\", ROUND(ret.total_return_amount, 2) AS \"退货总金额(元)\", ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS \"退货率(%)\", CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN '优秀 - 各项指标健康' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN '良好 - 部分指标需关注' ELSE '预警 - 需要立即改善' END AS \"经营健康度\" FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:15:38 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:15:38 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:15:38 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:15:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:15:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-08 00:15:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:58 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:15:58 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department AS \"部门(产品类别)\", ls.worker_count AS \"人员数\", ROUND(ls.total_work_minutes / 60.0, 2) AS \"总工时(小时)\", ls.total_output_qty AS \"总产量\", ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS \"人均产量\", ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS \"时均产量(件/小时)\", ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS \"人效指数(产量/人/小时)\", ws.total_planned_qty AS \"计划产量\", ws.total_completed_qty AS \"完成产量\", ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS \"计划完成率(%)\", ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS \"估算产值(元)\", ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS \"人均产值(元)\", qs.total_pass_qty AS \"质检合格数\", qs.total_fail_qty AS \"质检不合格数\", ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS \"不良率(%)\", ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS \"生产损耗率(%)\", ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS \"综合绩效得分\", CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN '红色预警' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN '黄色预警' ELSE '正常' END AS \"状态预警\" FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY \"综合绩效得分\" DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:15:59 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:15:59 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:15:59 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:16:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:16:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-08 00:16:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:16:07 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:16:07 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN '高风险' WHEN delivery_risk_score + quality_risk_score >= 50 THEN '中风险' ELSE '低风险' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN '交期异常+质量问题' WHEN delivery_risk_score >= 50 THEN '交期异常' WHEN quality_risk_score >= 30 THEN '质量问题' ELSE '正常' END AS risk_pattern FROM supplier_risk) SELECT supplier_name AS \"供应商名称\", supplier_category AS \"供应商类别\", order_count AS \"订单数\", receipt_count AS \"收货数\", avg_delivery_days AS \"平均交期(天)\", max_delivery_days AS \"最长交期(天)\", delivery_volatility AS \"交期波动\", total_receipts AS \"收货批次\", return_count AS \"退货次数\", quality_rate AS \"合格率(%)\", delivery_risk_score AS \"交期风险分\", quality_risk_score AS \"质量风险分\", total_risk_score AS \"综合风险分\", risk_level AS \"风险等级\", risk_pattern AS \"风险模式\" FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:16:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:16:07 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:16:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:16:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:16:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:16:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:16:13 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:16:13 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→待生产, STARTED→生产中, CLOSED→已完成），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN '待生产' WHEN 'STARTED' THEN '生产中' WHEN 'CLOSED' THEN '已完成' ELSE '未知' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN '进度严重滞后' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN '进度滞后' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN '质量异常' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '报工停滞' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN '无报工记录' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN '效率偏低' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number AS \"工单号\", product_name AS \"产品名称\", product_category AS \"产品类别\", status_name AS \"状态\", planned_qty AS \"计划数量\", completed_qty AS \"完成数量\", remaining_qty AS \"剩余数量\", completion_rate AS \"完成率(%)\", worker_count AS \"参与人数\", ROUND(total_work_minutes / 60.0, 1) AS \"累计工时(小时)\", elapsed_hours AS \"已用时间(小时)\", COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS \"异常标记\", CASE WHEN progress_anomaly = '进度严重滞后' OR quality_anomaly IS NOT NULL THEN '高' WHEN progress_anomaly = '进度滞后' OR labor_anomaly IS NOT NULL THEN '中' WHEN efficiency_anomaly IS NOT NULL THEN '低' ELSE '-' END AS \"风险等级\" FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:16:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:16:13 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:16:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:22 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_analyzeOrder\lzwcai_mcpskills_analyzeOrder\logs -2026-01-08 00:30:22 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:30:26 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:30:27 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:30:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 00:30:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:27 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:30:27 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:30:27 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:30:27 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:30:27 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:32 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:30:32 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:30:32 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:32 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:30:32 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:30:32 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:30:32 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:30:32 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:34 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:30:34 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-08 00:30:34 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:34 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:30:34 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:30:34 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:30:34 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:30:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:35 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:30:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-08 00:30:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:35 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:30:35 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:30:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:30:35 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:30:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:30:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:30:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:37 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:30:37 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:30:38 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:30:38 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:30:38 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:39 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:30:39 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 00:30:39 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:39 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:30:39 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:30:39 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:30:39 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:30:39 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:44 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:30:44 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 00:30:44 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:44 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:30:44 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:30:44 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:30:44 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:30:44 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:38:55 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-08 00:38:55 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:38:57 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:38:59 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:38:59 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 00:38:59 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:38:59 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:38:59 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:52:34 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-08 00:52:34 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:52:36 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:52:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:52:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:52:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:52:37 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:52:37 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:53:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:53:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:53:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:53:21 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:53:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:56:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:56:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:56:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:56:21 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:56:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 09:52:27 - mcp_services - INFO - [main.py:344] - MCP 服务器已关闭 -2026-01-08 10:00:42 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-08 10:00:42 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 10:00:45 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 10:02:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:02:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 10:02:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:02:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:02:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:02:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:02:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:02:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:02:30 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:02:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 10:02:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:02:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:02:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:02:30 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:02:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:02:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:02:35 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:02:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 10:02:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:02:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:02:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:02:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:02:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:02:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:22:23 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:22:23 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 10:22:23 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:22:23 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:22:23 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:22:24 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:22:24 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:22:24 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:57:20 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:57:20 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 10:57:20 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:57:20 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:57:20 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:57:20 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:57:20 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:57:20 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:05:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 11:05:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-08 11:05:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:05:47 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 11:05:47 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name", - "parameters": {}, - "testParams": {} -} -2026-01-08 11:05:48 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 11:05:48 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 11:05:48 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:13:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 11:13:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-08 11:13:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:13:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 11:13:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 11:13:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 11:13:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 11:13:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:34:55 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 11:34:55 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 11:34:55 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:34:55 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 11:34:55 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr", - "parameters": {}, - "testParams": {} -} -2026-01-08 11:34:56 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 11:34:56 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 11:34:56 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:59:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 11:59:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 11:59:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:59:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 11:59:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 11:59:37 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 11:59:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 11:59:37 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:50:49 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-09 22:50:49 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 22:50:56 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 22:50:57 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:50:57 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-09 22:50:57 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:50:57 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:50:57 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:50:57 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:50:57 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:50:57 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:01 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:51:01 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-09 22:51:01 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:01 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:51:01 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 50 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:51:01 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:51:01 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:51:01 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:06 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:51:06 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-09 22:51:06 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:06 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:51:06 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:51:06 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:51:06 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:51:06 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:51:14 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-09 22:51:14 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:14 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:51:14 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:51:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:51:14 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:51:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:22 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:51:22 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-09 22:51:22 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:51:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:51:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:51:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:51:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:52:54 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:52:54 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-09 22:52:54 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:52:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:52:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:52:54 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:52:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:52:54 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 23:05:38 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 23:05:38 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-09 23:05:38 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 23:05:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 23:05:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 50 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts", - "parameters": {}, - "testParams": {} -} -2026-01-09 23:05:39 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 23:05:39 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 23:05:39 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 23:48:54 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 23:48:54 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-09 23:48:54 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 23:48:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 23:48:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20", - "parameters": {}, - "testParams": {} -} -2026-01-09 23:48:54 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 23:48:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 23:48:54 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-10 00:10:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-10 00:10:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-10 00:10:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status", - "parameters": {}, - "testParams": {} -} -2026-01-10 00:10:04 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-10 00:10:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-10 00:26:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-10 00:26:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-10 00:26:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12", - "parameters": {}, - "testParams": {} -} -2026-01-10 00:26:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-10 00:26:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-10 00:47:45 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-10 00:47:45 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-10 00:47:45 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-10 00:47:45 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-10 00:47:45 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10", - "parameters": {}, - "testParams": {} -} -2026-01-10 00:47:46 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-10 00:47:46 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-10 00:47:46 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:13 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-16 12:39:13 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-16 12:39:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-16 12:39:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-16 12:39:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id WHERE EXTRACT(YEAR FROM so.order_date_utc) = 2025 ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 30", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:17 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-16 12:39:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 30 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 30 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:26 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:26 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-16 12:39:26 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name LIMIT 30 ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC LIMIT 30 ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC LIMIT 30 ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC LIMIT 30 ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:26 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:26 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:30 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-16 12:39:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC LIMIT 30 ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:30 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-16 12:39:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC LIMIT 30 ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC LIMIT 30 ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category LIMIT 30 ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST LIMIT 30 ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status LIMIT 30", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:33 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:33 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:34 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:34 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-16 12:39:34 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC LIMIT 30 ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC LIMIT 30 ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:30:19 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-29 13:30:19 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 57 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 57 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.167.30.2:8088 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-29 13:30:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-29 13:30:22 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:30:22 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-29 13:30:22 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:30:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:30:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED'), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 5 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.5 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 10 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 80 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 5 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 10 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 30 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 20 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id LEFT JOIN global_metrics gm ON 1=1 LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:30:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:30:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:30:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:30:25 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:30:25 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-29 13:30:25 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:30:25 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:30:25 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 20 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 2 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 36 OR last_report_time IS NULL OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 36) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 15 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 15 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 72), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 60 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 30 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 15 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:30:25 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:30:25 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:30:25 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:26 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-29 13:36:26 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 57 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 57 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.167.30.2:8088 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-29 13:36:27 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-29 13:36:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-29 13:36:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED'), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 5 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.5 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 10 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 80 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 5 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 10 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 30 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 20 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id LEFT JOIN global_metrics gm ON 1=1 LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:30 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-29 13:36:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 20 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 2 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC LIMIT 15 ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END LIMIT 15 ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 36 OR last_report_time IS NULL OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 36) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC LIMIT 15 ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 15 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 15 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 72), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 60 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 30 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 15 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:30 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-29 13:36:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 120 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 90 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 40 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 60 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 75 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 88 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 90 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 60 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name LIMIT 15 ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 90 AND sri.return_count > 3 THEN 'HIGH' WHEN sri.avg_delivery_days > 75 OR sri.return_count > 2 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC LIMIT 15 ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 20 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 12 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC LIMIT 15 ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC LIMIT 15 ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 20 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 12 AND 20 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 12 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 8 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 20 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 15 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs LIMIT 15 ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 20 THEN 'HIGH_RISK' WHEN return_rate > 12 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:34 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:38 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:38 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-29 13:36:38 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 20 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 30 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 12 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC LIMIT 15 ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC LIMIT 15 ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category LIMIT 15 ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 20 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 12 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST LIMIT 15 ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:38 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:38 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:41 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:41 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-29 13:36:41 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:41 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:41 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.5 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.5 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 5 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -5 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.5 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.5 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.4 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.4 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.8 AND defect_rate > 8 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC LIMIT 15 ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.15 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.85 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.15 THEN 'RISING' WHEN total_output < prev_output * 0.85 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.3 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.7 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC LIMIT 15 ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.08 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.92 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.25 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.08 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.92 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.25 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.15 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.85 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 12 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.4 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs LIMIT 15 ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:41 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:41 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:41 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log deleted file mode 100644 index be7799b..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log +++ /dev/null @@ -1,143 +0,0 @@ -2026-01-29 13:30:19 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-29 13:30:19 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 57 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 57 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.167.30.2:8088 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-29 13:30:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-29 13:30:22 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:30:22 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-29 13:30:22 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:30:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:30:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED'), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 5 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.5 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 10 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 80 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 5 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 10 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 30 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 20 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id LEFT JOIN global_metrics gm ON 1=1 LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:30:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:30:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:30:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:30:25 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:30:25 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-29 13:30:25 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:30:25 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:30:25 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 20 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 2 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 36 OR last_report_time IS NULL OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 36) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 15 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 15 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 72), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 60 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 30 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 15 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:30:25 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:30:25 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:30:25 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:26 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-29 13:36:26 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 57 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 57 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.167.30.2:8088 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-29 13:36:27 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-29 13:36:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-29 13:36:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED'), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 5 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.5 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 10 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 80 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 5 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 10 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 30 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 20 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id LEFT JOIN global_metrics gm ON 1=1 LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:30 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-29 13:36:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 20 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 2 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC LIMIT 15 ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END LIMIT 15 ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 36 OR last_report_time IS NULL OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 36) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC LIMIT 15 ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 15 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 15 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 72), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 60 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 30 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 15 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:30 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-29 13:36:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 120 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 90 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 40 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 60 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 75 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 88 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 90 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 60 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name LIMIT 15 ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 90 AND sri.return_count > 3 THEN 'HIGH' WHEN sri.avg_delivery_days > 75 OR sri.return_count > 2 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC LIMIT 15 ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 20 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 12 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC LIMIT 15 ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC LIMIT 15 ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 20 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 12 AND 20 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 12 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 8 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 20 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 15 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs LIMIT 15 ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 20 THEN 'HIGH_RISK' WHEN return_rate > 12 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:34 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:38 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:38 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-29 13:36:38 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 20 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 30 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 12 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC LIMIT 15 ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC LIMIT 15 ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category LIMIT 15 ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 20 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 12 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST LIMIT 15 ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:38 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:38 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:41 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-29 13:36:41 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-29 13:36:41 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:41 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-29 13:36:41 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "57", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.5 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.5 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 5 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -5 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.5 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.5 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.4 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.4 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.8 AND defect_rate > 8 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC LIMIT 15 ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.15 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.85 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.15 THEN 'RISING' WHEN total_output < prev_output * 0.85 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.3 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.7 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC LIMIT 15 ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.08 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.92 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.25 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.08 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.92 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.25 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.15 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.85 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 12 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.4 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs LIMIT 15 ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 15", - "parameters": {}, - "testParams": {} -} -2026-01-29 13:36:41 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-29 13:36:41 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-29 13:36:41 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-08 b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-08 deleted file mode 100644 index b3fb093..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-08 +++ /dev/null @@ -1,362 +0,0 @@ -2026-01-08 00:15:16 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_analyzeOrder\lzwcai_mcpskills_analyzeOrder\logs -2026-01-08 00:15:16 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:15:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:15:19 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:15:19 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 00:15:19 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:19 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:15:19 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS \"订单编号\", c.customer_name AS \"客户名称\", so.order_date_utc::DATE AS \"订单日期\", so.deal_amount AS \"订单金额\", so.payment_status AS \"付款状态\", ROUND(gm.avg_production_days::NUMERIC, 1) AS \"历史平均生产周期(天)\", ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS \"历史平均物流延迟(天)\", COALESCE(lds.delay_count, 0)::INT AS \"历史延误次数\", ROUND(gm.defect_rate_pct::NUMERIC, 2) AS \"产品不良率%\", ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS \"报废率%\", gm.active_wo_count::INT AS \"进行中工单数\", gm.lagging_wo_count::INT AS \"滞后工单数\", ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS \"延迟概率%\", CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN '红色' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN '黄色' ELSE '绿色' END AS \"预警等级\", CASE WHEN gm.lagging_wo_count >= 2 THEN '生产严重滞后' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN '物流延误风险高' WHEN gm.avg_production_days > 15 THEN '生产周期过长' WHEN gm.defect_rate_pct > 10 THEN '质量问题突出' ELSE '正常' END AS \"主要风险因素\" FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY \"延迟概率%\" DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:15:19 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:15:19 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:15:19 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:15:27 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:15:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:15:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:27 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:15:27 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→待生产, STARTED→生产中, CLOSED→已完成），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN '待生产' WHEN 'STARTED' THEN '生产中' WHEN 'CLOSED' THEN '已完成' ELSE '未知' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN '进度严重滞后' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN '进度滞后' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN '质量异常' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '报工停滞' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN '无报工记录' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN '效率偏低' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number AS \"工单号\", product_name AS \"产品名称\", product_category AS \"产品类别\", status_name AS \"状态\", planned_qty AS \"计划数量\", completed_qty AS \"完成数量\", remaining_qty AS \"剩余数量\", completion_rate AS \"完成率(%)\", worker_count AS \"参与人数\", ROUND(total_work_minutes / 60.0, 1) AS \"累计工时(小时)\", elapsed_hours AS \"已用时间(小时)\", COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS \"异常标记\", CASE WHEN progress_anomaly = '进度严重滞后' OR quality_anomaly IS NOT NULL THEN '高' WHEN progress_anomaly = '进度滞后' OR labor_anomaly IS NOT NULL THEN '中' WHEN efficiency_anomaly IS NOT NULL THEN '低' ELSE '-' END AS \"风险等级\" FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:15:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:15:28 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:15:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:15:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:15:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:15:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:37 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:15:37 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT '经营决策简报' AS \"报告标题\", CURRENT_DATE AS \"报告日期\", ss.total_orders AS \"销售订单总数\", ROUND(ss.total_sales_amount, 2) AS \"销售总金额(元)\", ROUND(ss.avg_order_amount, 2) AS \"平均订单金额(元)\", ss.paid_orders AS \"已付款订单\", ss.partial_orders AS \"部分付款订单\", ss.unpaid_orders AS \"未付款订单\", ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS \"付款完成率(%)\", ROUND(ss.unpaid_amount, 2) AS \"待收款金额(元)\", ps.total_work_orders AS \"工单总数\", ps.closed_orders AS \"已完成工单\", ps.started_orders AS \"进行中工单\", ps.open_orders AS \"待开始工单\", ROUND(ps.total_planned_qty, 0) AS \"计划产量\", ROUND(ps.total_completed_qty, 0) AS \"完成产量\", ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS \"生产完成率(%)\", ls.worker_count AS \"活跃员工数\", ROUND(ls.total_work_minutes / 60.0, 1) AS \"总工时(小时)\", ROUND(ls.total_output_qty, 0) AS \"总产出量\", ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS \"人均产量\", ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS \"时均产量(件/小时)\", qs.inspection_count AS \"质检批次\", ROUND(qs.total_pass_qty, 0) AS \"合格数量\", ROUND(qs.total_fail_qty, 0) AS \"不合格数量\", ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS \"合格率(%)\", ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS \"不良率(%)\", scr.scrap_count AS \"报废记录数\", ar.receipt_count AS \"收款笔数\", ROUND(ar.total_receipt_amount, 2) AS \"收款总额(元)\", ap.payment_count AS \"付款笔数\", ROUND(ap.total_payment_amount, 2) AS \"付款总额(元)\", ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS \"净现金流(元)\", inv.invoice_count AS \"开票数量\", ROUND(inv.total_invoice_amount, 2) AS \"开票总额(元)\", sh.shipment_count AS \"发货单数\", ROUND(sh.total_shipment_amount, 2) AS \"发货总金额(元)\", pur.purchase_order_count AS \"采购订单数\", ret.return_count AS \"退货单数\", ROUND(ret.total_return_amount, 2) AS \"退货总金额(元)\", ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS \"退货率(%)\", CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN '优秀 - 各项指标健康' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN '良好 - 部分指标需关注' ELSE '预警 - 需要立即改善' END AS \"经营健康度\" FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:15:38 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:15:38 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:15:38 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:15:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:15:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-08 00:15:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:58 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:15:58 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department AS \"部门(产品类别)\", ls.worker_count AS \"人员数\", ROUND(ls.total_work_minutes / 60.0, 2) AS \"总工时(小时)\", ls.total_output_qty AS \"总产量\", ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS \"人均产量\", ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS \"时均产量(件/小时)\", ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS \"人效指数(产量/人/小时)\", ws.total_planned_qty AS \"计划产量\", ws.total_completed_qty AS \"完成产量\", ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS \"计划完成率(%)\", ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS \"估算产值(元)\", ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS \"人均产值(元)\", qs.total_pass_qty AS \"质检合格数\", qs.total_fail_qty AS \"质检不合格数\", ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS \"不良率(%)\", ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS \"生产损耗率(%)\", ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS \"综合绩效得分\", CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN '红色预警' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN '黄色预警' ELSE '正常' END AS \"状态预警\" FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY \"综合绩效得分\" DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:15:59 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:15:59 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:15:59 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:16:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:16:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-08 00:16:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:16:07 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:16:07 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN '高风险' WHEN delivery_risk_score + quality_risk_score >= 50 THEN '中风险' ELSE '低风险' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN '交期异常+质量问题' WHEN delivery_risk_score >= 50 THEN '交期异常' WHEN quality_risk_score >= 30 THEN '质量问题' ELSE '正常' END AS risk_pattern FROM supplier_risk) SELECT supplier_name AS \"供应商名称\", supplier_category AS \"供应商类别\", order_count AS \"订单数\", receipt_count AS \"收货数\", avg_delivery_days AS \"平均交期(天)\", max_delivery_days AS \"最长交期(天)\", delivery_volatility AS \"交期波动\", total_receipts AS \"收货批次\", return_count AS \"退货次数\", quality_rate AS \"合格率(%)\", delivery_risk_score AS \"交期风险分\", quality_risk_score AS \"质量风险分\", total_risk_score AS \"综合风险分\", risk_level AS \"风险等级\", risk_pattern AS \"风险模式\" FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:16:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:16:07 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:16:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:16:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:16:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:16:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:16:13 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:16:13 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→待生产, STARTED→生产中, CLOSED→已完成），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN '待生产' WHEN 'STARTED' THEN '生产中' WHEN 'CLOSED' THEN '已完成' ELSE '未知' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN '进度严重滞后' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN '进度滞后' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN '质量异常' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '报工停滞' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN '无报工记录' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN '效率偏低' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number AS \"工单号\", product_name AS \"产品名称\", product_category AS \"产品类别\", status_name AS \"状态\", planned_qty AS \"计划数量\", completed_qty AS \"完成数量\", remaining_qty AS \"剩余数量\", completion_rate AS \"完成率(%)\", worker_count AS \"参与人数\", ROUND(total_work_minutes / 60.0, 1) AS \"累计工时(小时)\", elapsed_hours AS \"已用时间(小时)\", COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS \"异常标记\", CASE WHEN progress_anomaly = '进度严重滞后' OR quality_anomaly IS NOT NULL THEN '高' WHEN progress_anomaly = '进度滞后' OR labor_anomaly IS NOT NULL THEN '中' WHEN efficiency_anomaly IS NOT NULL THEN '低' ELSE '-' END AS \"风险等级\" FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:16:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:16:13 - lzwcai_mcpskills_analyzeOrder.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:16:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:38:55 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-08 00:38:55 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:38:57 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:38:59 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:38:59 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 00:38:59 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:38:59 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:38:59 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:38:59 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:52:34 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-08 00:52:34 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:52:36 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:52:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:52:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:52:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:52:37 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:52:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:52:37 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:53:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:53:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:53:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:53:21 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:53:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:53:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:56:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 00:56:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:56:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr", - "parameters": {}, - "testParams": {} -} -2026-01-08 00:56:21 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 00:56:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 00:56:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 09:52:27 - mcp_services - INFO - [main.py:344] - MCP 服务器已关闭 -2026-01-08 10:00:42 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-08 10:00:42 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 10:00:45 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 10:02:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:02:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 10:02:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:02:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:02:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:02:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:02:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:02:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:02:30 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:02:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 10:02:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:02:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:02:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:02:30 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:02:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:02:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:02:35 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:02:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 10:02:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:02:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:02:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:02:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:02:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:02:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:22:23 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:22:23 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 10:22:23 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:22:23 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:22:23 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:22:24 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:22:24 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:22:24 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:57:20 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 10:57:20 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 10:57:20 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:57:20 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 10:57:20 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC", - "parameters": {}, - "testParams": {} -} -2026-01-08 10:57:20 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 10:57:20 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 10:57:20 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:05:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 11:05:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-08 11:05:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:05:47 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 11:05:47 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name", - "parameters": {}, - "testParams": {} -} -2026-01-08 11:05:48 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 11:05:48 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 11:05:48 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:13:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 11:13:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-08 11:13:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:13:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 11:13:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 11:13:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 11:13:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 11:13:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:34:55 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 11:34:55 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 11:34:55 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:34:55 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 11:34:55 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr", - "parameters": {}, - "testParams": {} -} -2026-01-08 11:34:56 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 11:34:56 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 11:34:56 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:59:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-08 11:59:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 11:59:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:59:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema -2026-01-08 11:59:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC", - "parameters": {}, - "testParams": {} -} -2026-01-08 11:59:37 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testSqlWithSchema "HTTP/1.1 200 " -2026-01-08 11:59:37 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-08 11:59:37 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-09 b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-09 deleted file mode 100644 index 0976602..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-09 +++ /dev/null @@ -1,139 +0,0 @@ -2026-01-09 22:50:49 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-09 22:50:49 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 22:50:56 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 22:50:57 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:50:57 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-09 22:50:57 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:50:57 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:50:57 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:50:57 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:50:57 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:50:57 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:01 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:51:01 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-09 22:51:01 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:01 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:51:01 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 50 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:51:01 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:51:01 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:51:01 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:06 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:51:06 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-09 22:51:06 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:06 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:51:06 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:51:06 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:51:06 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:51:06 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:51:14 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-09 22:51:14 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:14 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:51:14 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:51:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:51:14 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:51:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:22 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:51:22 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-09 22:51:22 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:51:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:51:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:51:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:51:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:52:54 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 22:52:54 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-09 22:52:54 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:52:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 22:52:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC", - "parameters": {}, - "testParams": {} -} -2026-01-09 22:52:54 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 22:52:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 22:52:54 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 23:05:38 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 23:05:38 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-09 23:05:38 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 23:05:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 23:05:38 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 50 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts", - "parameters": {}, - "testParams": {} -} -2026-01-09 23:05:39 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 23:05:39 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 23:05:39 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 23:48:54 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 23:48:54 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-09 23:48:54 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 23:48:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 23:48:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20", - "parameters": {}, - "testParams": {} -} -2026-01-09 23:48:54 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 23:48:54 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 23:48:54 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-10 b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-10 deleted file mode 100644 index 4466214..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-10 +++ /dev/null @@ -1,45 +0,0 @@ -2026-01-10 00:10:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-10 00:10:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-10 00:10:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status", - "parameters": {}, - "testParams": {} -} -2026-01-10 00:10:04 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-10 00:10:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-10 00:26:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-10 00:26:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-10 00:26:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12", - "parameters": {}, - "testParams": {} -} -2026-01-10 00:26:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-10 00:26:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-10 00:47:45 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-10 00:47:45 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-10 00:47:45 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-10 00:47:45 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-10 00:47:45 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10", - "parameters": {}, - "testParams": {} -} -2026-01-10 00:47:46 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-10 00:47:46 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-10 00:47:46 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-26 b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-26 deleted file mode 100644 index ae6a095..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-26 +++ /dev/null @@ -1,109 +0,0 @@ -2026-01-16 12:39:13 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs -2026-01-16 12:39:13 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-16 12:39:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-16 12:39:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-16 12:39:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OrderDelayWarningAnalysis", - "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级", - "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id WHERE EXTRACT(YEAR FROM so.order_date_utc) = 2025 ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 30", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:17 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-16 12:39:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "WorkOrderProgressAndAnomalyNodes", - "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点", - "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 30 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 30 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:26 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:26 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-16 12:39:26 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplyChainRiskWarning", - "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示", - "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name LIMIT 30 ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC LIMIT 30 ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC LIMIT 30 ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC LIMIT 30 ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:26 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:26 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:30 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-16 12:39:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "OnePageDecisionBrief", - "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报", - "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC LIMIT 30 ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:30 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-16 12:39:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "EfficiencyOutputLossDashboard", - "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示", - "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC LIMIT 30 ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC LIMIT 30 ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category LIMIT 30 ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST LIMIT 30 ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status LIMIT 30", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:33 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:33 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:34 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:39:34 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-16 12:39:34 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:39:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "MetricTrendAndTurningPointWarning", - "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警", - "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC LIMIT 30 ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC LIMIT 30 ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:39:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:39:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:39:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_error.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_error.log deleted file mode 100644 index e69de29..0000000 diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log deleted file mode 100644 index 0e5d22a..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log +++ /dev/null @@ -1,295 +0,0 @@ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:15:16 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:15:17 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:15:19 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 00:15:19 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:19 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:15:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:15:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:15:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:15:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:38 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:15:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-08 00:15:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:15:59 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:16:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-08 00:16:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:16:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:16:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:16:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:16:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:30:22 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:30:26 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:30:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 00:30:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:27 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:32 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:30:32 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:32 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:34 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-08 00:30:34 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-08 00:30:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:30:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:38 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:39 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 00:30:39 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:39 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:30:44 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 00:30:44 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:30:44 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:38:55 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:38:57 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:38:59 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 00:38:59 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:38:59 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 00:52:34 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 00:52:36 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 00:52:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 00:52:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:52:37 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:53:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:53:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:53:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 00:56:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 00:56:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 00:56:21 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 09:52:27 - mcp_services - INFO - [main.py:344] - MCP 服务器已关闭 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-08 10:00:42 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-08 10:00:45 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-08 10:02:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 10:02:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:02:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:02:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 10:02:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:02:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:02:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 10:02:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:02:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:22:23 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-08 10:22:23 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:22:24 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 10:57:20 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-08 10:57:20 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 10:57:20 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:05:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-08 11:05:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:05:48 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:13:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-08 11:13:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:13:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:34:55 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-08 11:34:55 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:34:56 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-08 11:59:37 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-08 11:59:37 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-08 11:59:37 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 22:50:49 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 22:50:56 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 22:50:57 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-09 22:50:57 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:50:57 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:01 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-09 22:51:01 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:01 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:06 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-09 22:51:06 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:06 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:14 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-09 22:51:14 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:51:22 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-09 22:51:22 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:51:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 22:52:54 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-09 22:52:54 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 22:52:54 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 23:05:38 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-09 23:05:38 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 23:05:39 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 23:48:54 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-09 23:48:54 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 23:48:54 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-10 00:10:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-10 00:10:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-10 00:10:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-10 00:26:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-10 00:26:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-10 00:26:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-10 00:47:45 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-10 00:47:45 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-10 00:47:46 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-16 12:39:13 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-16 12:39:14 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-16 12:39:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-16 12:39:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-16 12:39:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:26 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-16 12:39:26 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:26 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief -2026-01-16 12:39:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-16 12:39:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:33 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:39:34 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-16 12:39:34 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:39:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 57 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 57 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.167.30.2:8088 -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-29 13:30:19 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-29 13:30:21 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-29 13:30:22 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-29 13:30:22 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:30:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:30:25 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-29 13:30:25 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:30:25 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 57 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 57 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.167.30.2:8088 -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-29 13:36:26 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-29 13:36:27 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-29 13:36:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis -2026-01-29 13:36:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes -2026-01-29 13:36:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning -2026-01-29 13:36:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:38 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard -2026-01-29 13:36:38 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:38 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-29 13:36:41 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning -2026-01-29 13:36:41 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-29 13:36:41 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/main.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/main.py deleted file mode 100644 index 71cb13f..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/main.py +++ /dev/null @@ -1,373 +0,0 @@ -from pathlib import Path -from typing import Any -import asyncio -import logging - -# 支持直接运行和模块导入两种方式 -try: - from .utils import load_json, generate_tool_name, generate_input_schema - from .utils import get_skill_by_id, DataSourceAPIClient, process_skill_response, test_sql_with_schema - from .utils import get_database_id, get_datasource_id, get_skill_id, get_env_config - from .utils.logger_config import logger_config -except ImportError: - from utils import load_json, generate_tool_name, generate_input_schema - from utils import get_skill_by_id, DataSourceAPIClient, process_skill_response, test_sql_with_schema - from utils import get_database_id, get_datasource_id, get_skill_id, get_env_config - from utils.logger_config import logger_config - -from mcp.server.models import InitializationOptions -from mcp.server import NotificationOptions, Server -import mcp.types as types - -# 初始化 MCP 专用日志器 -mcp_logger = logger_config.setup_mcp_logging() - -# ========== 数据源配置 ========== -# 数据源类型常量 -DATA_SOURCE_API = "api" # 仅使用API数据 -DATA_SOURCE_LOCAL = "local" # 仅使用本地JSON数据 -DATA_SOURCE_BOTH = "both" # 合并本地和API数据 - -# 默认数据源（可修改） -DEFAULT_DATA_SOURCE = DATA_SOURCE_LOCAL -# ================================ - - -def get_queries(): - """ - 获取业务查询配置 - - Returns: - list: 包含所有业务查询配置的列表 - """ - try: - # 获取当前文件所在目录 - current_dir = Path(__file__).parent - - # 构建 businessQueries.json 的路径 - json_path = current_dir / "businessQueries.json" - - mcp_logger.debug(f"正在读取业务查询配置文件: {json_path}") - - # 使用 load_json 方法读取 JSON 文件 - queries = load_json(json_path) - - mcp_logger.info(f"成功加载 {len(queries)} 个业务查询配置") - - return queries - except Exception as e: - mcp_logger.error(f"加载业务查询配置失败: {e}", exc_info=True) - raise - - -def generate_tool_schema_from_query(query: dict) -> types.Tool: - """ - 根据查询配置生成 MCP 工具模式 - - Args: - query: 单个查询配置字典 - - Returns: - types.Tool: MCP 工具对象 - """ - try: - # 获取参数定义并生成 inputSchema - parameters = query.get('parameters', {}) - input_schema = generate_input_schema(parameters) - - # 生成工具名称（格式: tool_拼音_id） - # tool_name = generate_tool_name(query['businessName'], query['id']) - tool_name = query['businessName'] - # 构建工具描述，包含业务名称和业务描述 - description = f"{query['businessName']}: {query['businessDescription']}" - - mcp_logger.debug(f"生成工具模式: {tool_name} - {query['businessName']}") - - return types.Tool( - name=tool_name, - description=description, - inputSchema=input_schema - ) - except Exception as e: - mcp_logger.error(f"生成工具模式失败: {query.get('id', 'unknown')}, 错误: {e}", exc_info=True) - raise - - -# 创建 MCP 服务器实例 -server = Server("lzwcai-mcpskills-mfg-data-agent") - -# 缓存查询配置，避免重复加载 -_queries_cache = None - - -async def get_queries_cache(source: str = None): - """ - 获取或初始化查询配置缓存 - - Args: - source: 数据源类型（默认使用 DEFAULT_DATA_SOURCE） - - "api": 仅使用API数据 - - "local": 仅使用本地JSON数据 - - "both": 合并本地和API数据 - - Returns: - 查询配置列表 - """ - global _queries_cache - if _queries_cache is None: - source = source or DEFAULT_DATA_SOURCE - mcp_logger.info(f"初始化查询配置（数据源: {source}）...") - - if source == DATA_SOURCE_LOCAL: - _queries_cache = get_queries() - mcp_logger.info(f"本地配置: {len(_queries_cache)} 条") - - elif source == DATA_SOURCE_API: - try: - _queries_cache = await call_third_party_api() - mcp_logger.info(f"API配置: {len(_queries_cache)} 条") - mcp_logger.info(f"API配置数组: {_queries_cache}") - except Exception as e: - mcp_logger.warning(f"API获取失败，降级使用本地配置: {e}") - _queries_cache = get_queries() - - else: # DATA_SOURCE_BOTH - local = get_queries() - try: - api = await call_third_party_api() - except Exception as e: - mcp_logger.warning(f"API获取失败: {e}") - api = [] - _queries_cache = local + api - mcp_logger.info(f"配置总数: {len(_queries_cache)} 条（本地{len(local)}+API{len(api)}）") - - return _queries_cache - - -@server.list_tools() -async def handle_list_tools() -> list[types.Tool]: - """ - 列出所有动态生成的 MCP 工具 - - Returns: - list[types.Tool]: 所有可用的工具列表 - """ - try: - mcp_logger.info("收到列出工具请求") - - queries = await get_queries_cache() - tools = [] - - for query in queries: - tool = generate_tool_schema_from_query(query) - tools.append(tool) - - mcp_logger.info(f"成功生成 {len(tools)} 个 MCP 工具") - mcp_logger.debug(f"工具列表: {[tool.name for tool in tools]}") - - return tools - except Exception as e: - mcp_logger.error(f"列出工具失败: {e}", exc_info=True) - raise - - -@server.call_tool() -async def handle_call_tool( - name: str, - arguments: dict[str, Any] | None -) -> list[types.TextContent]: - """ - 处理工具调用请求 - - Args: - name: 工具名称 - arguments: 工具参数 - - Returns: - list[types.TextContent]: 工具执行结果（返回参数和对应的接口配置） - """ - try: - mcp_logger.info(f"收到工具调用请求: {name}") - mcp_logger.debug(f"工具参数: {arguments}") - - # 获取查询配置缓存 - queries = await get_queries_cache() - - # 根据工具名称查找对应的 item（接口配置） - tool_item = None - for query in queries: - # tool_name = generate_tool_name(query['businessName'], query['id']) - tool_name = query['businessName'] - if tool_name == name: - tool_item = query - break - - # 构建返回结果 - import json - - if tool_item: - request_data = { - "datasourceId": get_datasource_id(), - "businessName": tool_item.get("businessName"), - "businessDescription": tool_item.get("businessDescription"), - "sqlTemplate": tool_item.get("sqlTemplate"), - "parameters": tool_item.get("parameters"), - "testParams": arguments or {} - } - - # 如果 arguments 中有 targetDatabaseName 且有值，添加到 request_data - if arguments and arguments.get("targetDatabaseName"): - request_data["targetDatabaseName"] = arguments["targetDatabaseName"] - mcp_logger.debug(f"添加目标数据库名称: {arguments['targetDatabaseName']}") - - # 调用测试SQL API - try: - mcp_logger.info("正在调用测试SQL API...") - api_response = test_sql_with_schema(request_data) - mcp_logger.info("测试SQL API调用成功") - - # 返回包含 data 字段的结果 - result = { - "success": True, - "data": api_response - } - result_text = json.dumps(result, ensure_ascii=False, indent=2) - - except Exception as e: - error_msg = f"调用测试SQL API失败: {str(e)}" - mcp_logger.error(error_msg, exc_info=True) - result = { - "success": False, - "error": error_msg, - "data": None - } - result_text = json.dumps(result, ensure_ascii=False, indent=2) - else: - error_msg = f"未找到工具 {name} 对应的配置" - result = { - "success": False, - "error": error_msg, - "data": None - } - result_text = json.dumps(result, ensure_ascii=False, indent=2) - - mcp_logger.debug(f"工具调用结果: {result_text}") - - return [ - types.TextContent( - type="text", - text=result_text - ) - ] - except Exception as e: - error_msg = f"工具调用失败: {name}, 错误: {e}" - mcp_logger.error(error_msg, exc_info=True) - return [ - types.TextContent( - type="text", - text=f"错误: {error_msg}" - ) - ] - - -async def call_third_party_api(skill_id: str = None) -> list: - """ - 调用第三方API获取技能信息并返回处理后的数据 - - Args: - skill_id: 技能ID（默认从环境变量 SKILL_ID 读取，如果未设置则使用 1981000305474482178） - - Returns: - 处理后的查询配置列表（businessQueries格式） - - Example: - queries = await call_third_party_api() - # 返回: [{"id": "...", "businessName": "...", ...}, ...] - """ - try: - # 如果没有传入 skill_id，则从环境变量读取 - if skill_id is None: - skill_id = get_skill_id() - - mcp_logger.info(f"调用第三方API，skill_id: {skill_id}") - - # 获取原始数据 - raw_result = get_skill_by_id(skill_id) - - mcp_logger.info(f"成功{raw_result}") - - # 处理并返回 - processed_queries = process_skill_response(raw_result) - - mcp_logger.info(f"成功获取并处理 {len(processed_queries)} 条数据") - return processed_queries - - except Exception as e: - mcp_logger.error(f"API调用失败: {e}", exc_info=True) - raise - - -async def async_main(): - """MCP 服务器异步主函数""" - try: - mcp_logger.info("=" * 60) - mcp_logger.info("正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder") - mcp_logger.info("版本: 0.1.0") - mcp_logger.info("=" * 60) - - # 输出环境配置信息 - env_config = get_env_config() - mcp_logger.info(f"环境配置 - Database ID: {env_config['database_id']}") - mcp_logger.info(f"环境配置 - Datasource ID: {env_config['datasource_id']}") - mcp_logger.info(f"环境配置 - Skill ID: {env_config['skill_id']}") - mcp_logger.info(f"环境配置 - Backend Base URL: {env_config['backend_base_url']}") - mcp_logger.info("=" * 60) - - from mcp.server.stdio import stdio_server - - async with stdio_server() as (read_stream, write_stream): - mcp_logger.info("MCP 服务器已启动，等待客户端连接...") - - await server.run( - read_stream, - write_stream, - InitializationOptions( - server_name="lzwcai-mcpskills-analyzeOrder", - server_version="0.1.0", - capabilities=server.get_capabilities( - notification_options=NotificationOptions(), - experimental_capabilities={}, - ), - ), - ) - - mcp_logger.info("MCP 服务器已关闭") - - except Exception as e: - mcp_logger.error(f"MCP 服务器运行失败: {e}", exc_info=True) - raise - - -def main(): - """入口点函数（用于 console_scripts）""" - try: - # 初始化系统日志 - # MCP协议使用stdio通信，必须禁用控制台输出以避免干扰JSON-RPC通信 - logger_config.setup_logging( - app_name="lzwcai_mcp_sqlexecutor", - log_level=logging.INFO, - console_output=False # 禁用控制台输出 - ) - - mcp_logger.info("开始运行 MCP SQL Executor 服务器") - asyncio.run(async_main()) - - except KeyboardInterrupt: - mcp_logger.info("收到中断信号，正在关闭服务器...") - except Exception as e: - mcp_logger.error(f"程序运行失败: {e}", exc_info=True) - raise - - -if __name__ == "__main__": - main() diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/pyproject.toml b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/pyproject.toml deleted file mode 100644 index d6139e9..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/pyproject.toml +++ /dev/null @@ -1,35 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai-mcpskills-mfg-data-agent" -version = "0.1.1" -description = "制造业数据智能体 - MCP server for manufacturing data intelligence with dynamic tool generation" -readme = "README.md" -requires-python = ">=3.13" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "sql", "manufacturing", "data", "agent", "智能体"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.13", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", - "pypinyin>=0.53.0", -] - -[project.scripts] -lzwcai-mcpskills-mfg-data-agent = "lzwcai_mcpskills_mfg_data_agent.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcpskills_mfg_data_agent"] - -[tool.hatch.build.targets.wheel.force-include] -"lzwcai_mcpskills_mfg_data_agent/businessQueries.json" = "lzwcai_mcpskills_mfg_data_agent/businessQueries.json" diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/sql11 b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/sql11 deleted file mode 100644 index 39b7319..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/sql11 +++ /dev/null @@ -1,169 +0,0 @@ --- ===================================================== --- 交付风险预测：延迟概率与红/黄/绿预警等级 --- 基于历史订单的生产周期、物流延误、设备故障等特征 --- ===================================================== - -WITH --- 1. 全局生产特征（汇总所有工单） -global_production AS ( - SELECT - COUNT(*) AS total_wo_count, - AVG(CASE WHEN planned_qty > 0 THEN completed_qty / planned_qty ELSE 0 END) AS avg_completion_rate, - SUM(CASE WHEN status IN ('OPEN', 'STARTED') THEN 1 ELSE 0 END) AS pending_wo_count, - SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_wo_count - FROM fact_work_order -), - --- 2. 全局质检特征 -global_quality AS ( - SELECT - COUNT(*) AS total_inspection_count, - SUM(COALESCE(pass_qty, 0)) AS total_pass_qty, - SUM(COALESCE(fail_qty, 0)) AS total_fail_qty, - CASE WHEN SUM(COALESCE(pass_qty, 0) + COALESCE(fail_qty, 0)) > 0 - THEN SUM(COALESCE(pass_qty, 0))::FLOAT / SUM(COALESCE(pass_qty, 0) + COALESCE(fail_qty, 0)) - ELSE 1 END AS qc_pass_rate - FROM fact_quality_inspection -), - --- 3. 全局工序不良特征 -global_operation AS ( - SELECT - COUNT(*) AS total_task_count, - SUM(COALESCE(good_qty, 0)) AS total_good_qty, - SUM(COALESCE(bad_qty, 0)) AS total_bad_qty, - CASE WHEN SUM(COALESCE(good_qty, 0) + COALESCE(bad_qty, 0)) > 0 - THEN SUM(COALESCE(bad_qty, 0))::FLOAT / SUM(COALESCE(good_qty, 0) + COALESCE(bad_qty, 0)) - ELSE 0 END AS operation_defect_rate - FROM fact_operation_task -), - --- 4. 客户级别发货统计 -customer_shipment AS ( - SELECT - customer_id, - COUNT(*) AS shipment_count, - SUM(COALESCE(amount, 0)) AS total_shipment_amount - FROM fact_sales_shipment - GROUP BY customer_id -), - --- 5. 客户级别退货统计 -customer_return AS ( - SELECT - customer_id, - COUNT(*) AS return_count, - SUM(COALESCE(amount, 0)) AS total_return_amount - FROM fact_sales_return - GROUP BY customer_id -), - --- 6. 订单风险评估 -order_risk AS ( - SELECT - so.sales_order_id, - so.sales_order_number, - c.customer_name, - so.order_date_utc, - so.deal_amount, - so.payment_status, - - -- 全局生产指标 - gp.avg_completion_rate AS production_completion_rate, - gp.pending_wo_count, - gp.total_wo_count AS work_order_count, - - -- 全局质检指标 - gq.qc_pass_rate, - gq.total_fail_qty, - - -- 全局工序指标 - go.operation_defect_rate, - - -- 客户级别指标 - COALESCE(cs.shipment_count, 0) AS shipment_count, - COALESCE(cr.return_count, 0) AS return_count, - CASE WHEN COALESCE(cs.shipment_count, 0) > 0 - THEN COALESCE(cr.return_count, 0)::FLOAT / cs.shipment_count - ELSE 0 END AS return_rate - - FROM fact_sales_order so - LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = true - CROSS JOIN global_production gp - CROSS JOIN global_quality gq - CROSS JOIN global_operation go - LEFT JOIN customer_shipment cs ON so.customer_id = cs.customer_id - LEFT JOIN customer_return cr ON so.customer_id = cr.customer_id -) - --- 7. 最终输出 -SELECT - sales_order_id, - sales_order_number, - customer_name, - order_date_utc, - deal_amount, - payment_status, - - -- 风险特征 - work_order_count, - ROUND(production_completion_rate::NUMERIC, 2) AS production_completion_rate, - pending_wo_count, - ROUND(qc_pass_rate::NUMERIC, 2) AS qc_pass_rate, - ROUND(operation_defect_rate::NUMERIC, 4) AS operation_defect_rate, - return_count, - ROUND(return_rate::NUMERIC, 4) AS return_rate, - - -- 延迟概率 - ROUND(( - CASE WHEN production_completion_rate < 0.3 THEN 0.30 - WHEN production_completion_rate < 0.5 THEN 0.20 - WHEN production_completion_rate < 0.8 THEN 0.10 - ELSE 0 END - + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 - WHEN qc_pass_rate < 0.9 THEN 0.15 - WHEN qc_pass_rate < 0.95 THEN 0.08 - ELSE 0 END - + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 - WHEN operation_defect_rate > 0.05 THEN 0.12 - WHEN operation_defect_rate > 0.02 THEN 0.05 - ELSE 0 END - + CASE WHEN return_rate > 0.1 THEN 0.15 - WHEN return_rate > 0.05 THEN 0.08 - WHEN return_rate > 0.02 THEN 0.03 - ELSE 0 END - + CASE WHEN payment_status = 'UNPAID' THEN 0.10 - WHEN payment_status = 'PARTIAL' THEN 0.05 - ELSE 0 END - )::NUMERIC, 2) AS delay_probability, - - -- 红/黄/绿预警 - CASE - WHEN ( - CASE WHEN production_completion_rate < 0.3 THEN 0.30 WHEN production_completion_rate < 0.5 THEN 0.20 WHEN production_completion_rate < 0.8 THEN 0.10 ELSE 0 END - + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 WHEN qc_pass_rate < 0.9 THEN 0.15 WHEN qc_pass_rate < 0.95 THEN 0.08 ELSE 0 END - + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 WHEN operation_defect_rate > 0.05 THEN 0.12 WHEN operation_defect_rate > 0.02 THEN 0.05 ELSE 0 END - + CASE WHEN return_rate > 0.1 THEN 0.15 WHEN return_rate > 0.05 THEN 0.08 WHEN return_rate > 0.02 THEN 0.03 ELSE 0 END - + CASE WHEN payment_status = 'UNPAID' THEN 0.10 WHEN payment_status = 'PARTIAL' THEN 0.05 ELSE 0 END - ) >= 0.50 THEN 'RED' - WHEN ( - CASE WHEN production_completion_rate < 0.3 THEN 0.30 WHEN production_completion_rate < 0.5 THEN 0.20 WHEN production_completion_rate < 0.8 THEN 0.10 ELSE 0 END - + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 WHEN qc_pass_rate < 0.9 THEN 0.15 WHEN qc_pass_rate < 0.95 THEN 0.08 ELSE 0 END - + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 WHEN operation_defect_rate > 0.05 THEN 0.12 WHEN operation_defect_rate > 0.02 THEN 0.05 ELSE 0 END - + CASE WHEN return_rate > 0.1 THEN 0.15 WHEN return_rate > 0.05 THEN 0.08 WHEN return_rate > 0.02 THEN 0.03 ELSE 0 END - + CASE WHEN payment_status = 'UNPAID' THEN 0.10 WHEN payment_status = 'PARTIAL' THEN 0.05 ELSE 0 END - ) >= 0.25 THEN 'YELLOW' - ELSE 'GREEN' - END AS risk_level, - - -- 风险原因 - CONCAT_WS(' | ', - CASE WHEN production_completion_rate < 0.5 THEN '生产进度滞后' END, - CASE WHEN qc_pass_rate < 0.9 THEN '质检通过率低' END, - CASE WHEN operation_defect_rate > 0.05 THEN '工序不良率高' END, - CASE WHEN return_rate > 0.05 THEN '历史退货率高' END, - CASE WHEN payment_status = 'UNPAID' THEN '未付款' END - ) AS risk_reasons - -FROM order_risk -ORDER BY delay_probability DESC, deal_amount DESC; diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/__init__.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/__init__.py deleted file mode 100644 index 3df2b44..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Utils package for lzwcai_mcp_sqlexecutor""" - -from .json_helper import load_json -from .name_helper import generate_tool_name -from .schema_helper import generate_input_schema, validate_input_schema -from .api_client import DataSourceAPIClient, get_skill_by_id, process_skill_response, test_sql_with_schema -from .env_config import get_database_id, get_datasource_id, get_skill_id, get_backend_base_url, get_env_config, set_env_variable - -__all__ = [ - 'load_json', - 'generate_tool_name', - 'generate_input_schema', - 'validate_input_schema', - 'DataSourceAPIClient', - 'get_skill_by_id', - 'process_skill_response', - 'test_sql_with_schema', - 'get_database_id', - 'get_datasource_id', - 'get_skill_id', - 'get_backend_base_url', - 'get_env_config', - 'set_env_variable' -] - diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/api_client.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/api_client.py deleted file mode 100644 index 279d800..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/api_client.py +++ /dev/null @@ -1,332 +0,0 @@ -""" -第三方API调用客户端 -用于调用外部数据源接口 -""" - -import httpx -import logging -import json -from typing import Dict, Any, Optional, List - -# 支持直接运行和模块导入两种方式 -try: - from .env_config import get_backend_base_url -except ImportError: - from env_config import get_backend_base_url - -# 获取日志记录器 -logger = logging.getLogger(__name__) - - -class DataSourceAPIClient: - """数据源API客户端""" - - def __init__( - self, - base_url: Optional[str] = None, - token: Optional[str] = None - ): - """ - 初始化API客户端 - - Args: - base_url: API基础URL（默认从环境变量 BACKEND_BASE_URL 读取，如果未设置则使用 http://192.168.2.236:8088） - token: 认证令牌（Bearer Token） - """ - # 如果没有传入 base_url，则从环境变量读取 - if base_url is None: - base_url = get_backend_base_url() - - self.base_url = base_url.rstrip('/') - self.token = token or "eyJhbGciOiJIUzUxMiJ9.eyJsb2dpbl91c2VyX2tleSI6IjJiYTk4ODllLWM2ZGItNDQ5YS1iZmFjLTQ2YzMxODFlODg5NCJ9.dvi8zm0LsWvJ_h9zD5blnHFRxa4z4_WBm1R487ekE7HlHzrN6dnvqhK8askqT5b1EcE8myHwRzLVMoI8UOjOrw" - self.client = httpx.Client(timeout=30.0) - - def _get_headers(self) -> Dict[str, str]: - """ - 获取请求头 - - Returns: - 请求头字典 - """ - return { - 'Authorization': f'Bearer {self.token}', - } - - def get_skill_by_id(self, skill_id: str) -> Dict[str, Any]: - """ - 根据技能ID获取技能信息 - - Args: - skill_id: 技能ID - - Returns: - API响应数据 - - Raises: - Exception: 请求失败时抛出 - """ - try: - url = f"{self.base_url}/datasource/skill/getBySkillId/{skill_id}" - - logger.info(f"正在调用API: {url}") - logger.info(f"请求参数 - skill_id: {skill_id}") - - response = self.client.get( - url, - headers=self._get_headers() - ) - - # 检查HTTP状态码 - response.raise_for_status() - - # 解析JSON响应 - data = response.json() - - logger.info(f"API调用成功: {url}") - logger.debug(f"响应数据: {data}") - - return data - - except httpx.TimeoutException: - error_msg = f"API请求超时: {url}" - logger.error(error_msg) - raise Exception(error_msg) - - except httpx.HTTPStatusError as e: - error_msg = f"API请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(error_msg) - logger.error(f"错误响应: {e.response.text}") - raise Exception(error_msg) - - except httpx.RequestError as e: - error_msg = f"API请求异常: {url}, 错误: {str(e)}" - logger.error(error_msg) - raise Exception(error_msg) - - except Exception as e: - error_msg = f"处理API响应时出错: {str(e)}" - logger.error(error_msg, exc_info=True) - raise Exception(error_msg) - - def test_sql_with_schema(self, request_data: Dict[str, Any]) -> Dict[str, Any]: - """ - 测试SQL语句并返回执行结果 - - Args: - request_data: 请求数据，包含以下字段： - - datasourceId: 数据源ID - - businessName: 业务名称 - - businessDescription: 业务描述 - - sqlTemplate: SQL模板 - - parameters: 参数定义 - - testParams: 测试参数 - - Returns: - API响应数据 - - Raises: - Exception: 请求失败时抛出 - """ - try: - url = f"{self.base_url}/datasource/sqlExecutionLog/testBatchSqlWithSchema" - - # 构建请求头（包含Content-Type） - headers = self._get_headers() - headers['Content-Type'] = 'application/json' - headers['Accept'] = '*/*' - - logger.info(f"正在调用测试SQL API: {url}") - logger.info(f"请求参数: {json.dumps(request_data, ensure_ascii=False, indent=2)}") - - # 发送POST请求 - response = self.client.post( - url, - headers=headers, - json=request_data - ) - - # 检查HTTP状态码 - response.raise_for_status() - - # 解析JSON响应 - result = response.json() - - logger.info(f"测试SQL API调用成功") - logger.debug(f"响应数据: {json.dumps(result, ensure_ascii=False, indent=2)}") - - # 处理返回数据结构: {code, data: [{errorMessage, data}, ...], msg} - # 检查外层 code - if result.get("code") != 200: - error_msg = result.get("msg", "接口返回错误") - logger.error(f"接口返回错误: code={result.get('code')}, msg={error_msg}") - raise Exception(error_msg) - - # data 是一个数组，每个元素包含 errorMessage 和 data - data_list = result.get("data", []) - - # 如果不是数组，兼容旧格式 - if isinstance(data_list, dict): - if data_list.get("errorMessage"): - error_msg = data_list.get("errorMessage") - logger.error(f"接口业务错误: {error_msg}") - raise Exception(error_msg) - return data_list.get("data") - - # 处理数组格式，提取每个 item 的 data 并组合 - combined_data = [] - for item in data_list: - if item.get("errorMessage"): - error_msg = item.get("errorMessage") - logger.error(f"接口业务错误: {error_msg}") - raise Exception(error_msg) - item_data = item.get("data", []) - combined_data.append(item_data) - - # 返回组合后的数据 [data1, data2, ...] - return combined_data - - except httpx.TimeoutException: - error_msg = f"测试SQL API请求超时: {url}" - logger.error(error_msg) - raise Exception(error_msg) - - except httpx.HTTPStatusError as e: - error_msg = f"测试SQL API请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(error_msg) - logger.error(f"错误响应: {e.response.text}") - raise Exception(error_msg) - - except httpx.RequestError as e: - error_msg = f"测试SQL API请求异常: {url}, 错误: {str(e)}" - logger.error(error_msg) - raise Exception(error_msg) - - except Exception as e: - error_msg = f"处理测试SQL API响应时出错: {str(e)}" - logger.error(error_msg, exc_info=True) - raise Exception(error_msg) - - def close(self): - """关闭HTTP客户端""" - self.client.close() - - -# 创建默认客户端实例 -default_client = DataSourceAPIClient() - - -def get_skill_by_id(skill_id: str, base_url: Optional[str] = None, token: Optional[str] = None) -> Dict[str, Any]: - """ - 便捷函数：根据技能ID获取技能信息 - - Args: - skill_id: 技能ID - base_url: API基础URL（可选，默认从环境变量 BACKEND_BASE_URL 读取） - token: 认证令牌（可选，使用默认值） - - Returns: - API响应数据 - """ - if base_url or token: - client = DataSourceAPIClient( - base_url=base_url, - token=token - ) - return client.get_skill_by_id(skill_id) - else: - return default_client.get_skill_by_id(skill_id) - - -def test_sql_with_schema(request_data: Dict[str, Any], base_url: Optional[str] = None, token: Optional[str] = None) -> Dict[str, Any]: - """ - 便捷函数：测试SQL语句并返回执行结果 - - Args: - request_data: 请求数据，包含以下字段： - - datasourceId: 数据源ID - - businessName: 业务名称 - - businessDescription: 业务描述 - - sqlTemplate: SQL模板 - - parameters: 参数定义 - - testParams: 测试参数 - base_url: API基础URL（可选，默认从环境变量 BACKEND_BASE_URL 读取） - token: 认证令牌（可选，使用默认值） - - Returns: - API响应数据 - """ - if base_url or token: - client = DataSourceAPIClient( - base_url=base_url, - token=token - ) - return client.test_sql_with_schema(request_data) - else: - return default_client.test_sql_with_schema(request_data) - - -def process_skill_response(response: Dict[str, Any]) -> List[Dict[str, Any]]: - """ - 处理API响应数据，映射为businessQueries格式 - - Args: - response: API原始响应数据 - - Returns: - 处理后的查询配置列表 - """ - try: - # 提取data数组 - data_list = response.get("data", []) - - # 默认的员工ID参数schema - default_employee_schema = { - "type": "object", - "required": ["employeeId"], - "properties": { - "employeeId": { - "type": "number", - "description": "员工ID，用于标识员工的唯一数字标识符", - "examples": [1001, 2002] - } - } - } - - # 映射每个skill为businessQuery格式 - queries = [] - for skill in data_list: - # 解析sqlParams字符串为JSON对象 - sql_params = json.loads(skill.get("sqlParams", "{}")) - - # 判断sqlParams是否为空对象 - is_empty_params = ( - not sql_params.get("properties") or - len(sql_params.get("properties", {})) == 0 - ) and ( - not sql_params.get("required") or - len(sql_params.get("required", [])) == 0 - ) - - # 如果是空对象，使用默认的员工ID参数 - if is_empty_params: - logger.info(f"技能 {skill.get('name')} (ID: {skill.get('id')}) 的sqlParams为空，使用默认员工ID参数") - sql_params = default_employee_schema - - # 映射字段 - query = { - "id": skill.get("id"), - "businessName": skill.get("name"), - "businessDescription": skill.get("description"), - "sqlTemplate": skill.get("sqlTemplate"), - "parameters": sql_params, - "datasourceId": skill.get("datasourceId") - } - queries.append(query) - - logger.info(f"成功处理 {len(queries)} 条技能数据") - return queries - - except Exception as e: - logger.error(f"处理API响应数据失败: {e}", exc_info=True) - raise - diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/env_config.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/env_config.py deleted file mode 100644 index 585b0a6..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/env_config.py +++ /dev/null @@ -1,106 +0,0 @@ -"""环境变量配置模块""" - -import os -from typing import Optional - - -def get_database_id(default: str = "29") -> str: - """ - 获取数据库ID环境变量 - - Args: - default: 默认值（默认为 "29"） - - Returns: - str: 数据库ID - - Environment Variables: - databaseId: 数据库ID - """ - return os.environ.get("databaseId", default) - - -def get_datasource_id(default: str = "") -> str: - """ - 获取数据源ID环境变量 - - Args: - default: 默认值（默认为 ""） - - Returns: - str: 数据源ID - - Environment Variables: - datasourceId: 数据源ID - """ - return os.environ.get("datasourceId", default) - - -def get_skill_id(default: str = "") -> str: - """ - 获取技能ID环境变量 - - Args: - default: 默认值（默认为 ""） - - Returns: - str: 技能ID - - Environment Variables: - skillId: 技能ID - """ - return os.environ.get("skillId", default) - - -def get_backend_base_url(default: str = "http://lzwcai-demp-corp-manager:8086") -> str: - """ - 获取后端API基础URL环境变量 - - Args: - default: 默认值（默认为 "http://lzwcai-demp-corp-manager:8086"） - - Returns: - str: 后端API基础URL - - Environment Variables: - backendBaseUrl: 后端API基础URL - """ - return os.environ.get("backendBaseUrl", default) - - -def get_env_config() -> dict: - """ - 获取所有环境配置 - - Returns: - dict: 包含所有配置的字典 - - Example: - config = get_env_config() - print(config['database_id']) # 输出: "29" - print(config['datasource_id']) # 输出: "" - print(config['skill_id']) # 输出: "" - print(config['backend_base_url']) # 输出: "http://lzwcai-demp-corp-manager:8086" - """ - return { - "database_id": get_database_id(), - "datasource_id": get_datasource_id(), - "skill_id": get_skill_id(), - "backend_base_url": get_backend_base_url() - } - - -def set_env_variable(key: str, value: str) -> None: - """ - 设置环境变量（仅在当前进程中有效） - - Args: - key: 环境变量名 - value: 环境变量值 - - Example: - set_env_variable("databaseId", "30") - set_env_variable("skillId", "1234567890") - """ - os.environ[key] = value - diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/json_helper.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/json_helper.py deleted file mode 100644 index 1a6fc95..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/json_helper.py +++ /dev/null @@ -1,60 +0,0 @@ -"""JSON 文件读取工具""" - -import json -from pathlib import Path -from typing import Any, Union - - -def load_json(json_path: Union[str, Path]) -> Any: - """ - 读取 JSON 文件并返回其内容 - - Args: - json_path: JSON 文件的路径（支持字符串或 Path 对象） - - Returns: - JSON 文件中解析后的数据（可以是字典、列表或其他 JSON 类型） - - Raises: - FileNotFoundError: 当文件不存在时 - json.JSONDecodeError: 当 JSON 格式无效时 - Exception: 其他读取错误 - - Example: - >>> data = load_json('config.json') - >>> print(data) - {'key': 'value'} - - >>> data = load_json(Path('data/users.json')) - >>> print(data) - [{'id': 1, 'name': 'Alice'}] - """ - try: - # 转换为 Path 对象 - path = Path(json_path) - - # 检查文件是否存在 - if not path.exists(): - raise FileNotFoundError(f"JSON 文件不存在: {json_path}") - - # 检查是否为文件 - if not path.is_file(): - raise ValueError(f"路径不是一个文件: {json_path}") - - # 读取并解析 JSON 文件 - with open(path, 'r', encoding='utf-8') as f: - data = json.load(f) - - return data - - except json.JSONDecodeError as e: - raise json.JSONDecodeError( - f"JSON 格式错误: {e.msg}", - e.doc, - e.pos - ) - except FileNotFoundError: - raise - except Exception as e: - raise Exception(f"读取 JSON 文件时发生错误: {str(e)}") - diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/logger_config.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/logger_config.py deleted file mode 100644 index 290964f..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/logger_config.py +++ /dev/null @@ -1,489 +0,0 @@ -# -*- coding: utf-8 -*- -""" -统一日志配置模块 -提供系统级别的日志配置和管理 -""" - -import os -import sys -import logging -from logging.handlers import RotatingFileHandler, TimedRotatingFileHandler -from datetime import datetime -from pathlib import Path - - -class LoggerConfig: - """日志配置管理类""" - - def __init__(self, logs_dir: str = None): - """初始化日志配置 - - Args: - logs_dir: 日志目录路径，默认为项目根目录下的logs文件夹 - """ - # 确定日志目录 - if logs_dir: - self.logs_dir = Path(logs_dir) - else: - # 获取项目根目录（logger_config.py 在 utils 目录下，需要上升两层到达项目根目录） - project_root = Path(__file__).parent.parent - self.logs_dir = project_root / "logs" - - # 创建日志目录 - self.logs_dir.mkdir(exist_ok=True) - - # 日志格式 - self.log_format = '%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s' - self.date_format = '%Y-%m-%d %H:%M:%S' - - # 从环境变量获取日志级别，默认为INFO - self.log_level = self._get_log_level_from_env() - - # 是否已初始化 - self._initialized = False - - def _get_log_level_from_env(self) -> int: - """从环境变量获取日志级别 - - Returns: - int: 日志级别 - """ - log_level_str = os.getenv('LOG_LEVEL', 'INFO').upper() - - # 日志级别映射 - level_mapping = { - 'DEBUG': logging.DEBUG, - 'INFO': logging.INFO, - 'WARNING': logging.WARNING, - 'WARN': logging.WARNING, # 兼容性别名 - 'ERROR': logging.ERROR, - 'CRITICAL': logging.CRITICAL, - 'FATAL': logging.CRITICAL # 兼容性别名 - } - - return level_mapping.get(log_level_str, logging.INFO) - - def setup_logging(self, - app_name: str = "lzwcai_mcp_sqlexecutor", - log_level: int = logging.INFO, - max_file_size: int = 10 * 1024 * 1024, # 10MB - backup_count: int = 5, - console_output: bool = True) -> logging.Logger: - """设置系统日志配置 - - Args: - app_name: 应用名称，用于日志文件命名 - log_level: 日志级别 - max_file_size: 单个日志文件最大大小（字节） - backup_count: 保留的备份文件数量 - console_output: 是否输出到控制台 - - Returns: - logging.Logger: 配置好的根日志器 - """ - if self._initialized: - return logging.getLogger() - - # 设置根日志器 - root_logger = logging.getLogger() - root_logger.setLevel(log_level) - - # 清除现有的处理器 - for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) - - # 创建格式化器 - formatter = logging.Formatter(self.log_format, self.date_format) - - # 1. 主日志文件 - 按大小滚动 - main_log_file = self.logs_dir / f"{app_name}.log" - file_handler = RotatingFileHandler( - main_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - file_handler.setLevel(log_level) - file_handler.setFormatter(formatter) - root_logger.addHandler(file_handler) - - # 2. 错误日志文件 - 只记录ERROR及以上级别 - error_log_file = self.logs_dir / f"{app_name}_error.log" - error_handler = RotatingFileHandler( - error_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - error_handler.setLevel(logging.ERROR) - error_handler.setFormatter(formatter) - root_logger.addHandler(error_handler) - - # 3. 按日期滚动的日志文件 - daily_log_file = self.logs_dir / f"{app_name}_daily.log" - daily_handler = TimedRotatingFileHandler( - daily_log_file, - when='midnight', - interval=1, - backupCount=30, # 保留30天 - encoding='utf-8' - ) - daily_handler.setLevel(log_level) - daily_handler.setFormatter(formatter) - daily_handler.suffix = "%Y-%m-%d" - root_logger.addHandler(daily_handler) - - # 4. 控制台输出 - # 重要：MCP协议使用stdio时，必须将日志输出到stderr，stdout仅用于JSON-RPC通信 - if console_output: - console_handler = logging.StreamHandler(sys.stderr) - console_handler.setLevel(log_level) - console_formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s', - self.date_format - ) - console_handler.setFormatter(console_formatter) - root_logger.addHandler(console_handler) - - self._initialized = True - - # 记录初始化信息 - root_logger.info(f"日志系统初始化完成 - 日志目录: {self.logs_dir}") - root_logger.info(f"日志配置 - 级别: {logging.getLevelName(log_level)}, 文件大小限制: {max_file_size//1024//1024}MB, 备份数量: {backup_count}") - - return root_logger - - def get_module_logger(self, module_name: str) -> logging.Logger: - """获取模块专用日志器 - - Args: - module_name: 模块名称 - - Returns: - logging.Logger: 模块日志器 - """ - return logging.getLogger(module_name) - - def create_component_logger(self, - component_name: str, - log_file: str = None, - log_level: int = None) -> logging.Logger: - """为特定组件创建独立的日志器 - - Args: - component_name: 组件名称 - log_file: 独立日志文件名（可选） - log_level: 日志级别（可选） - - Returns: - logging.Logger: 组件日志器 - """ - logger = logging.getLogger(component_name) - - if log_file: - # 为组件创建独立的日志文件 - component_log_file = self.logs_dir / log_file - handler = RotatingFileHandler( - component_log_file, - maxBytes=5 * 1024 * 1024, # 5MB - backupCount=3, - encoding='utf-8' - ) - - formatter = logging.Formatter(self.log_format, self.date_format) - handler.setFormatter(formatter) - - if log_level: - handler.setLevel(log_level) - - logger.addHandler(handler) - logger.info(f"组件日志器创建完成: {component_name} -> {component_log_file}") - - return logger - - def setup_mqtt_logging(self) -> logging.Logger: - """设置MQTT专用日志 - - Returns: - logging.Logger: MQTT日志器 - """ - return self.create_component_logger( - "mqtt_communication", - "mqtt_communication.log", - logging.DEBUG - ) - - def setup_mcp_logging(self) -> logging.Logger: - """设置MCP专用日志 - - Returns: - logging.Logger: MCP日志器 - """ - return self.create_component_logger( - "mcp_services", - "mcp_services.log", - logging.DEBUG - ) - - def setup_api_logging(self) -> logging.Logger: - """设置API专用日志 - - Returns: - logging.Logger: API日志器 - """ - return self.create_component_logger( - "api_requests", - "api_requests.log", - logging.INFO - ) - - def get_logs_info(self) -> dict: - """获取日志系统信息 - - Returns: - dict: 日志系统信息 - """ - log_files = [] - if self.logs_dir.exists(): - for log_file in self.logs_dir.glob("*.log*"): - stat = log_file.stat() - log_files.append({ - "name": log_file.name, - "size": stat.st_size, - "modified": datetime.fromtimestamp(stat.st_mtime).isoformat() - }) - - return { - "logs_directory": str(self.logs_dir), - "initialized": self._initialized, - "log_files": log_files, - "total_files": len(log_files) - } - - def cleanup_old_logs(self, days: int = 30): - """清理旧日志文件 - - Args: - days: 保留天数 - """ - if not self.logs_dir.exists(): - return - - from datetime import timedelta - cutoff_time = datetime.now() - timedelta(days=days) - - cleaned_files = [] - for log_file in self.logs_dir.glob("*.log*"): - if log_file.stat().st_mtime < cutoff_time.timestamp(): - try: - log_file.unlink() - cleaned_files.append(log_file.name) - except Exception as e: - logging.error(f"清理日志文件失败: {log_file.name}, 错误: {e}") - - if cleaned_files: - logging.info(f"清理了 {len(cleaned_files)} 个旧日志文件: {cleaned_files}") - - def set_log_level(self, level: int, logger_name: str = None): - """动态调整日志级别 - - Args: - level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL) - logger_name: 指定日志器名称，None表示调整根日志器 - """ - if logger_name: - logger = logging.getLogger(logger_name) - else: - logger = logging.getLogger() - - old_level = logger.level - logger.setLevel(level) - - # 同时调整所有处理器的级别 - for handler in logger.handlers: - if not isinstance(handler, logging.StreamHandler) or handler.stream not in (sys.stdout, sys.stderr): - # 不调整控制台处理器的级别，保持原有设置 - handler.setLevel(level) - - level_name = logging.getLevelName(level) - old_level_name = logging.getLevelName(old_level) - target = logger_name or "根日志器" - - logger.info(f"日志级别已调整: {target} {old_level_name} -> {level_name}") - - def set_temporary_log_level(self, level: int, logger_name: str = None): - """临时调整日志级别（会保存原始级别用于恢复） - - Args: - level: 临时日志级别 - logger_name: 指定日志器名称，None表示调整根日志器 - """ - if not hasattr(self, '_original_levels'): - self._original_levels = {} - - target_name = logger_name or 'root' - - if logger_name: - logger = logging.getLogger(logger_name) - else: - logger = logging.getLogger() - - # 保存原始级别 - if target_name not in self._original_levels: - self._original_levels[target_name] = logger.level - - # 设置新级别 - self.set_log_level(level, logger_name) - - level_name = logging.getLevelName(level) - target = logger_name or "根日志器" - logger.info(f"临时调整日志级别: {target} -> {level_name} (可通过restore_log_level恢复)") - - def restore_log_level(self, logger_name: str = None): - """恢复日志级别到调整前的状态 - - Args: - logger_name: 指定日志器名称，None表示恢复根日志器 - """ - if not hasattr(self, '_original_levels'): - logging.warning("没有找到保存的原始日志级别") - return - - target_name = logger_name or 'root' - - if target_name not in self._original_levels: - logging.warning(f"没有找到 {target_name} 的原始日志级别") - return - - original_level = self._original_levels[target_name] - self.set_log_level(original_level, logger_name) - - # 清除保存的级别 - del self._original_levels[target_name] - - target = logger_name or "根日志器" - level_name = logging.getLevelName(original_level) - logging.info(f"已恢复日志级别: {target} -> {level_name}") - - def get_current_log_levels(self) -> dict: - """获取当前所有日志器的级别信息 - - Returns: - dict: 日志器级别信息 - """ - levels_info = {} - - # 根日志器 - root_logger = logging.getLogger() - levels_info['root'] = { - 'level': root_logger.level, - 'level_name': logging.getLevelName(root_logger.level), - 'handlers_count': len(root_logger.handlers) - } - - # 其他已创建的日志器 - for name, logger in logging.Logger.manager.loggerDict.items(): - if isinstance(logger, logging.Logger): - levels_info[name] = { - 'level': logger.level, - 'level_name': logging.getLevelName(logger.level), - 'handlers_count': len(logger.handlers) - } - - return levels_info - - -# 全局日志配置实例 -logger_config = LoggerConfig() - - -def setup_system_logging(app_name: str = "lzwcai_mcp_sqlexecutor", - log_level: int = logging.INFO) -> logging.Logger: - """系统日志初始化快捷函数 - - Args: - app_name: 应用名称 - log_level: 日志级别 - - Returns: - logging.Logger: 根日志器 - """ - return logger_config.setup_logging(app_name, log_level) - - -def get_logger(name: str) -> logging.Logger: - """获取日志器的快捷函数 - - Args: - name: 日志器名称 - - Returns: - logging.Logger: 日志器实例 - """ - return logger_config.get_module_logger(name) - - -def set_log_level(level: int, logger_name: str = None): - """动态调整日志级别的快捷函数 - - Args: - level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL) - logger_name: 指定日志器名称，None表示调整根日志器 - - Examples: - # 调整根日志器为DEBUG级别 - set_log_level(logging.DEBUG) - - # 调整特定模块日志器为WARNING级别 - set_log_level(logging.WARNING, "agent_ontology.core") - """ - logger_config.set_log_level(level, logger_name) - - -def set_temporary_log_level(level: int, logger_name: str = None): - """临时调整日志级别的快捷函数 - - Args: - level: 临时日志级别 - logger_name: 指定日志器名称，None表示调整根日志器 - - Examples: - # 临时调整为DEBUG级别进行调试 - set_temporary_log_level(logging.DEBUG) - # ... 进行调试 ... - # 恢复原始级别 - restore_log_level() - """ - logger_config.set_temporary_log_level(level, logger_name) - - -def restore_log_level(logger_name: str = None): - """恢复日志级别的快捷函数 - - Args: - logger_name: 指定日志器名称，None表示恢复根日志器 - """ - logger_config.restore_log_level(logger_name) - - -def get_current_log_levels() -> dict: - """获取当前日志级别信息的快捷函数 - - Returns: - dict: 日志器级别信息 - - Examples: - levels = get_current_log_levels() - print(f"根日志器级别: {levels['root']['level_name']}") - """ - return logger_config.get_current_log_levels() - - -# 便捷的日志级别常量 -class LogLevel: - """日志级别常量类""" - DEBUG = logging.DEBUG - INFO = logging.INFO - WARNING = logging.WARNING - ERROR = logging.ERROR - CRITICAL = logging.CRITICAL \ No newline at end of file diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/name_helper.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/name_helper.py deleted file mode 100644 index d66cf32..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/name_helper.py +++ /dev/null @@ -1,41 +0,0 @@ -# -*- coding: utf-8 -*- -""" -名称生成工具模块 -""" - -from pypinyin import lazy_pinyin, Style -import logging - -logger = logging.getLogger(__name__) - - -def generate_tool_name(business_name: str, tool_id: str) -> str: - """ - 根据业务名称和ID生成工具名称 - 格式: tool_拼音_id - - Args: - business_name: 业务名称（中文） - tool_id: 工具ID - - Returns: - str: 格式化的工具名称 - """ - try: - # 将中文转换为拼音（无音调，小写） - pinyin_list = lazy_pinyin(business_name, style=Style.NORMAL) - # 拼接拼音 - pinyin_str = ''.join(pinyin_list) - - # 将 ID 中的 '-' 替换为 '_' - formatted_id = tool_id.replace('-', '_') - - # 组合成最终的工具名称 - tool_name = f"tool_{pinyin_str}_{formatted_id}" - - return tool_name - except Exception as e: - logger.error(f"生成工具名称失败: {business_name}, {tool_id}, 错误: {e}", exc_info=True) - # 降级处理：如果拼音转换失败，使用 ID - return f"tool_{tool_id.replace('-', '_')}" - diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/schema_helper.py b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/schema_helper.py deleted file mode 100644 index 626559f..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/utils/schema_helper.py +++ /dev/null @@ -1,166 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Schema 生成工具模块 -""" - -from typing import Any, Dict, List - - -def generate_input_schema(parameters: Dict[str, Any]) -> Dict[str, Any]: - """ - 从查询配置的参数定义生成 MCP 工具的 inputSchema - - 此函数会保留完整的 JSON Schema 信息，包括： - - type: Schema 类型（通常是 "object"） - - required: 必填字段列表 - - properties: 属性定义（包括每个属性的 type, description, format, examples 等） - - description: Schema 的整体描述（如果有） - - 以及其他任何 JSON Schema 标准字段 - - 此函数还会自动添加以下字段（如果原始 parameters 中未定义）： - - targetDatabaseName: 目标数据库名称（非必填，默认为空字符串） - - Args: - parameters: 查询配置中的参数定义字典，应该是一个完整的 JSON Schema 对象 - - Returns: - Dict[str, Any]: 符合 JSON Schema 规范的 inputSchema 对象 - - Example: - >>> params = { - ... "type": "object", - ... "required": ["userId", "startTime"], - ... "properties": { - ... "userId": { - ... "type": "integer", - ... "description": "用户的唯一标识符", - ... "examples": [10086] - ... }, - ... "startTime": { - ... "type": "string", - ... "format": "date-time", - ... "description": "查询的起始时间", - ... "examples": ["2023-01-01 00:00:00"] - ... } - ... } - ... } - >>> schema = generate_input_schema(params) - >>> # schema 将包含所有原始信息，包括 format 和 examples - >>> # 同时会自动添加 targetDatabaseName 字段 - """ - # 如果 parameters 本身就是一个完整的 JSON Schema 对象，直接使用 - # 但确保至少包含 type 和 properties - if not parameters: - # 如果 parameters 为空，返回一个空的 object schema - return { - "type": "object", - "properties": {}, - "required": [] - } - - # 检查 parameters 是否已经是完整的 JSON Schema 格式 - # 完整格式应该有 "type": "object" 和 "properties" 字段 - if "type" in parameters and parameters.get("type") == "object" and "properties" in parameters: - # 已经是完整的 JSON Schema 格式，直接使用 - input_schema = dict(parameters) - else: - # parameters 是简化格式（直接是参数定义），需要转换为 JSON Schema 格式 - # 例如: {"workOrderNumber": {"type": "string", "description": "...", "required": true}} - properties = {} - required = [] - - for param_name, param_def in parameters.items(): - if isinstance(param_def, dict): - # 提取 required 标记 - if param_def.get("required", False): - required.append(param_name) - - # 复制参数定义，但移除 required 字段（它应该在顶层的 required 数组中） - prop_def = {k: v for k, v in param_def.items() if k != "required"} - properties[param_name] = prop_def - - input_schema = { - "type": "object", - "properties": properties, - "required": required - } - - # 确保必需的字段存在 - if "type" not in input_schema: - input_schema["type"] = "object" - - if "properties" not in input_schema: - input_schema["properties"] = {} - - if "required" not in input_schema: - input_schema["required"] = [] - - # 添加 targetDatabaseName 字段（如果不存在） - if "targetDatabaseName" not in input_schema["properties"]: - input_schema["properties"]["targetDatabaseName"] = { - "type": "string", - "description": "目标数据库名称", - "default": "" - } - - # 保留所有其他字段，如 description, examples, format 等 - # JSON Schema 标准支持的字段都会被保留： - # - additionalProperties - # - patternProperties - # - minProperties / maxProperties - # - dependencies - # - 等等 - - return input_schema - - -def validate_input_schema(schema: Dict[str, Any]) -> tuple[bool, str]: - """ - 验证 inputSchema 是否符合基本的 JSON Schema 规范 - - Args: - schema: 要验证的 schema 对象 - - Returns: - tuple[bool, str]: (是否有效, 错误消息或成功消息) - - Example: - >>> schema = {"type": "object", "properties": {"id": {"type": "string"}}} - >>> is_valid, msg = validate_input_schema(schema) - >>> print(is_valid, msg) - True, "Schema 验证通过" - """ - if not isinstance(schema, dict): - return False, "Schema 必须是一个字典对象" - - if schema.get("type") != "object": - return False, "Schema 的 type 字段必须是 'object'" - - if "properties" not in schema: - return False, "Schema 必须包含 properties 字段" - - if not isinstance(schema.get("properties"), dict): - return False, "Schema 的 properties 字段必须是一个字典对象" - - # 验证 required 字段（如果存在） - if "required" in schema: - required = schema["required"] - if not isinstance(required, list): - return False, "Schema 的 required 字段必须是一个列表" - - # 验证所有 required 的字段都在 properties 中定义 - properties = schema["properties"] - for field in required: - if field not in properties: - return False, f"必填字段 '{field}' 未在 properties 中定义" - 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-## 需求概述 - -本模型基于 `module/` 目录下 52 份业务导出 Excel，面向制造企业端到端经营与生产分析（线索→商机→合同→订单→采购/生产→入出库→质检→开票/收付款/核销→资金流→分佣），抽象形成可扩展的分析型数据模型。 - -设计目标： - -- 兼容 ISA-95 / IEC 62264：区分 Level 3（生产执行）与 Level 4（经营管理）数据边界 -- 分析型优先：星型模型 + 事实星座（Conformed Dimensions） -- 主数据一致性：维度表按 SCD Type 2 管理（`RowValidFrom/RowValidTo/IsCurrent`） -- 事务可追溯：事实表以 `EventTimeUTC` 作为事件时间分区/水印字段 - -范围说明： - -- 当前 Excel 以“编号/名称”作为主要业务键（例如 `产品编码`、`合同编号`、`工单编号`）。分析层统一引入代理主键 `{Entity}Id`。 -- 多数单据缺少“行明细”粒度（例如销售订单/采购订单仅见单头字段），模型保留单头事实为主，后续如补充明细可平滑扩展。 - -## 术语表 - -| 术语 | 含义 | -|---|---| -| 业务键（Business Key） | 来自源系统的编码/编号/单号等唯一标识，如 `ProductCode`、`ContractNumber` | -| 代理键（Surrogate Key） | 分析层自增/雪花主键，如 `ProductId` | -| SCD Type 2 | 维度历史保留：通过 `RowValidFrom/RowValidTo/IsCurrent` 记录有效期 | -| 退化维度（Degenerate Dimension） | 直接保存在事实表中的单据号、状态等维度属性 | -| EventTimeUTC | 统一事件时间（UTC），作为事实表分区/增量水印字段 | - -## 主题域总览（ISA-95 视角） - -| 主题域 | ISA-95 层级 | 覆盖 Excel | -|---|---|---| -| 主数据（产品/BOM/工艺/资源） | L3/L4 共用 | 产品列表、物料清单、工艺路线、工序、仓库、供应商、客户、设备、质检原因 | -| 生产执行 | Level 3 | 工单、任务、报工、装配工单 | -| 质量 | Level 3 | 质检记录、质检不通过原因 | -| 库存与仓储作业 | Level 3 | 出入库明细、盘点、调拨、其他出入库、报废 | -| 销售与履约 | Level 4 | 销售报价、销售订单、销售出库、销售退货 | -| 采购与供应 | Level 4 | 采购申请、采购订单、采购入库、采购退货 | -| 财务与资金 | Level 4 | 发票、回款、收款/付款、预收/预付、核销、资金流向、资金转账、其他收支、分佣 | -| CRM（市场到合同） | Level 4 | 线索、商机、合同 | - -## 实体清单（维度/事实） - -维度（SCD2）： - -- `DimPerson`（人员维度） -- `DimProduct`（产品维度） -- `DimCustomer`（客户维度） -- `DimSupplier`（供应商维度） -- `DimWarehouse`（仓库维度） -- `DimBom`（物料清单维度） -- `DimRouting`（工艺路线维度） -- `DimOperation`（工序维度） -- `DimEquipment`（设备维度） -- `DimQcReason`（质检不通过原因维度） -- `DimContract`（合同维度，可选：合同也可作为事实） - -事实（按 `EventTimeUTC` 分区）： - -- `FactInventoryMovement`（出入库流水事实） -- `FactWorkOrder`（工单事实） -- `FactOperationTask`（工序任务事实） -- `FactLaborReport`（报工事实） -- `FactQualityInspection`（质检事实） -- `FactSalesOrder`（销售订单事实） -- `FactSalesShipment`（销售出库事实） -- `FactSalesReturn`（销售退货事实） -- `FactPurchaseRequest`（采购申请事实） -- `FactPurchaseOrder`（采购订单事实） -- `FactPurchaseReceipt`（采购入库事实） -- `FactPurchaseReturn`（采购退货事实） -- `FactInventoryTransfer`（库存调拨事实） -- `FactInventoryCount`（库存盘点事实） -- `FactScrap`（报废事实） -- `FactArReceipt`（应收收款事实） -- `FactApPayment`（应付付款事实） -- `FactInvoice`（开票申请事实） -- `FactWriteOff`（核销事实） -- `FactCashFlow`（资金流水事实） -- `FactCommission`（分佣事实） -- `FactLead`（线索事实） -- `FactOpportunity`（商机事实） -- `FactProductionPlan`（生产计划/装配工单汇总事实，可选） - -## ER 图（事实星座） - -```mermaid -erDiagram - DimProduct ||--o{ FactInventoryMovement : "by_product (关联产品)" - DimWarehouse ||--o{ FactInventoryMovement : "by_warehouse (关联仓库)" - DimBom ||--o{ FactInventoryMovement : "by_bom (关联BOM)" - DimPerson ||--o{ FactInventoryMovement : "operated_by (操作人)" - - DimProduct ||--o{ FactWorkOrder : "produces (生产产品)" - DimSupplier ||--o{ FactWorkOrder : "outsourced_to (委外供应商)" - DimRouting ||--o{ FactWorkOrder : "uses_routing (使用工艺)" - DimPerson ||--o{ FactWorkOrder : "managed_by (生产主管)" - - DimOperation ||--o{ FactOperationTask : "executes (执行工序)" - DimProduct ||--o{ FactOperationTask : "for_product (关联产品)" - DimPerson ||--o{ FactOperationTask : "worker (执行工人)" - - DimProduct ||--o{ FactLaborReport : "reports_on (报工产品)" - DimOperation ||--o{ FactLaborReport : "at_operation (报工工序)" - DimPerson ||--o{ FactLaborReport : "reported_by (报工人员)" - - DimQcReason ||--o{ FactQualityInspection : "fail_reason (不合格原因)" - DimProduct ||--o{ FactQualityInspection : "inspects (质检产品)" - DimPerson ||--o{ FactQualityInspection : "inspected_by (质检员)" - - DimCustomer ||--o{ FactSalesOrder : "buys (购买)" - DimPerson ||--o{ FactSalesOrder : "sold_by (销售员)" - DimCustomer ||--o{ FactSalesShipment : "receives (收货)" - DimPerson ||--o{ FactSalesShipment : "shipped_by (发货员)" - DimCustomer ||--o{ FactSalesReturn : "returns (退货)" - DimPerson ||--o{ FactSalesReturn : "handled_by (处理人)" - - DimSupplier ||--o{ FactPurchaseOrder : "supplies (供应)" - DimPerson ||--o{ FactPurchaseOrder : "purchased_by (采购员)" - DimSupplier ||--o{ FactPurchaseReceipt : "delivers (送货)" - DimPerson ||--o{ FactPurchaseReceipt : "received_by (收货员)" - DimSupplier ||--o{ FactPurchaseReturn : "takes_back (退回)" - - DimContract ||--o{ FactInvoice : "invoiced_for (开票合同)" - DimContract ||--o{ FactArReceipt : "cash_in (收款合同)" - DimContract ||--o{ FactApPayment : "cash_out (付款合同)" - DimContract ||--o{ FactWriteOff : "writeoff (核销合同)" - DimPerson ||--o{ FactContract : "owned_by (合同负责人)" - DimPerson ||--o{ FactLead : "owned_by (线索负责人)" - DimPerson ||--o{ FactOpportunity : "owned_by (商机负责人)" -``` - -## 业务数据流转全景图 - -本流程图展示了从商机线索到最终产品交付的端到端数据流转过程，以及各环节涉及的关键数据实体。 - -```mermaid -flowchart TD - %% 节点样式定义 - classDef process fill:#e1f5fe,stroke:#01579b,stroke-width:2px; - classDef decision fill:#fff9c4,stroke:#fbc02d,stroke-width:2px; - classDef table fill:#f3e5f5,stroke:#7b1fa2,stroke-width:1px,stroke-dasharray: 5 5; - - %% 1. CRM阶段 - subgraph Stage_CRM [CRM与销售阶段] - direction TB - Node_Lead[("线索处理
In: FactLead
Logic: 线索清洗/转化")]:::process - Node_Opp[("商机确认
In: FactLead
Out: FactOpportunity")]:::process - Decision_Win{商机赢单?}:::decision - Node_Contract[("合同签订
In: FactOpportunity
Out: DimContract")]:::process - Node_SO[("销售订单生成
In: DimContract
Out: FactSalesOrder")]:::process - - Table_Lead[("FactLead")]:::table - Table_Opp[("FactOpportunity")]:::table - Table_SO[("FactSalesOrder")]:::table - end - - %% 2. 计划阶段 - subgraph Stage_Plan [计划与排程阶段] - direction TB - Node_MPS[("主生产计划(MPS)
In: FactSalesOrder
Logic: 需求合并/产能平衡
Out: FactProductionPlan")]:::process - Node_MRP[("MRP运算/BOM展开
In: FactProductionPlan, DimBom
Logic: 净需求计算
Out: FactWorkOrder(Planned)")]:::process - Decision_MakeBuy{自制/外购?}:::decision - Node_PR[("采购申请生成
Out: FactPurchaseRequest")]:::process - - Table_Plan[("FactProductionPlan")]:::table - Table_Bom[("DimBom")]:::table - Table_Stock[("FactInventoryMovement
(当前库存)")]:::table - end - - %% 新增：3. 采购与供应阶段 - subgraph Stage_Procure [采购与供应阶段] - direction TB - Node_PO[("采购订单(PO)
In: FactPurchaseRequest
Out: FactPurchaseOrder")]:::process - Node_Receive[("收货与IQC
Logic: 验收/质检
Out: FactQualityInspection")]:::process - Decision_IQC{IQC合格?}:::decision - Node_Return[("采购退货
Out: FactPurchaseReturn")]:::process - Node_Mat_In[("原材料入库
Logic: 批次录入/更新库存
Out: FactPurchaseReceipt
FactInventoryMovement")]:::process - Node_AP[("应付挂账(AP)
Logic: 三单匹配
Out: FactApPayment")]:::process - - Table_PO[("FactPurchaseOrder")]:::table - Table_Receipt[("FactPurchaseReceipt")]:::table - end - - %% 4. 生产执行阶段 (原 Stage_Make) - subgraph Stage_Make [生产执行阶段] - direction TB - Node_WO_Release[("工单下达
In: FactWorkOrder(Planned)
Logic: 物料齐套/锁定资源
Out: FactWorkOrder(Released)")]:::process - Node_Task[("工序任务派工
In: FactWorkOrder, DimRouting
Out: FactOperationTask")]:::process - Node_Labor[("生产报工
In: FactOperationTask
Logic: 投入/产出/工时记录
Out: FactLaborReport")]:::process - - Table_WO[("FactWorkOrder")]:::table - Table_Task[("FactOperationTask")]:::table - Table_Labor[("FactLaborReport")]:::table - end - - %% 5. 质量与入库阶段 (原 Stage_QC) - subgraph Stage_QC [质量与仓储阶段] - direction TB - Node_QC[("成品检验(FQC)
In: FactLaborReport
Logic: 检验标准比对
Out: FactQualityInspection")]:::process - Decision_Pass{质检合格?}:::decision - Node_Scrap[("报废处理
Logic: 记录不良原因
Out: FactScrap")]:::process - Node_FG_In[("成品入库
In: FactWorkOrder(Closed)
Logic: 更新库存余额
Out: FactInventoryMovement(IN)")]:::process - - Table_QC[("FactQualityInspection")]:::table - Table_Inv[("FactInventoryMovement")]:::table - end - - %% 6. 交付阶段 (原 Stage_Deliver) - subgraph Stage_Deliver [交付阶段] - direction TB - Node_Ship[("销售发货
In: FactSalesOrder, FactInventoryMovement
Logic: 拣货/出库扣减
Out: FactSalesShipment")]:::process - Node_End((交付完成)) - - Table_Ship[("FactSalesShipment")]:::table - end - - %% 新增：7. 财务与资金阶段 - subgraph Stage_Finance [财务与资金阶段] - direction TB - %% AR Flow - Node_Invoice[("销售开票
In: FactSalesShipment
Out: FactInvoice")]:::process - Node_ArRec[("销售收款
Out: FactArReceipt")]:::process - Node_ArOff[("应收核销
Logic: 核销发票与收款
Out: FactWriteOff")]:::process - - %% AP Flow - Node_ApPay[("采购付款
Out: FactApPayment")]:::process - Node_ApOff[("应付核销
Logic: 核销应付与付款
Out: FactWriteOff")]:::process - - %% Cash Flow - Node_Cash[("资金流水
Logic: 账户变动记录
Out: FactCashFlow")]:::process - - Table_Inv[("FactInvoice")]:::table - Table_Ar[("FactArReceipt")]:::table - Table_Ap[("FactApPayment")]:::table - Table_Cash[("FactCashFlow")]:::table - end - - %% 关系连线 - Node_Lead --> Node_Opp - Node_Opp --> Decision_Win - Decision_Win -- Yes --> Node_Contract - Node_Contract --> Node_SO - Node_SO --> Node_MPS - - Node_MPS --> Node_MRP - Node_MRP --> Decision_MakeBuy - Decision_MakeBuy -- 外购 --> Node_PR - Decision_MakeBuy -- 自制 --> Node_WO_Release - - %% 采购流程连线 - Node_PR --> Node_PO - Node_PO --> Node_Receive - Node_Receive --> Decision_IQC - Decision_IQC -- No --> Node_Return - Decision_IQC -- Yes --> Node_Mat_In - Node_Mat_In --> Node_AP - Node_Mat_In --> Node_WO_Release - - Node_WO_Release --> Node_Task - Node_Task --> Node_Labor - Node_Labor --> Node_QC - - Node_QC --> Decision_Pass - Decision_Pass -- No --> Node_Scrap - Decision_Pass -- Yes --> Node_FG_In - - Node_FG_In --> Node_Ship - Node_Ship --> Node_End - - %% 财务流程连线 - Node_Ship --> Node_Invoice - Node_Invoice --> Node_ArRec - Node_ArRec --> Node_ArOff - - Node_AP --> Node_ApPay - Node_ApPay --> Node_ApOff - - Node_ArRec --> Node_Cash - Node_ApPay --> Node_Cash - - %% 数据表关联（虚线） - Table_Lead -.-> Node_Lead - Node_Opp -.-> Table_Opp - Node_Contract -.-> Table_SO - Table_SO -.-> Node_MPS - Node_MPS -.-> Table_Plan - Table_Plan -.-> Node_MRP - Table_Bom -.-> Node_MRP - Table_Stock -.-> Node_MRP - - Node_PR -.-> Table_PO - Node_PO -.-> Table_PO - Node_Mat_In -.-> Table_Receipt - Node_Mat_In -.-> Table_Inv - - Node_WO_Release -.-> Table_WO - Node_Task -.-> Table_Task - Node_Labor -.-> Table_Labor - Node_QC -.-> Table_QC - Node_Receive -.-> Table_QC - Node_FG_In -.-> Table_Inv - Node_Ship -.-> Table_Ship - - Node_Invoice -.-> Table_Inv - Node_ArRec -.-> Table_Ar - Node_ApPay -.-> Table_Ap - Node_Cash -.-> Table_Cash -``` - -### 关键业务规则说明 - -1. **商机转订单**： - * 当 `FactOpportunity` 状态确认为“赢单”且 `DimContract` 签订后，系统生成 `FactSalesOrder`。 - * 此时确立销售对象（Customer）、产品（Product）及交付日期（DeliveryDate）。 - -2. **计划运算 (MPS/MRP)**： - * `FactProductionPlan` 汇总销售订单需求，结合 `FactInventoryMovement` 计算出的当前库存快照，进行净需求计算。 - * 利用 `DimBom` 展开物料清单，区分自制件（生成 `FactWorkOrder`）与采购件（生成 `FactPurchaseRequest`）。 - -3. **采购闭环管理**： - * **采购订单 (PO)**：`FactPurchaseOrder` 经审批后生效，明确供应商、价格、交期。 - * **入库验收 (IQC)**：物料到达后必须进行 IQC（`FactQualityInspection`），合格品方可办理入库（`FactPurchaseReceipt`）并增加库存（`FactInventoryMovement`）。 - * **财务对接 (AP)**：系统自动匹配 PO（采购单）、Receipt（入库单）和供应商发票，生成应付账款（`FactApPayment`），确保“三单一致”。 - * **供应商管理**：IQC 合格率与交期准确率回写至 `DimSupplier`，用于季度评估与优选名录维护。 - -4. **生产执行闭环**： - * 工单下达前需校验物料齐套性（依赖采购入库的 `FactInventoryMovement`）。 - * 现场工人通过 `FactLaborReport` 上报实际投入工时与产出数量，该数据是成本核算的核心依据。 - -5. **质量控制 (FQC)**： - * `FactQualityInspection` 记录成品检验结果。 - * 质检合格触发成品入库，不合格则根据原因（`DimQcReason`）触发返工或报废（`FactScrap`）。 - -6. **库存与交付**： - * 所有实物库存变动（入库、出库、报废）统一写入 `FactInventoryMovement`，确保库存台账的一致性。 - * `FactSalesShipment` 记录发货动作，作为收入确认（Revenue Recognition）的前置事件。 - -7. **财务资金流转**： - * **应收 (AR)**：销售发货后生成发票申请（`FactInvoice`），回款后（`FactArReceipt`）进行核销（`FactWriteOff`）。 - * **应付 (AP)**：采购入库确认应付后（`FactApPayment` 中的挂账状态），执行付款（`FactApPayment` 中的实付）并核销。 - * **资金 (Cash)**：所有收付款动作最终汇总至 `FactCashFlow`，提供实时的资金日记账分析。 - -## 表结构详设 - -类型说明（可按目标数据库微调）： - -- `bigint`：代理主键/外键 -- `varchar(n)`：业务键、名称、状态等 -- `decimal(18,4)`：数量与金额（金额可按需要改 `decimal(18,2)`） -- `datetime2`：统一时间字段（UTC） - -### DimPerson（人员维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PersonId | bigint | N | autoincr | 主键 | | - | PersonCode | varchar(64) | N | | 人员编码（业务键） | 来源：用户ID/工号 | - | PersonName | varchar(255) | N | | 姓名 | | - | Department | varchar(100) | Y | | 部门 | | - | Position | varchar(100) | Y | | 职位/岗位 | | - | Phone | varchar(32) | Y | | 电话 | | - | Email | varchar(100) | Y | | 邮箱 | | - | Status | varchar(20) | Y | | 状态 | ACTIVE, INACTIVE | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | -| Birthday | date | Y | | 出生日期 | | -| Employment_day | date | Y | | 入职日期 | | -| Job_description | varchar(255) | Y | | 职务描述 | | - -### DimProduct（产品维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ProductId | bigint | N | autoincr | 主键 | | - | ProductCode | varchar(64) | N | | 产品编码（业务键） | 来源：产品列表.产品编码 | - | ProductName | varchar(255) | N | | 产品名称 | | - | ProductSpec | varchar(255) | Y | | 产品规格 | | - | ProductCategory | varchar(100) | Y | | 产品类别 | | - | ProductType | varchar(100) | Y | | 产品类型 | | - | UomCode | varchar(16) | Y | | 主单位 | kg,pcs,L,m | - | Brand | varchar(100) | Y | | 品牌 | | - | Origin | varchar(100) | Y | | 产地 | | - | TransportCondition | varchar(100) | Y | | 运输条件 | | - | Barcode | varchar(64) | Y | | 条形码 | | - | DrawingNumber | varchar(64) | Y | | 图号 | | - | Material | varchar(100) | Y | | 材料 | | - | SurfaceTreatment | varchar(100) | Y | | 表面处理 | | - | MinStockQty | decimal(18,4) | Y | | 最低库存数量 | | - | SafetyStockQty | decimal(18,4) | Y | | 安全库存数量 | | - | MaxStockQty | decimal(18,4) | Y | | 最高库存数量 | | - | SourceSystem | varchar(50) | Y | | 数据来源 | 来源：数据来源 | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimCustomer（客户维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| CustomerId | bigint | N | autoincr | 主键 | | - | CustomerName | varchar(255) | N | | 客户名称（业务键） | 来源：客户列表.客户名称 | - | CustomerSource | varchar(100) | Y | | 客户来源 | | - | Phone | varchar(32) | Y | | 手机 | | - | Telephone | varchar(32) | Y | | 电话 | | - | Email | varchar(100) | Y | | 邮箱 | | - | CustomerLevel | varchar(50) | Y | | 客户级别 | | - | Industry | varchar(100) | Y | | 客户行业 | | - | Address | varchar(500) | Y | | 详细地址 | | - | IsLocked | bit | Y | | 锁定状态 | 0,1 | - | DealStatus | varchar(50) | Y | | 成交状态 | | - | LastFollowUpTimeUTC | datetime2 | Y | | 最后跟进时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimSupplier（供应商维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| SupplierId | bigint | N | autoincr | 主键 | | - | SupplierCode | varchar(64) | N | | 供应商编码（业务键） | 来源：供应商列表.供应商编码 | - | SupplierName | varchar(255) | N | | 供应商名称 | | - | SupplierCategory | varchar(100) | Y | | 供应商分类 | | - | SupplierLevel | varchar(50) | Y | | 供应商级别 | | - | Address | varchar(500) | Y | | 地址 | | - | DefaultCurrencyCode | varchar(16) | Y | | 默认币别 | CNY,USD,EUR | - | SettlementTerm | varchar(50) | Y | | 结算期限 | | - | ContractExpiryDateUTC | datetime2 | Y | | 合同到期日（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimWarehouse（仓库维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| WarehouseId | bigint | N | autoincr | 主键 | | - | WarehouseCode | varchar(64) | N | | 仓库编码（业务键） | 来源：仓库列表.仓库编码 | - | WarehouseName | varchar(255) | N | | 仓库名称 | | - | WarehouseType | varchar(50) | Y | | 仓库类型 | | - | Status | varchar(20) | Y | | 仓库状态 | ENABLED,DISABLED | - | Address | varchar(500) | Y | | 仓库地址 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimBom（物料清单维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| BomId | bigint | N | autoincr | 主键 | | - | BomNumber | varchar(64) | N | | 清单编号（业务键） | 来源：物料清单.清单编号 | - | BomTopic | varchar(100) | Y | | 清单主题 | 来源：清单主题 | -| IsPrimary | bit | Y | | 主辅清单标识 | 0,1 | -| LevelNumber | int | Y | | 层级 | | - | ProductCode | varchar(64) | Y | | 关联产品编码 | 来源：产品编号 | - | UnitUsageQty | decimal(18,4) | Y | | 单位用量 | | - | UomCode | varchar(16) | Y | | 产品单位 | | - | SupplierName | varchar(255) | Y | | 默认供应商（退化属性） | 来源：供应商 | -| Remark | varchar(500) | Y | | 备注 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimRouting（工艺路线维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| RoutingId | bigint | N | autoincr | 主键 | | - | RoutingNumber | varchar(64) | N | | 工艺路线编号（业务键） | 来源：工艺路线.工艺路线编号 | - | RoutingName | varchar(255) | N | | 工艺路线名称 | | - | OperationListText | varchar(2000) | Y | | 工序/工艺路线列表（原文） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimOperation（工序维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| OperationId | bigint | N | autoincr | 主键 | | - | OperationNumber | varchar(64) | N | | 工序编号（业务键） | 来源：工序.工序编号 | - | OperationName | varchar(255) | N | | 工序名称 | | - | ReportPermission | varchar(50) | Y | | 报工权限 | | - | ReportRatio | decimal(18,6) | Y | | 报工数配比 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimEquipment（设备维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| EquipmentId | bigint | N | autoincr | 主键 | | - | EquipmentNumber | varchar(64) | N | | 设备编号（业务键） | 来源：设备.设备编号 | - | EquipmentName | varchar(255) | N | | 设备名称 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimQcReason（质检不通过原因维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| QcReasonId | bigint | N | autoincr | 主键 | | - | QcReasonNumber | varchar(64) | N | | 不通过原因编号（业务键） | 来源：质检原因.不通过原因编号 | - | QcReasonName | varchar(255) | N | | 不通过原因 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimContract（合同维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ContractId | bigint | N | autoincr | 主键 | | - | ContractNumber | varchar(64) | N | | 合同编号（业务键） | 来源：合同.合同编号 | - | ContractName | varchar(255) | Y | | 合同名称 | | - | OwnerId | bigint | Y | | 负责人 (外键 → DimPerson) | | - | CustomerName | varchar(255) | Y | | 客户名称（退化） | | - | CustomerId | bigint | Y | | 关联客户（外键 → DimCustomer） | | - | ContractAmount | decimal(18,4) | Y | | 合同金额 | | - | OrderTimeUTC | datetime2 | Y | | 下单时间（UTC） | | -| PaidAmount | decimal(18,4) | Y | | 回款金额 | | -| UnpaidAmount | decimal(18,4) | Y | | 未回款金额 | | -| Stage | varchar(50) | Y | | 当前阶段 | | -| Result | varchar(50) | Y | | 结果 | | - | ContractType | varchar(50) | Y | | 合同类型 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactInventoryMovement（出入库流水事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| InventoryMovementId | bigint | N | autoincr | 主键 | | - | EventTimeUTC | datetime2 | N | | 出入库事件时间（UTC） | 来源：出入库日期/入库时间/出库时间 | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | - | WarehouseId | bigint | Y | | 关联DimWarehouse (外键 → DimWarehouse) | | - | BomId | bigint | Y | | 关联DimBom (外键 → DimBom) | | -| OperatorId | bigint | Y | | 外键 → DimPerson（操作人） | | -|SalesOrderId | bigint | Y | | 关联FactSalesOrder (外键 → FactSalesOrder) | | -|PurchaseOrderId | bigint | Y | | 关联FactPurchaseOrder (外键 → FactPurchaseOrder) | | - | DocNumber | varchar(64) | N | | 出入库单号 | 来源：出入库单号/入库单编号/出库单编号 | - | DocType | varchar(30) | N | | 单据类型 | IN,OUT,TRANSFER,OTHER_IN,OTHER_OUT | - | MovementQty | decimal(18,4) | N | 0 | 出入库数量（入为正，出为负） | | -| UomCode | varchar(16) | Y | | 单位 | | -| UnitPrice | decimal(18,4) | Y | | 单价 | | -| DiscountUnitPrice | decimal(18,4) | Y | | 折后单价 | | -| Amount | decimal(18,4) | Y | | 总价 | | -| DiscountAmount | decimal(18,4) | Y | | 折后总价 | | -| OnhandQtyAfter | decimal(18,4) | Y | | 发生后库存量（源字段） | | -| ResponsiblePerson | varchar(100) | Y | | 负责人 | | -| AuditStatus | varchar(20) | Y | | 审核状态 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactWorkOrder（工单事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| WorkOrderId | bigint | N | autoincr | 主键 | | - | WorkOrderNumber | varchar(64) | N | | 工单编号（退化维度） | 来源：工单编号 | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | -| SupplierId | bigint | Y | | 外键 → DimSupplier（委外时） | | -| RoutingId | bigint | Y | | 外键 → DimRouting | | -| ProductionManagerId | bigint | Y | | 外键 → DimPerson（生产主管） | | - | PlannedQty | decimal(18,4) | Y | | 计划生产数 | | - | CompletedQty | decimal(18,4) | Y | | 完成数 | | -| OperationGoodQty | decimal(18,4) | Y | | 工序良品数 | | -| OperationBadQty | decimal(18,4) | Y | | 工序不良品数 | | -| QcGoodQty | decimal(18,4) | Y | | 工单质检良品数 | | -| QcBadQty | decimal(18,4) | Y | | 工单质检不良品数 | | - | Status | varchar(30) | Y | | 工单状态 | OPEN,STARTED,CLOSED,SCRAP | - | EventTimeUTC | datetime2 | N | | 工单状态变更/抽取时间（UTC） | | -| SourceDocNumber | varchar(64) | Y | | 单据编号（退化） | 来源：单据编号 | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactOperationTask（工序任务事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| OperationTaskId | bigint | N | autoincr | 主键 | | -| WorkOrderNumber | varchar(64) | N | | 工单编号（退化维度） | 来源：工单编号 | -| OperationId | bigint | Y | | 关联DimOperation (外键 → DimOperation) | | -| ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | -| PlannedStartTimeUTC | datetime2 | Y | | 计划开始时间（UTC） | | -| PlannedEndTimeUTC | datetime2 | Y | | 计划结束时间（UTC） | | -| ActualStartTimeUTC | datetime2 | Y | | 实际开始时间（UTC） | | -| ActualEndTimeUTC | datetime2 | Y | | 实际结束时间（UTC） | | -| PlannedQty | decimal(18,4) | Y | | 计划数 | | -| GoodQty | decimal(18,4) | Y | | 良品数 | | -| BadQty | decimal(18,4) | Y | | 不良品数 | | -| BadReasonText | varchar(500) | Y | | 不良品项（原文） | | -| EventTimeUTC | datetime2 | N | | 任务事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactLaborReport（报工事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| LaborReportId | bigint | N | autoincr | 主键 | | - | WorkOrderNumber | varchar(64) | N | | 工单（退化维度） | 来源：报工列表.工单 | -| TaskName | varchar(255) | Y | | 任务（退化维度） | 来源：报工列表.任务 | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | -| OperationStatus | varchar(30) | Y | | 工序状态 | | - | WorkerName | varchar(100) | Y | | 生产人员 | | - | ReportQty | decimal(18,4) | N | 0 | 报工数 | | - | GoodQty | decimal(18,4) | Y | | 良品数 | | - | BadQty | decimal(18,4) | Y | | 良品数 | | -| DurationMinutes | decimal(18,2) | Y | | 报工时长（分钟） | | -| UomCode | varchar(16) | Y | | 单位 | | -| BadReasonText | varchar(500) | Y | | 不良品项（原文） | | - | EventTimeUTC | datetime2 | N | | 报工事件时间（UTC） | | -| SupplierId | bigint | Y | | 外键 → DimSupplier（委外时） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactQualityInspection（质检事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| QualityInspectionId | bigint | N | autoincr | 主键 | | - | InspectionNumber | varchar(64) | N | | 质检记录编号（退化维度） | 来源：质检记录编号 | -| InspectionBatchNumber | varchar(64) | Y | | 质检批次号 | | - | WorkOrderNumber | varchar(64) | Y | | 工单（退化维度） | | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | -| SupplierId | bigint | Y | | 外键 → DimSupplier | | -| QcReasonId | bigint | Y | | 外键 → DimQcReason（不通过原因） | | - | InspectorId | bigint | Y | | 质检员 (外键 → DimPerson) | | -| QcType | varchar(30) | Y | | 质检类型 | IN_PROCESS,FINAL,INCOMING | - | PassQty | decimal(18,4) | Y | | 通过数 | | - | FailQty | decimal(18,4) | Y | | 不通过数 | | -| SalesOrderNumber | varchar(64) | Y | | 销售订单（退化维度） | | -| CustomerName | varchar(255) | Y | | 客户（退化维度） | | -| ShipQty | decimal(18,4) | Y | | 发货数 | | - | EventTimeUTC | datetime2 | N | | 质检事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactSalesOrder（销售订单事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| SalesOrderId | bigint | N | autoincr | 主键 | | - | SalesOrderNumber | varchar(64) | N | | 订单编号（退化维度） | 来源：销售订单.订单编号 | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | SalesPersonId | bigint | Y | | 关联销售员 (外键 → DimPerson) | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化维度） | | - | ContractId | bigint | Y | | 关联合同 (外键 → DimContract) | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | OrderDateUTC | datetime2 | Y | | 订单单据日期（UTC） | | -| DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | - | DealAmount | decimal(18,4) | Y | | 成交金额 | | -| PaymentStatus | varchar(30) | Y | | 收款状态/付款状态 | | -| TransportMode | varchar(50) | Y | | 运输方式 | | -| Packaging | varchar(100) | Y | | 包材 | | -| CustomsNumber | varchar(64) | Y | | 报关单号 | | -| ProductionDocNumber | varchar(64) | Y | | 生产单据编号 | | -| Stage | varchar(50) | Y | | 当前阶段 | | - | Result | varchar(50) | Y | | 结果 | | - | EventTimeUTC | datetime2 | N | | 订单事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactSalesShipment（销售出库事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| SalesShipmentId | bigint | N | autoincr | 主键 | | - | ShipmentNumber | varchar(64) | N | | 出库单编号（退化维度） | 来源：销售出库单.出库单编号 | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | SalesPersonId | bigint | Y | | 关联销售员 (外键 → DimPerson) | | -| OperatorId | bigint | Y | | 外键 → DimPerson（发货员） | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化维度） | | - | ContractId | bigint | Y | | 关联合同 (外键 → DimContract) | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | DocDateUTC | datetime2 | Y | | 出库单单据日期（UTC） | | -| DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | -| TransportMode | varchar(50) | Y | | 运输方式 | | -| CustomsNumber | varchar(64) | Y | | 报关单号 | | - | Amount | decimal(18,4) | Y | | 成交金额/单据金额 | | - | EventTimeUTC | datetime2 | N | | 出库事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactSalesReturn（销售退货事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| SalesReturnId | bigint | N | autoincr | 主键 | | - | SalesReturnNumber | varchar(64) | N | | 销售退货单编号（退化维度） | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | SalesPersonId | bigint | Y | | 销售员 (外键 → DimPerson) | | -| OperatorId | bigint | Y | | 外键 → DimPerson（处理人） | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化维度） | | -| ReturnReason | varchar(255) | Y | | 退货原因 | | -| IsReship | bit | Y | | 是否退货重发 | 0,1 | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | -| DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | Amount | decimal(18,4) | Y | | 单据金额 | | - | EventTimeUTC | datetime2 | N | | 退货事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactPurchaseRequest（采购申请事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PurchaseRequestId | bigint | N | autoincr | 主键 | | - | PurchaseRequestNumber | varchar(64) | N | | 采购申请单编号（退化维度） | | - | SupplierId | bigint | Y | | 建议供应商 (外键 → DimSupplier) | | - | ApplicantId | bigint | Y | | 申请人 (外键 → DimPerson) | | - | CurrencyCode | varchar(16) | Y | | 币别 | | - | FxRate | decimal(18,6) | Y | | 汇率 | | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | - | TotalAmount | decimal(18,4) | Y | | 采购总价 | | - | Status | varchar(30) | Y | | 采购状态 | | - | RelatedOrderNumber | varchar(64) | Y | | 订单编号（退化） | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化） | | - | EventTimeUTC | datetime2 | N | | 采购申请事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactPurchaseOrder（采购订单事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PurchaseOrderId | bigint | N | autoincr | 主键 | | - | PurchaseOrderNumber | varchar(64) | N | | 采购订单编号（退化维度） | | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | - | BuyerId | bigint | Y | | 关联采购员 (外键 → DimPerson) | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | -| DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化） | | - | ContractId | bigint | Y | | 关联合同 (外键 → DimContract) | | -| DeliveryNoteNumber | varchar(64) | Y | | 送货单号 | | -| SettlementDateUTC | datetime2 | Y | | 结算日期（UTC） | | -| SettlementTerm | varchar(50) | Y | | 结算期限 | | - | EventTimeUTC | datetime2 | N | | 采购订单事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactPurchaseReceipt（采购入库事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PurchaseReceiptId | bigint | N | autoincr | 主键 | | - | PurchaseReceiptNumber | varchar(64) | N | | 采购入库单编号（退化维度） | | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | -| BuyerId | bigint | Y | | 外键 → DimPerson（采购员） | | -| OperatorId | bigint | Y | | 外键 → DimPerson（收货员） | | - | PurchaseOrderNumber | varchar(64) | Y | | 采购订单（退化维度） | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | -| DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | -| DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | -| ContractNumber | varchar(64) | Y | | 合同编号（退化） | | - | Amount | decimal(18,4) | Y | | 成交金额 | | -| EstimatedCost | decimal(18,4) | Y | | 预计采购费用 | | -| ReceiptQtyTotal | decimal(18,4) | Y | | 入库总数 | | -| PaidAmount | decimal(18,4) | Y | | 已付款金额 | | -| UnpaidAmount | decimal(18,4) | Y | | 未付款金额 | | -| PaymentStatus | varchar(30) | Y | | 付款状态 | | -| ReturnedQty | decimal(18,4) | Y | | 已退货数 | | -| NotReturnedQty | decimal(18,4) | Y | | 未退货数 | | -| ReturnStatus | varchar(30) | Y | | 退货状态 | | - | EventTimeUTC | datetime2 | N | | 入库事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactPurchaseReturn（采购退货事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PurchaseReturnId | bigint | N | autoincr | 主键 | | - | PurchaseReturnNumber | varchar(64) | N | | 采购退货单编号（退化维度） | | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | - | BuyerId | bigint | Y | | 采购员 (外键 → DimPerson) | | - | OperatorId | bigint | Y | | 处理人 (外键 → DimPerson) | | - | CurrencyCode | varchar(16) | Y | | 币别 | | - | FxRate | decimal(18,6) | Y | | 汇率 | | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | ReturnReason | varchar(255) | Y | | 退货原因 | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化） | | - | RelatedOrderNumber | varchar(64) | Y | | 订单号（退化） | | - | EventTimeUTC | datetime2 | N | | 退货事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactInventoryTransfer（库存调拨事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| InventoryTransferId | bigint | N | autoincr | 主键 | | - | TransferNumber | varchar(64) | N | | 调拨单编号（退化维度） | | - | OperatorId | bigint | Y | | 操作人 (外键 → DimPerson) | | - | TransferName | varchar(255) | Y | | 调拨名称 | | - | Tag | varchar(100) | Y | | 单据标签 | | - | TransferTimeUTC | datetime2 | Y | | 调拨时间（UTC） | | - | PlateNumber | varchar(32) | Y | | 车牌号 | | - | DeliveryMode | varchar(50) | Y | | 交货方式 | | - | EventTimeUTC | datetime2 | N | | 调拨事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactInventoryCount（库存盘点事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| InventoryCountId | bigint | N | autoincr | 主键 | | - | CountNumber | varchar(64) | N | | 盘点编号（退化维度） | | - | OperatorId | bigint | Y | | 盘点人 (外键 → DimPerson) | | - | WarehouseName | varchar(255) | Y | | 盘点仓库（退化） | | - | CountName | varchar(255) | Y | | 盘点名称 | | - | CountTimeUTC | datetime2 | Y | | 盘点时间（UTC） | | - | EventTimeUTC | datetime2 | N | | 盘点事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactScrap（报废事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ScrapId | bigint | N | autoincr | 主键 | | - | ScrapNumber | varchar(64) | N | | 报废编号（退化维度） | | - | OperatorId | bigint | Y | | 操作人 (外键 → DimPerson) | | - | WarehouseName | varchar(255) | Y | | 仓库（退化） | | - | ScrapTimeUTC | datetime2 | Y | | 报废日期（UTC） | | - | EventTimeUTC | datetime2 | N | | 报废事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactArReceipt（应收收款事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ArReceiptId | bigint | N | autoincr | 主键 | | - | DocNumber | varchar(64) | N | | 单据编号（退化维度） | 来源：收款单/回款/预收/其他收入等 | - | DocType | varchar(30) | N | | 单据类型 | RECEIPT,REFUND,ADVANCE,ADVANCE_REFUND,OTHER_INCOME | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | -| DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | Amount | decimal(18,4) | Y | | 收款总金额/回款金额/应收金额 | | -| AmountBase | decimal(18,4) | Y | | 本位币金额 | 来源：本位币字段 | -| FeeAmount | decimal(18,4) | Y | | 手续费 | | -| DiscountAmount | decimal(18,4) | Y | | 优惠金额 | | -| WriteOffAmount | decimal(18,4) | Y | | 核销总金额 | | -| WrittenOffAmount | decimal(18,4) | Y | | 已核销金额 | | -| UnwrittenOffAmount | decimal(18,4) | Y | | 未核销金额 | | -| RelatedDocNumber | varchar(64) | Y | | 关联单编号/销售订单编号 | | - | EventTimeUTC | datetime2 | N | | 收款事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactApPayment（应付付款事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ApPaymentId | bigint | N | autoincr | 主键 | | - | DocNumber | varchar(64) | N | | 单据编号（退化维度） | 来源：付款单/预付款/其他支出等 | - | DocType | varchar(30) | N | | 单据类型 | PAYMENT,REFUND,ADVANCE,ADVANCE_REFUND,OTHER_EXPENSE | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | -| DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | Amount | decimal(18,4) | Y | | 付款总金额/应付金额 | | -| AmountBase | decimal(18,4) | Y | | 本位币金额 | | -| FeeAmount | decimal(18,4) | Y | | 手续费 | | -| DiscountAmount | decimal(18,4) | Y | | 优惠金额 | | -| WriteOffAmount | decimal(18,4) | Y | | 核销总金额 | | -| WrittenOffAmount | decimal(18,4) | Y | | 已核销金额 | | -| UnwrittenOffAmount | decimal(18,4) | Y | | 未核销金额 | | -| RelatedDocNumber | varchar(64) | Y | | 关联单编号/采购订单编号 | | - | EventTimeUTC | datetime2 | N | | 付款事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactInvoice（开票申请事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| InvoiceId | bigint | N | autoincr | 主键 | | - | InvoiceRequestNumber | varchar(64) | N | | 发票申请编号（退化维度） | 来源：发票申请编号 | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化维度） | | -| HandlerId | bigint | Y | | 外键 → DimPerson（经办人） | | -| CustomerId | bigint | Y | | 外键 → DimCustomer | | - | InvoiceAmount | decimal(18,4) | Y | | 开票金额 | | -| ContractAmount | decimal(18,4) | Y | | 合同金额 | | - | InvoiceDateUTC | datetime2 | Y | | 开票日期（UTC） | | - | ActualInvoiceDateUTC | datetime2 | Y | | 开票日期（UTC） | | -| InvoiceType | varchar(50) | Y | | 开票类型 | | -| Stage | varchar(50) | Y | | 当前阶段 | | -| Result | varchar(50) | Y | | 结果 | | - | EventTimeUTC | datetime2 | N | | 开票事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactWriteOff（核销事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| WriteOffId | bigint | N | autoincr | 主键 | | - | WriteOffNumber | varchar(64) | N | | 核销单编号（退化维度） | 来源：核销单编号 | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | - | HandlerId | bigint | Y | | 经办人 (外键 → DimPerson) | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | - | WriteOffType | varchar(30) | Y | | 核销类型 | AR,AP | - | CurrencyCode | varchar(16) | Y | | 币别 | | - | FxRate | decimal(18,6) | Y | | 汇率 | | - | DiscountAmount | decimal(18,4) | Y | | 优惠金额 | | - | WriteOffAmount | decimal(18,4) | Y | | 核销总金额 | | - | EventTimeUTC | datetime2 | N | | 核销事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactCashFlow（资金流水事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| CashFlowId | bigint | N | autoincr | 主键 | | - | DocNumber | varchar(64) | Y | | 单据编号（退化维度） | 来源：资金流向.单据编号/转账单编号 | - | DocType | varchar(30) | N | | 单据类型 | CASHFLOW,TRANSFER | - | AccountName | varchar(255) | Y | | 账户名称 | | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | BusinessType | varchar(100) | Y | | 业务类型 | | - | IncomeAmount | decimal(18,4) | Y | | 收入 | | - | ExpenseAmount | decimal(18,4) | Y | | 支出 | | - | BalanceAmount | decimal(18,4) | Y | | 余额 | | - | Counterparty | varchar(255) | Y | | 往来单位 | | - | Remark | varchar(500) | Y | | 单据备注 | | - | EventTimeUTC | datetime2 | N | | 资金事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactCommission（分佣事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| CommissionId | bigint | N | autoincr | 主键 | | - | CommissionPerson | varchar(100) | N | | 分佣人 | | - | Department | varchar(100) | Y | | 部门 | | - | CommissionRatePct | decimal(9,4) | Y | | 提成比例（%） | | - | Coefficient | decimal(18,6) | Y | | 系数 | | - | ReceivableCommission | decimal(18,4) | Y | | 应收佣金 | | - | ReceivedCommission | decimal(18,4) | Y | | 实收佣金 | | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | ProductName | varchar(255) | Y | | 产品名称（退化） | | - | OpportunityName | varchar(255) | Y | | 商机名称（退化） | | - | ContractAmount | decimal(18,4) | Y | | 合同金额 | | - | DealTimeUTC | datetime2 | Y | | 成交时间（UTC） | | - | IsInvoiced | bit | Y | | 是否开票 | 0,1 | - | EventTimeUTC | datetime2 | N | | 分佣事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactLead（线索事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| LeadId | bigint | N | autoincr | 主键 | | - | LeadName | varchar(255) | N | | 线索名称 | | - | OwnerId | bigint | Y | | 负责人 (外键 → DimPerson) | | - | LeadSource | varchar(100) | Y | | 线索来源 | | -| Phone | varchar(32) | Y | | 手机 | | -| Telephone | varchar(32) | Y | | 电话 | | -| Email | varchar(100) | Y | | 邮箱 | | -| Address | varchar(500) | Y | | 地址 | | -| Industry | varchar(100) | Y | | 客户行业 | | -| CustomerLevel | varchar(50) | Y | | 客户级别 | | -| NextContactTimeUTC | datetime2 | Y | | 下次联系时间（UTC） | | -| FollowUpStatus | varchar(50) | Y | | 跟进状态 | | -| IsConverted | bit | Y | | 是否已转化 | 0,1 | -| Stage | varchar(50) | Y | | 当前阶段 | | -| Result | varchar(50) | Y | | 结果 | | - | EventTimeUTC | datetime2 | N | | 线索事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactOpportunity（商机事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| OpportunityId | bigint | N | autoincr | 主键 | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | OpportunityAmount | decimal(18,4) | Y | | 商机金额 | | - | ExpectedDealDateUTC | datetime2 | Y | | 预计成交日期（UTC） | | -| NextContactTimeUTC | datetime2 | Y | | 下次联系时间（UTC） | | -| StatusGroup | varchar(50) | Y | | 商机状态组 | | -| Stage | varchar(50) | Y | | 当前阶段 | | - | Result | varchar(50) | Y | | 结果 | | - | EventTimeUTC | datetime2 | N | | 商机事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactProductionPlan（生产计划_装配工单汇总事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ProductionPlanId | bigint | N | autoincr | 主键 | | - | AssemblyWorkOrderNumber | varchar(64) | N | | 装配工单编号（退化维度） | 来源：装配工单编号 | - | WorkOrderCount | int | Y | | 工单数 | | - | FinishedCount | int | Y | | 已结束数 | | - | PlannedQty | decimal(18,4) | Y | | 计划数 | | - | CompletedQty | decimal(18,4) | Y | | 完工数 | | - | PlanStatus | varchar(50) | Y | | 生产计划状态 | | - | ProgressText | varchar(100) | Y | | 工单生成/用料生成/单据进度（原文） | | - | CustomerName | varchar(255) | Y | | 客户（退化） | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | - | SalesOrderId | bigint | Y | | 关联FactSalesOrder (外键 → FactSalesOrder) | | - | EventTimeUTC | datetime2 | N | | 计划事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -## KPI 映射（示例） - -| KPI | 口径 | 表/字段 | -|---|---|---| -| 报工良率 | `sum(GoodQty) / nullif(sum(ReportQty),0)` | `FactLaborReport.GoodQty/ReportQty` | -| 质检良率 | `sum(PassQty) / nullif(sum(PassQty+FailQty),0)` | `FactQualityInspection.PassQty/FailQty` | -| 采购未付款 | `sum(UnpaidAmount)` | `FactPurchaseReceipt.UnpaidAmount` | -| AR 未核销 | `sum(UnwrittenOffAmount)` | `FactArReceipt.UnwrittenOffAmount` | -| AP 未核销 | `sum(UnwrittenOffAmount)` | `FactApPayment.UnwrittenOffAmount` | - - -## 数据字典 - -以下枚举值仅供参考，实际实施时应根据业务系统配置进行调整。 - -### 1. 通用类 (Common) - -| 字典代码 | 字典名称 | 枚举值 (Code: Name) | 适用字段 | -|---|---|---|---| -| **CurrencyCode** | 币种 | CNY: 人民币
USD: 美元
EUR: 欧元
JPY: 日元
HKD: 港币 | `FactSalesOrder.CurrencyCode`
`FactPurchaseOrder.CurrencyCode`
`FactArReceipt.CurrencyCode`
... | -| **UomCode** | 计量单位 | pcs: 个/件
kg: 千克
m: 米
L: 升
box: 箱
set: 套 | `DimProduct.UomCode`
`DimBom.UomCode`
`FactInventoryMovement.UomCode` | -| **Boolean** | 布尔标识 | 0: 否 (No/False)
1: 是 (Yes/True) | `IsCurrent`, `IsLocked`, `IsPrimary`, `IsReship`, `IsConverted`, `IsInvoiced` | - -### 2. 人员与组织 (Person & Org) - -| 字典代码 | 字典名称 | 枚举值 | 适用字段 | -|---|---|---|---| -| **PersonStatus** | 人员状态 | ACTIVE: 在职
INACTIVE: 离职
LEAVE: 休假 | `DimPerson.Status` | -| **Department** | 部门 | SALES: 销售部
MFG: 生产部
PUR: 采购部
WH: 仓储部
FIN: 财务部
QC: 质检部 | `DimPerson.Department` | -| **SupplierLevel** | 供应商等级 | STRATEGIC: 战略供应商
PREFERRED: 优选供应商
APPROVED: 合格供应商
PROBATION: 考察供应商
EXIT: 淘汰供应商 | `DimSupplier.SupplierLevel` | -| **CustomerLevel** | 客户等级 | KA: 重点客户
VIP: 重要客户
NORMAL: 普通客户
POTENTIAL: 潜在客户 | `DimCustomer.CustomerLevel`
`FactLead.CustomerLevel` | -| **Industry** | 行业 | MFG: 制造
RETAIL: 零售
IT: 互联网
FIN: 金融
EDU: 教育 | `DimCustomer.Industry`
`FactLead.Industry` | - -### 3. 生产与库存 (Production & Inventory) - -| 字典代码 | 字典名称 | 枚举值 | 适用字段 | -|---|---|---|---| -| **WorkOrderStatus** | 工单状态 | PLANNED: 计划中
RELEASED: 已下达
STARTED: 生产中
PAUSED: 暂停
COMPLETED: 已完工
CLOSED: 已结案
SCRAP: 报废 | `FactWorkOrder.Status` | -| **MovementType** | 出入库类型 | IN_PURCHASE: 采购入库
IN_FG: 完工入库
IN_RETURN: 销售退货入库
IN_OTHER: 其他入库
OUT_SALES: 销售出库
OUT_MATERIAL: 生产领料
OUT_RETURN: 采购退货出库
OUT_SCRAP: 报废出库
TRANSFER: 调拨 | `FactInventoryMovement.DocType` | -| **WarehouseType** | 仓库类型 | RAW: 原材料仓
FG: 成品仓
WIP: 线边仓/半成品仓
SCRAP: 废品仓
VMI: 供应商库存仓 | `DimWarehouse.WarehouseType` | -| **QcType** | 质检类型 | IQC: 来料检验
IPQC: 制程检验
FQC: 终检
OQC: 出货检验 | `FactQualityInspection.QcType` | -| **ProductType** | 产品类型 | FINISHED: 成品
SEMI: 半成品
RAW: 原材料
SERVICE: 服务 | `DimProduct.ProductType` | -| **TransportCondition** | 运输条件 | NORMAL: 常温
COLD: 冷链
FRAGILE: 易碎
HAZARDOUS: 危险品 | `DimProduct.TransportCondition` | -| **PlanStatus** | 计划状态 | DRAFT: 草稿
CONFIRMED: 已确认
EXECUTING: 执行中
COMPLETED: 已完成 | `FactProductionPlan.PlanStatus` | - -### 4. 销售与采购 (Sales & Purchase) - -| 字典代码 | 字典名称 | 枚举值 | 适用字段 | -|---|---|---|---| -| **LeadSource** | 线索来源 | WEB: 官网
REFERRAL: 推荐
EXHIBITION: 展会
COLD_CALL: 陌拜
OTHER: 其他 | `FactLead.LeadSource` | -| **SalesStage** | 销售阶段 | NEW: 新建
QUOTED: 已报价
NEGOTIATING: 谈判中
WON: 赢单
LOST: 输单 | `FactOpportunity.Stage`
`FactLead.Stage` | -| **OppStatusGroup** | 商机状态组 | OPEN: 开启
WON: 赢单
LOST: 输单 | `FactOpportunity.StatusGroup` | -| **PurchaseStatus** | 采购状态 | DRAFT: 草稿
APPROVED: 已审批
SENT: 已发送
PARTIAL_RECEIVED: 部分收货
RECEIVED: 全部收货
CLOSED: 关闭 | `FactPurchaseRequest.Status` | -| **PaymentStatus** | 支付状态 | UNPAID: 未付款
PARTIAL: 部分付款
PAID: 已付款
OVERDUE: 逾期 | `FactSalesOrder.PaymentStatus`
`FactPurchaseReceipt.PaymentStatus` | -| **TransportMode** | 运输方式 | LAND: 陆运
SEA: 海运
AIR: 空运
RAIL: 铁运
EXPRESS: 快递 | `FactSalesOrder.TransportMode`
`FactSalesShipment.TransportMode` | - -### 5. 财务 (Finance) - -| 字典代码 | 字典名称 | 枚举值 | 适用字段 | -|---|---|---|---| -| **FinDocType** | 财务单据类型 | RECEIPT: 收款单
PAYMENT: 付款单
INVOICE: 发票
REFUND: 退款
ADVANCE: 预收/预付
WRITEOFF: 核销 | `FactArReceipt.DocType`
`FactApPayment.DocType` | -| **WriteOffType** | 核销类型 | AR: 应收核销
AP: 应付核销 | `FactWriteOff.WriteOffType` | -| **InvoiceType** | 发票类型 | VAT_SPECIAL: 增值税专票
VAT_NORMAL: 增值税普票
ELECTRONIC: 电子发票 | `FactInvoice.InvoiceType` | - - -## 变更记录 - -| 版本 | 日期 | 作者 | 变更描述 | 脚本影响 | -|---|---|---|---|---| -| v1.0.0 | 2025-12-25 | GPT-5.2 | 首次输出：维度SCD2 + 事实星座 + ER图 + 字段清单 | 未生成 | -| v1.1.0 | 2025-12-28 | Trae | 新增 DimPerson（人员维度）并关联销售、采购、生产、审批等事实表 | 待生成 | -| v1.2.0 | 2025-12-29 | Trae | 新增「业务数据流转全景图」，包含商机-订单-计划-生产-交付全链路 Mermaid 流程图 | 无 | -| v1.3.0 | 2025-12-29 | Trae | 升级全景图，新增外部采购闭环（PO管理、入库验收IQC、三单匹配AP、供应商管理） | 无 | -| v1.4.0 | 2025-12-29 | Trae | 升级全景图，新增财务与资金阶段（开票Invoice、收付款AP/AR、核销WriteOff、资金CashFlow） | 无 | -| v1.5.0 | 2025-12-30 | Trae | 新增「数据字典」章节，规范化通用类、人员组织、生产库存、销售采购及财务类枚举值 | 无 | diff --git a/lzwcai_mcpskills_mfg_data_agent/pyproject.toml b/lzwcai_mcpskills_mfg_data_agent/pyproject.toml deleted file mode 100644 index 2b6d240..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/pyproject.toml +++ /dev/null @@ -1,38 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai-mcpskills-mfg-data-agent" -version = "0.1.6" -description = "制造业数据智能体 - MCP server for manufacturing data intelligence with dynamic tool generation" -readme = "README.md" -requires-python = ">=3.10" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "sql", "manufacturing", "data", "agent", "智能体"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", - "pypinyin>=0.53.0", -] - -[project.scripts] -lzwcai-mcpskills-mfg-data-agent = "lzwcai_mcpskills_mfg_data_agent.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcpskills_mfg_data_agent"] - -[tool.hatch.build.targets.wheel.force-include] -"lzwcai_mcpskills_mfg_data_agent/businessQueries.json" = "lzwcai_mcpskills_mfg_data_agent/businessQueries.json" diff --git a/lzwcai_mcpskills_mfg_data_agent/sql/一页式决策简报.sql b/lzwcai_mcpskills_mfg_data_agent/sql/一页式决策简报.sql deleted file mode 100644 index 156d3f6..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/sql/一页式决策简报.sql +++ /dev/null @@ -1,317 +0,0 @@ --- ===================================================== --- 一页式决策简报SQL --- One-Page Decision Brief --- 数据库: PostgreSQL --- ===================================================== - --- ===================================================== --- 主查询：一页式决策简报 --- ===================================================== -WITH -sales_summary AS ( - SELECT - COUNT(*) AS total_orders, - SUM(deal_amount) AS total_sales_amount, - AVG(deal_amount) AS avg_order_amount, - SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, - SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, - SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, - SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, - SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount - FROM fact_sales_order -), - -production_summary AS ( - SELECT - COUNT(*) AS total_work_orders, - SUM(planned_qty) AS total_planned_qty, - SUM(completed_qty) AS total_completed_qty, - SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, - SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, - SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders - FROM fact_work_order -), - -ar_summary AS ( - SELECT - COUNT(*) AS receipt_count, - SUM(amount) AS total_receipt_amount, - AVG(amount) AS avg_receipt_amount - FROM fact_ar_receipt -), - -ap_summary AS ( - SELECT - COUNT(*) AS payment_count, - SUM(amount) AS total_payment_amount, - AVG(amount) AS avg_payment_amount - FROM fact_ap_payment -), - -invoice_summary AS ( - SELECT - COUNT(*) AS invoice_count, - SUM(invoice_amount) AS total_invoice_amount, - AVG(invoice_amount) AS avg_invoice_amount - FROM fact_invoice -), - -return_summary AS ( - SELECT - COUNT(*) AS return_count, - SUM(amount) AS total_return_amount, - AVG(amount) AS avg_return_amount - FROM fact_sales_return -), - -shipment_summary AS ( - SELECT - COUNT(*) AS shipment_count, - SUM(amount) AS total_shipment_amount, - AVG(amount) AS avg_shipment_amount - FROM fact_sales_shipment -), - -purchase_summary AS ( - SELECT COUNT(*) AS purchase_order_count - FROM fact_purchase_order -), - -quality_summary AS ( - SELECT - COUNT(*) AS inspection_count, - SUM(pass_qty) AS total_pass_qty, - SUM(fail_qty) AS total_fail_qty - FROM fact_quality_inspection -), - -labor_summary AS ( - SELECT - COUNT(DISTINCT worker_name) AS worker_count, - SUM(duration_minutes) AS total_work_minutes, - SUM(report_qty) AS total_output_qty - FROM fact_labor_report -), - -scrap_summary AS ( - SELECT COUNT(*) AS scrap_count - FROM fact_scrap -) - -SELECT - 'Decision Brief' AS report_title, - CURRENT_DATE AS report_date, - - -- 销售板块 - ss.total_orders AS sales_order_count, - ROUND(ss.total_sales_amount, 2) AS total_sales_amount, - ROUND(ss.avg_order_amount, 2) AS avg_order_amount, - ss.paid_orders AS paid_order_count, - ss.partial_orders AS partial_paid_count, - ss.unpaid_orders AS unpaid_order_count, - ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, - ROUND(ss.unpaid_amount, 2) AS receivable_amount, - - -- 生产板块 - ps.total_work_orders AS work_order_count, - ps.closed_orders AS completed_work_orders, - ps.started_orders AS in_progress_work_orders, - ps.open_orders AS pending_work_orders, - ROUND(ps.total_planned_qty, 0) AS planned_qty, - ROUND(ps.total_completed_qty, 0) AS completed_qty, - ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, - - -- 人效板块 - ls.worker_count AS active_worker_count, - ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, - ROUND(ls.total_output_qty, 0) AS total_output, - ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, - ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, - - -- 质量板块 - qs.inspection_count AS qc_batch_count, - ROUND(qs.total_pass_qty, 0) AS pass_qty, - ROUND(qs.total_fail_qty, 0) AS fail_qty, - ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, - ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, - scr.scrap_count AS scrap_record_count, - - -- 财务板块 - ar.receipt_count AS ar_receipt_count, - ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, - ap.payment_count AS ap_payment_count, - ROUND(ap.total_payment_amount, 2) AS total_ap_amount, - ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, - inv.invoice_count AS invoice_count, - ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, - - -- 物流板块 - sh.shipment_count AS shipment_count, - ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, - pur.purchase_order_count AS purchase_order_count, - - -- 售后板块 - ret.return_count AS return_count, - ROUND(ret.total_return_amount, 2) AS total_return_amount, - ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, - - -- 综合健康度评估 - CASE - WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 - AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 - AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 - THEN 'EXCELLENT' - WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 - AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 - AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 - THEN 'GOOD' - ELSE 'WARNING' - END AS health_status - -FROM sales_summary ss -CROSS JOIN production_summary ps -CROSS JOIN ar_summary ar -CROSS JOIN ap_summary ap -CROSS JOIN invoice_summary inv -CROSS JOIN return_summary ret -CROSS JOIN shipment_summary sh -CROSS JOIN purchase_summary pur -CROSS JOIN quality_summary qs -CROSS JOIN labor_summary ls -CROSS JOIN scrap_summary scr; - - --- ===================================================== --- 补充查询1：本月 vs 上月对比分析 --- ===================================================== -WITH -current_month AS ( - SELECT - 'current_month' AS period, - COUNT(*) AS order_count, - SUM(deal_amount) AS sales_amount - FROM fact_sales_order - WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE) -), -last_month AS ( - SELECT - 'last_month' AS period, - COUNT(*) AS order_count, - SUM(deal_amount) AS sales_amount - FROM fact_sales_order - WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month') -) -SELECT - period, - order_count, - ROUND(sales_amount, 2) AS sales_amount -FROM current_month -UNION ALL -SELECT * FROM last_month; - - --- ===================================================== --- 补充查询2：客户贡献度TOP10 --- ===================================================== -SELECT - c.customer_name, - COUNT(so.sales_order_id) AS order_count, - ROUND(SUM(so.deal_amount), 2) AS total_order_amount, - ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, - SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, - SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount -FROM fact_sales_order so -INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' -GROUP BY c.customer_name -ORDER BY total_order_amount DESC -LIMIT 10; - - --- ===================================================== --- 补充查询3：产品类别业绩分析 --- ===================================================== -SELECT - p.product_category, - COUNT(DISTINCT wo.work_order_id) AS work_order_count, - ROUND(SUM(wo.planned_qty), 0) AS planned_qty, - ROUND(SUM(wo.completed_qty), 0) AS completed_qty, - ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, - COUNT(DISTINCT lr.worker_name) AS worker_count, - ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours -FROM dim_product p -LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id -LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id -WHERE p.is_current = 't' -GROUP BY p.product_category -ORDER BY completed_qty DESC; - - --- ===================================================== --- 补充查询4：关键预警指标 --- ===================================================== -SELECT - 'warning_metrics' AS category, - 'unpaid_order_amount' AS metric_name, - ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, - 50000 AS threshold, - CASE - WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 - THEN 'EXCEEDED' ELSE 'NORMAL' - END AS status -FROM fact_sales_order - -UNION ALL - -SELECT - 'warning_metrics', - 'defect_rate_pct', - ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), - 5, - CASE - WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 - THEN 'EXCEEDED' ELSE 'NORMAL' - END -FROM fact_quality_inspection - -UNION ALL - -SELECT - 'warning_metrics', - 'production_completion_rate', - ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), - 70, - CASE - WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 - THEN 'LOW' ELSE 'NORMAL' - END -FROM fact_work_order - -UNION ALL - -SELECT - 'warning_metrics', - 'return_rate_pct', - ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / - NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), - 5, - CASE - WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / - NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 - THEN 'EXCEEDED' ELSE 'NORMAL' - END; - - --- ===================================================== --- 补充查询5：月度趋势汇总 --- ===================================================== -SELECT - TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, - COUNT(*) AS order_count, - ROUND(SUM(deal_amount), 2) AS sales_amount, - ROUND(AVG(deal_amount), 2) AS avg_order_amount, - SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, - SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count -FROM fact_sales_order -GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) -ORDER BY month DESC -LIMIT 12; diff --git a/lzwcai_mcpskills_mfg_data_agent/sql/人效产值损耗三维模型仪表盘.sql b/lzwcai_mcpskills_mfg_data_agent/sql/人效产值损耗三维模型仪表盘.sql deleted file mode 100644 index b6f98b9..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/sql/人效产值损耗三维模型仪表盘.sql +++ /dev/null @@ -1,193 +0,0 @@ --- ===================================================== --- 人效-产值-损耗三维模型仪表盘SQL --- Efficiency-Output-Loss 3D Model Dashboard --- 数据库: PostgreSQL --- 维度: 产品类别(化工/机械/电子)作为部门维度 --- ===================================================== - --- 1. 部门级综合仪表盘（主查询） -WITH -labor_stats AS ( - SELECT - p.product_category AS department, - COUNT(DISTINCT lr.worker_name) AS worker_count, - SUM(lr.duration_minutes) AS total_work_minutes, - SUM(lr.report_qty) AS total_output_qty, - COUNT(DISTINCT lr.work_order_number) AS work_order_count - FROM fact_labor_report lr - INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' - GROUP BY p.product_category -), - -work_order_stats AS ( - SELECT - p.product_category AS department, - COUNT(*) AS total_work_orders, - SUM(wo.planned_qty) AS total_planned_qty, - SUM(wo.completed_qty) AS total_completed_qty, - SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, - SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, - SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders - FROM fact_work_order wo - INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' - GROUP BY p.product_category -), - -quality_stats AS ( - SELECT - p.product_category AS department, - SUM(qi.pass_qty) AS total_pass_qty, - SUM(qi.fail_qty) AS total_fail_qty, - COUNT(*) AS inspection_count - FROM fact_quality_inspection qi - INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' - GROUP BY p.product_category -), - -sales_stats AS ( - SELECT - AVG(deal_amount) AS avg_order_amount, - SUM(deal_amount) AS total_sales_amount, - COUNT(*) AS order_count - FROM fact_sales_order -), - -scrap_stats AS ( - SELECT COUNT(*) AS total_scrap_count - FROM fact_scrap -) - -SELECT - ls.department, - - -- 人效维度 - ls.worker_count, - ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, - ls.total_output_qty, - ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, - ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, - ROUND( - ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), - 2 - ) AS efficiency_index, - - -- 产值维度 - ws.total_planned_qty AS planned_qty, - ws.total_completed_qty AS completed_qty, - ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, - ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, - ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, - - -- 损耗维度 - qs.total_pass_qty AS qc_pass_qty, - qs.total_fail_qty AS qc_fail_qty, - ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, - ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, - - -- 综合评分 - ROUND( - (ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + - (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + - (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, - 2 - ) AS performance_score, - - -- 预警等级 - CASE - WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 - OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 - THEN 'RED' - WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 - OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 - THEN 'YELLOW' - ELSE 'GREEN' - END AS warning_level - -FROM labor_stats ls -LEFT JOIN work_order_stats ws ON ls.department = ws.department -LEFT JOIN quality_stats qs ON ls.department = qs.department -CROSS JOIN sales_stats ss -CROSS JOIN scrap_stats scr -ORDER BY performance_score DESC; - - --- ===================================================== --- 2. 人员级明细报表 --- ===================================================== -SELECT - lr.worker_name, - p.product_category AS department, - COUNT(DISTINCT lr.work_order_number) AS work_order_count, - SUM(lr.duration_minutes) AS total_work_minutes, - ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, - SUM(lr.report_qty) AS total_output, - ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, - RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank -FROM fact_labor_report lr -INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' -GROUP BY lr.worker_name, p.product_category -ORDER BY p.product_category, total_output DESC; - - --- ===================================================== --- 3. 月度趋势分析 --- ===================================================== -SELECT - TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, - p.product_category AS department, - COUNT(DISTINCT lr.worker_name) AS active_worker_count, - SUM(lr.duration_minutes) AS total_work_minutes, - SUM(lr.report_qty) AS total_output, - ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, - ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour -FROM fact_labor_report lr -INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' -GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category -ORDER BY month DESC, p.product_category; - - --- ===================================================== --- 4. 产品级损耗分析 --- ===================================================== -SELECT - p.product_category AS department, - p.product_name, - p.product_code, - COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, - COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, - ROUND( - COALESCE(SUM(qi.fail_qty), 0) / - NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, - 2 - ) AS defect_rate, - CASE - WHEN COALESCE(SUM(qi.fail_qty), 0) / - NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 - THEN 'HIGH' - WHEN COALESCE(SUM(qi.fail_qty), 0) / - NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 - THEN 'MEDIUM' - ELSE 'LOW' - END AS loss_level -FROM dim_product p -LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id -WHERE p.is_current = 't' -GROUP BY p.product_category, p.product_name, p.product_code -ORDER BY defect_rate DESC NULLS LAST; - - --- ===================================================== --- 5. 工单完成率分析 --- ===================================================== -SELECT - p.product_category AS department, - wo.status, - COUNT(*) AS order_count, - SUM(wo.planned_qty) AS total_planned, - SUM(wo.completed_qty) AS total_completed, - ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, - ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate -FROM fact_work_order wo -INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' -GROUP BY p.product_category, wo.status -ORDER BY p.product_category, wo.status; diff --git a/lzwcai_mcpskills_mfg_data_agent/sql/供应链风险预警.sql b/lzwcai_mcpskills_mfg_data_agent/sql/供应链风险预警.sql deleted file mode 100644 index 857c7ee..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/sql/供应链风险预警.sql +++ /dev/null @@ -1,416 +0,0 @@ --- ===================================================== --- 供应链风险预警SQL --- Supply Chain Risk Warning --- 数据库: PostgreSQL --- ===================================================== - --- ===================================================== --- 1. 供应商历史交期表现分析 --- ===================================================== -WITH supplier_delivery AS ( - SELECT - po.supplier_id, - COUNT(*) AS order_count, - COUNT(pr.purchase_receipt_id) AS receipt_count, - AVG( - CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL - THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) - ELSE NULL END - ) AS avg_delivery_days, - MAX( - CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL - THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) - ELSE NULL END - ) AS max_delivery_days, - STDDEV( - CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL - THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) - ELSE NULL END - ) AS stddev_delivery_days - FROM fact_purchase_order po - LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id - GROUP BY po.supplier_id -), - -supplier_quality AS ( - SELECT - pr.supplier_id, - COUNT(pr.purchase_receipt_id) AS total_receipts, - SUM(pr.receipt_qty_total) AS total_qty, - SUM(pr.amount) AS total_amount - FROM fact_purchase_receipt pr - GROUP BY pr.supplier_id -), - -supplier_returns AS ( - SELECT - pret.supplier_id, - COUNT(*) AS return_count, - SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count - FROM fact_purchase_return pret - GROUP BY pret.supplier_id -), - -supplier_quality_rate AS ( - SELECT - sq.supplier_id, - sq.total_receipts, - sq.total_qty, - sq.total_amount, - COALESCE(sr.return_count, 0) AS return_count, - COALESCE(sr.damage_count, 0) AS damage_count, - CASE WHEN sq.total_receipts > 0 - THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts - ELSE 100 END AS quality_rate - FROM supplier_quality sq - LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id -), - -supplier_risk AS ( - SELECT - s.supplier_id, - s.supplier_name, - s.supplier_category, - COALESCE(sd.order_count, 0) AS order_count, - COALESCE(sd.receipt_count, 0) AS receipt_count, - ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, - ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, - ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, - COALESCE(sqr.total_receipts, 0) AS total_receipts, - COALESCE(sqr.return_count, 0) AS return_count, - ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, - CASE - WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 - WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 - WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 - ELSE 10 - END + - CASE - WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 - WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 - ELSE 10 - END AS delivery_risk_score, - CASE - WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 - WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 - WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 - ELSE 5 - END AS quality_risk_score - FROM dim_supplier s - LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id - LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id - WHERE s.is_current = 't' -), - -supplier_risk_level AS ( - SELECT - *, - delivery_risk_score + quality_risk_score AS total_risk_score, - CASE - WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' - WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' - ELSE 'LOW' - END AS risk_level, - CASE - WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' - WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' - WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' - ELSE 'NORMAL' - END AS risk_pattern - FROM supplier_risk -) - -SELECT - supplier_name, - supplier_category, - order_count, - receipt_count, - avg_delivery_days, - max_delivery_days, - delivery_volatility, - total_receipts, - return_count, - quality_rate, - delivery_risk_score, - quality_risk_score, - total_risk_score, - risk_level, - risk_pattern -FROM supplier_risk_level -ORDER BY total_risk_score DESC, supplier_name; - - --- ===================================================== --- 补充查询1：高风险订单预警清单 --- ===================================================== -WITH supplier_risk_info AS ( - SELECT - s.supplier_id, - s.supplier_name, - s.supplier_category, - COALESCE( - (SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) - FROM fact_purchase_order po2 - LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id - WHERE po2.supplier_id = s.supplier_id - AND pr.doc_date_utc IS NOT NULL), 0 - ) AS avg_delivery_days, - COALESCE( - (SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0 - ) AS return_count, - COALESCE( - (SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0 - ) AS receipt_count - FROM dim_supplier s - WHERE s.is_current = 't' -), -order_risk AS ( - SELECT - po.purchase_order_number, - po.doc_date_utc::date AS order_date, - sri.supplier_name, - sri.supplier_category, - ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, - sri.return_count, - sri.receipt_count, - CASE WHEN sri.receipt_count > 0 - THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) - ELSE 100 END AS quality_rate, - EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, - CASE - WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' - WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' - ELSE 'LOW' - END AS risk_level - FROM fact_purchase_order po - JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id -) -SELECT - purchase_order_number, - order_date, - supplier_name, - supplier_category, - supplier_avg_days, - quality_rate, - days_since_order, - risk_level, - CASE - WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' - WHEN risk_level = 'MEDIUM' THEN 'MONITOR' - ELSE 'NORMAL' - END AS action_required -FROM order_risk -WHERE risk_level IN ('HIGH', 'MEDIUM') -ORDER BY - CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, - days_since_order DESC; - - --- ===================================================== --- 补充查询2：供应商类别风险分布 --- ===================================================== -WITH supplier_stats AS ( - SELECT - s.supplier_category, - COUNT(DISTINCT s.supplier_id) AS supplier_count, - COUNT(DISTINCT po.purchase_order_id) AS order_count, - COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, - COUNT(DISTINCT pret.purchase_return_id) AS return_count, - SUM(pr.amount) AS total_amount - FROM dim_supplier s - LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id - LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id - LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id - WHERE s.is_current = 't' - GROUP BY s.supplier_category -) -SELECT - supplier_category, - supplier_count, - order_count, - receipt_count, - return_count, - CASE WHEN receipt_count > 0 - THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) - ELSE 100 END AS quality_rate, - ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, - CASE - WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' - WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' - ELSE 'LOW' - END AS category_risk_level -FROM supplier_stats -ORDER BY return_count DESC; - - --- ===================================================== --- 补充查询3：近期交期异常趋势 --- ===================================================== -WITH monthly_delivery AS ( - SELECT - DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, - COUNT(*) AS receipt_count, - AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days - FROM fact_purchase_receipt pr - JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id - WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL - GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp) -), -monthly_returns AS ( - SELECT - DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, - COUNT(*) AS return_count - FROM fact_purchase_return pret - GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp) -), -monthly_combined AS ( - SELECT - md.month_start, - md.receipt_count, - ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, - COALESCE(mr.return_count, 0) AS return_count - FROM monthly_delivery md - LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start -), -with_trend AS ( - SELECT - *, - LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, - LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count - FROM monthly_combined -) -SELECT - month_start, - receipt_count, - avg_delivery_days, - return_count, - CASE WHEN receipt_count > 0 - THEN ROUND(return_count * 100.0 / receipt_count, 1) - ELSE 0 END AS return_rate, - CASE - WHEN prev_avg_days IS NULL THEN 'NONE' - WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' - WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' - ELSE 'STABLE' - END AS delivery_trend, - CASE - WHEN prev_return_count IS NULL THEN 'NONE' - WHEN return_count > prev_return_count THEN 'INCREASING' - WHEN return_count < prev_return_count THEN 'DECREASING' - ELSE 'STABLE' - END AS return_trend -FROM with_trend -ORDER BY month_start DESC; - - --- ===================================================== --- 补充查询4：风险预警汇总看板 --- ===================================================== -WITH risk_summary AS ( - SELECT - s.supplier_id, - s.supplier_name, - COALESCE( - (SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0 - ) AS return_count, - COALESCE( - (SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0 - ) AS receipt_count - FROM dim_supplier s - WHERE s.is_current = 't' -), -risk_counts AS ( - SELECT - SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, - SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, - SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, - COUNT(*) AS total_suppliers - FROM risk_summary -), -order_stats AS ( - SELECT - COUNT(*) AS pending_orders, - COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders - FROM fact_purchase_order -), -return_stats AS ( - SELECT - COUNT(*) AS total_returns, - COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns - FROM fact_purchase_return -) -SELECT - 'high_risk_suppliers' AS metric_name, - rc.high_risk_suppliers::text AS metric_value, - CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status -FROM risk_counts rc -UNION ALL -SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, - CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END -FROM risk_counts rc -UNION ALL -SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' -FROM risk_counts rc -UNION ALL -SELECT 'pending_orders', os.pending_orders::text, - CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END -FROM order_stats os -UNION ALL -SELECT 'overdue_orders_30d', os.overdue_orders::text, - CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END -FROM order_stats os -UNION ALL -SELECT 'recent_returns_30d', rs.recent_returns::text, - CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END -FROM return_stats rs; - - --- ===================================================== --- 补充查询5：供应商风险排行榜 --- ===================================================== -WITH supplier_metrics AS ( - SELECT - s.supplier_id, - s.supplier_name, - s.supplier_category, - COUNT(DISTINCT po.purchase_order_id) AS order_count, - COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, - COUNT(DISTINCT pret.purchase_return_id) AS return_count, - SUM(pr.amount) AS total_amount - FROM dim_supplier s - LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id - LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id - LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id - WHERE s.is_current = 't' - GROUP BY s.supplier_id, s.supplier_name, s.supplier_category -), -ranked_suppliers AS ( - SELECT - *, - CASE WHEN receipt_count > 0 - THEN ROUND(return_count * 100.0 / receipt_count, 1) - ELSE 0 END AS return_rate, - ROW_NUMBER() OVER (ORDER BY - CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, - return_count DESC - ) AS risk_rank - FROM supplier_metrics -) -SELECT - risk_rank, - supplier_name, - supplier_category, - order_count, - receipt_count, - return_count, - return_rate, - ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, - CASE - WHEN return_rate > 10 THEN 'HIGH_RISK' - WHEN return_rate > 5 THEN 'MEDIUM_RISK' - WHEN return_count > 0 THEN 'LOW_RISK' - ELSE 'EXCELLENT' - END AS risk_assessment -FROM ranked_suppliers -ORDER BY risk_rank -LIMIT 20; diff --git a/lzwcai_mcpskills_mfg_data_agent/sql/工单执行进度与异常节点.sql b/lzwcai_mcpskills_mfg_data_agent/sql/工单执行进度与异常节点.sql deleted file mode 100644 index f7d3770..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/sql/工单执行进度与异常节点.sql +++ /dev/null @@ -1,409 +0,0 @@ --- ===================================================== --- 工单执行进度与异常节点SQL --- 数据库: PostgreSQL --- 实时拉取工单数据，动态映射订单各环节状态 --- ===================================================== - --- ===================================================== --- 1. 工单执行进度主视图 --- ===================================================== -WITH work_order_base AS ( - SELECT - wo.work_order_id, - wo.work_order_number, - wo.product_id, - wo.status, - wo.planned_qty, - wo.completed_qty, - wo.event_time_utc::timestamp AS start_time, - wo.last_updated_utc::timestamp AS last_update, - wo.source_system - FROM fact_work_order wo -), - -product_info AS ( - SELECT - product_id, - product_name, - product_category - FROM dim_product - WHERE is_current = 't' -), - -labor_summary AS ( - SELECT - work_order_number, - COUNT(DISTINCT worker_name) AS worker_count, - SUM(report_qty) AS total_report_qty, - SUM(duration_minutes) AS total_minutes, - MAX(event_time_utc::timestamp) AS last_report_time - FROM fact_labor_report - GROUP BY work_order_number -), - -quality_summary AS ( - SELECT - work_order_number, - SUM(pass_qty) AS pass_qty, - SUM(fail_qty) AS fail_qty - FROM fact_quality_inspection - GROUP BY work_order_number -), - --- ===================================================== --- 2. 工单进度计算与状态映射 --- ===================================================== -work_order_progress AS ( - SELECT - wb.work_order_id, - wb.work_order_number, - p.product_name, - p.product_category, - wb.status AS raw_status, - - -- 状态映射 - CASE wb.status - WHEN 'OPEN' THEN '待生产' - WHEN 'STARTED' THEN '生产中' - WHEN 'CLOSED' THEN '已完成' - ELSE '未知' - END AS status_name, - - wb.planned_qty, - wb.completed_qty, - - -- 完成进度 - CASE WHEN wb.planned_qty > 0 - THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) - ELSE 0 END AS completion_rate, - - -- 剩余数量 - GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, - - wb.start_time, - wb.last_update, - - -- 已用时间(小时) - ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, - - -- 报工信息 - COALESCE(ls.worker_count, 0) AS worker_count, - COALESCE(ls.total_report_qty, 0) AS total_report_qty, - COALESCE(ls.total_minutes, 0) AS total_work_minutes, - ls.last_report_time, - - -- 质检信息 - COALESCE(qs.pass_qty, 0) AS qc_pass_qty, - COALESCE(qs.fail_qty, 0) AS qc_fail_qty - - FROM work_order_base wb - LEFT JOIN product_info p ON wb.product_id = p.product_id - LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number - LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number -), - --- ===================================================== --- 3. 异常节点检测 --- ===================================================== -anomaly_detection AS ( - SELECT - *, - - -- 进度异常：进行中但完成率过低 - CASE - WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN '进度严重滞后' - WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN '进度滞后' - ELSE NULL - END AS progress_anomaly, - - -- 质量异常：废品率过高 - CASE - WHEN qc_pass_qty + qc_fail_qty > 0 - AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN '质量异常' - ELSE NULL - END AS quality_anomaly, - - -- 报工异常：长时间无报工 - CASE - WHEN raw_status = 'STARTED' - AND last_report_time IS NOT NULL - AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '报工停滞' - WHEN raw_status = 'STARTED' - AND last_report_time IS NULL - AND elapsed_hours > 24 THEN '无报工记录' - ELSE NULL - END AS labor_anomaly, - - -- 效率异常：人效过低 - CASE - WHEN total_work_minutes > 0 - AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN '效率偏低' - ELSE NULL - END AS efficiency_anomaly - - FROM work_order_progress -) - --- ===================================================== --- 输出：工单执行进度明细 --- ===================================================== -SELECT - work_order_number AS "工单号", - product_name AS "产品名称", - product_category AS "产品类别", - status_name AS "状态", - planned_qty AS "计划数量", - completed_qty AS "完成数量", - remaining_qty AS "剩余数量", - completion_rate AS "完成率(%)", - worker_count AS "参与人数", - ROUND(total_work_minutes / 60.0, 1) AS "累计工时(小时)", - elapsed_hours AS "已用时间(小时)", - - -- 异常标记 - COALESCE(progress_anomaly, '') || - CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || - COALESCE(quality_anomaly, '') || - CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || - COALESCE(labor_anomaly, '') || - CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || - COALESCE(efficiency_anomaly, '') AS "异常标记", - - -- 风险等级 - CASE - WHEN progress_anomaly = '进度严重滞后' OR quality_anomaly IS NOT NULL THEN '高' - WHEN progress_anomaly = '进度滞后' OR labor_anomaly IS NOT NULL THEN '中' - WHEN efficiency_anomaly IS NOT NULL THEN '低' - ELSE '-' - END AS "风险等级" - -FROM anomaly_detection -ORDER BY - CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, - CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, - completion_rate ASC; - - --- ===================================================== --- 补充查询1：工单状态分布汇总 --- ===================================================== -WITH status_summary AS ( - SELECT - CASE status - WHEN 'OPEN' THEN '待生产' - WHEN 'STARTED' THEN '生产中' - WHEN 'CLOSED' THEN '已完成' - ELSE '未知' - END AS status_name, - status AS raw_status, - COUNT(*) AS order_count, - SUM(planned_qty) AS total_planned, - SUM(completed_qty) AS total_completed - FROM fact_work_order - GROUP BY status -) -SELECT - status_name AS "状态", - order_count AS "工单数", - ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS "占比(%)", - ROUND(total_planned, 0) AS "计划总量", - ROUND(total_completed, 0) AS "完成总量", - CASE WHEN total_planned > 0 - THEN ROUND(total_completed * 100.0 / total_planned, 1) - ELSE 0 END AS "完成率(%)" -FROM status_summary -ORDER BY - CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END; - - --- ===================================================== --- 补充查询2：异常工单预警清单 --- ===================================================== -WITH work_order_anomaly AS ( - SELECT - wo.work_order_number, - p.product_name, - wo.status, - wo.planned_qty, - wo.completed_qty, - CASE WHEN wo.planned_qty > 0 - THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) - ELSE 0 END AS completion_rate, - ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, - (SELECT MAX(lr.event_time_utc::timestamp) - FROM fact_labor_report lr - WHERE lr.work_order_number = wo.work_order_number) AS last_report_time - FROM fact_work_order wo - LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' - WHERE wo.status = 'STARTED' -) -SELECT - work_order_number AS "工单号", - product_name AS "产品", - ROUND(planned_qty, 0) AS "计划数量", - ROUND(completed_qty, 0) AS "完成数量", - completion_rate AS "完成率(%)", - elapsed_hours AS "已用时间(小时)", - CASE - WHEN elapsed_hours > 48 AND completion_rate < 20 THEN '进度严重滞后' - WHEN elapsed_hours > 24 AND completion_rate < 30 THEN '进度滞后' - WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN '无报工记录' - WHEN last_report_time IS NOT NULL - AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '报工停滞' - ELSE '正常' - END AS "异常类型", - CASE - WHEN elapsed_hours > 48 AND completion_rate < 20 THEN '立即跟进，排查生产瓶颈' - WHEN elapsed_hours > 24 AND completion_rate < 30 THEN '关注进度，协调资源' - WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN '确认工单是否已开工' - WHEN last_report_time IS NOT NULL - AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '跟进报工情况' - ELSE '-' - END AS "处理建议" -FROM work_order_anomaly -WHERE elapsed_hours > 24 AND completion_rate < 50 - OR (last_report_time IS NULL AND elapsed_hours > 24) - OR (last_report_time IS NOT NULL - AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) -ORDER BY - CASE - WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 - WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 - ELSE 3 - END, - completion_rate ASC; - - --- ===================================================== --- 补充查询3：产品类别执行进度汇总 --- ===================================================== -SELECT - p.product_category AS "产品类别", - COUNT(*) AS "工单数", - SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS "待生产", - SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS "生产中", - SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS "已完成", - ROUND(SUM(wo.planned_qty), 0) AS "计划总量", - ROUND(SUM(wo.completed_qty), 0) AS "完成总量", - CASE WHEN SUM(wo.planned_qty) > 0 - THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) - ELSE 0 END AS "整体完成率(%)" -FROM fact_work_order wo -LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' -GROUP BY p.product_category -ORDER BY "工单数" DESC; - - --- ===================================================== --- 补充查询4：工单执行时间线 --- ===================================================== -WITH timeline AS ( - SELECT - wo.work_order_number, - p.product_name, - wo.status, - wo.event_time_utc::timestamp AS start_time, - wo.last_updated_utc::timestamp AS last_update, - CASE WHEN wo.status = 'CLOSED' - THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) - ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) - END AS duration_hours, - wo.planned_qty, - wo.completed_qty - FROM fact_work_order wo - LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' -) -SELECT - work_order_number AS "工单号", - product_name AS "产品", - CASE status - WHEN 'OPEN' THEN '待生产' - WHEN 'STARTED' THEN '生产中' - WHEN 'CLOSED' THEN '已完成' - END AS "状态", - TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS "开始时间", - TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS "最后更新", - duration_hours AS "持续时间(小时)", - ROUND(planned_qty, 0) AS "计划数量", - ROUND(completed_qty, 0) AS "完成数量", - CASE WHEN planned_qty > 0 - THEN ROUND(completed_qty * 100.0 / planned_qty, 1) - ELSE 0 END AS "完成率(%)" -FROM timeline -ORDER BY start_time DESC -LIMIT 50; - - --- ===================================================== --- 补充查询5：实时生产看板汇总 --- ===================================================== -WITH current_stats AS ( - SELECT - COUNT(*) AS total_orders, - SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, - SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, - SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, - SUM(planned_qty) AS total_planned, - SUM(completed_qty) AS total_completed - FROM fact_work_order -), -anomaly_stats AS ( - SELECT - COUNT(*) AS anomaly_count - FROM fact_work_order wo - WHERE wo.status = 'STARTED' - AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 - AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30 -), -today_stats AS ( - SELECT - COUNT(*) AS today_completed - FROM fact_work_order - WHERE status = 'CLOSED' - AND last_updated_utc::date = CURRENT_DATE -) -SELECT - '总工单数' AS "指标", - cs.total_orders::text AS "数值", - '-' AS "状态" -FROM current_stats cs -UNION ALL -SELECT - '待生产', - cs.open_orders::text, - CASE WHEN cs.open_orders > 20 THEN '积压' ELSE '正常' END -FROM current_stats cs -UNION ALL -SELECT - '生产中', - cs.started_orders::text, - '进行中' -FROM current_stats cs -UNION ALL -SELECT - '已完成', - cs.closed_orders::text, - '正常' -FROM current_stats cs -UNION ALL -SELECT - '整体完成率', - ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', - CASE - WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN '良好' - WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN '正常' - ELSE '偏低' - END -FROM current_stats cs -UNION ALL -SELECT - '异常工单数', - ans.anomaly_count::text, - CASE WHEN ans.anomaly_count > 5 THEN '需关注' ELSE '正常' END -FROM anomaly_stats ans -UNION ALL -SELECT - '今日完成', - ts.today_completed::text, - '-' -FROM today_stats ts; diff --git a/lzwcai_mcpskills_mfg_data_agent/sql/指标趋势分析与拐点预警.sql b/lzwcai_mcpskills_mfg_data_agent/sql/指标趋势分析与拐点预警.sql deleted file mode 100644 index 45f978d..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/sql/指标趋势分析与拐点预警.sql +++ /dev/null @@ -1,426 +0,0 @@ --- ===================================================== --- 指标趋势分析与拐点预警SQL --- Metric Trend Analysis and Turning Point Warning --- 数据库: PostgreSQL --- ===================================================== - --- ===================================================== --- 1. 日度指标基础数据 --- ===================================================== -WITH daily_metrics AS ( - SELECT - DATE(lr.event_time_utc::timestamp) AS metric_date, - COUNT(DISTINCT lr.worker_name) AS worker_count, - SUM(lr.duration_minutes) / 60.0 AS total_hours, - SUM(lr.report_qty) AS total_output, - CASE WHEN SUM(lr.duration_minutes) > 0 - THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) - ELSE 0 END AS hourly_efficiency - FROM fact_labor_report lr - GROUP BY DATE(lr.event_time_utc::timestamp) -), - -daily_quality AS ( - SELECT - DATE(qi.event_time_utc::timestamp) AS metric_date, - SUM(qi.pass_qty) AS pass_qty, - SUM(qi.fail_qty) AS fail_qty, - CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 - THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) - ELSE 0 END AS defect_rate - FROM fact_quality_inspection qi - GROUP BY DATE(qi.event_time_utc::timestamp) -), - -daily_production AS ( - SELECT - DATE(wo.event_time_utc::timestamp) AS metric_date, - SUM(wo.planned_qty) AS planned_qty, - SUM(wo.completed_qty) AS completed_qty, - CASE WHEN SUM(wo.planned_qty) > 0 - THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) - ELSE 0 END AS completion_rate - FROM fact_work_order wo - GROUP BY DATE(wo.event_time_utc::timestamp) -), - -combined_daily AS ( - SELECT - COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, - COALESCE(dm.worker_count, 0) AS worker_count, - COALESCE(dm.total_hours, 0) AS total_hours, - COALESCE(dm.total_output, 0) AS total_output, - COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, - COALESCE(dq.defect_rate, 0) AS defect_rate, - COALESCE(dp.completion_rate, 0) AS completion_rate - FROM daily_metrics dm - FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date - FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date - WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL -), - -numbered_data AS ( - SELECT - *, - ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq - FROM combined_daily -), - --- ===================================================== --- 2. 移动平均计算 (7日移动平均) --- ===================================================== -moving_avg_step1 AS ( - SELECT - metric_date, - day_seq, - worker_count, - total_output, - hourly_efficiency, - defect_rate, - completion_rate, - AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, - AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, - AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, - AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, - AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x - FROM numbered_data -), - -moving_avg AS ( - SELECT - *, - LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, - LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, - LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate - FROM moving_avg_step1 -), - --- ===================================================== --- 3. 简化的趋势斜率计算 --- ===================================================== -slope_calc AS ( - SELECT - *, - (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, - (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, - (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect - FROM moving_avg -), - --- ===================================================== --- 4. 趋势判断与异常检测 --- ===================================================== -trend_analysis AS ( - SELECT - metric_date, - hourly_efficiency, - total_output, - defect_rate, - completion_rate, - ROUND(ma7_efficiency, 2) AS ma7_efficiency, - ROUND(ma7_output, 2) AS ma7_output, - ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, - ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, - ROUND(COALESCE(slope_output, 0), 4) AS slope_output, - ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, - prev_ma7_efficiency, - prev_ma7_output, - prev_ma7_defect_rate, - - CASE - WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' - WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' - ELSE 'STABLE' - END AS efficiency_trend, - - CASE - WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' - WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' - ELSE 'STABLE' - END AS output_trend, - - CASE - WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' - WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' - ELSE 'STABLE' - END AS defect_trend, - - CASE - WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' - ELSE 'NORMAL' - END AS efficiency_status, - - CASE - WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' - ELSE 'NORMAL' - END AS output_status, - - CASE - WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' - ELSE 'NORMAL' - END AS defect_status - - FROM slope_calc -) - --- ===================================================== --- 输出：日度趋势分析明细 --- ===================================================== -SELECT - metric_date, - ROUND(hourly_efficiency, 2) AS hourly_output, - ma7_efficiency AS efficiency_ma7, - slope_efficiency, - efficiency_trend, - efficiency_status, - CASE - WHEN prev_ma7_efficiency IS NOT NULL - AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' - WHEN prev_ma7_efficiency IS NOT NULL - AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' - ELSE 'NONE' - END AS efficiency_turning_point, - ROUND(total_output, 0) AS daily_output, - ma7_output AS output_ma7, - slope_output, - output_trend, - output_status, - CASE - WHEN prev_ma7_output IS NOT NULL - AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' - WHEN prev_ma7_output IS NOT NULL - AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' - ELSE 'NONE' - END AS output_turning_point, - ROUND(defect_rate, 2) AS defect_rate, - ma7_defect_rate AS defect_rate_ma7, - slope_defect, - defect_trend, - defect_status, - CASE - WHEN prev_ma7_defect_rate IS NOT NULL - AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' - WHEN prev_ma7_defect_rate IS NOT NULL - AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' - ELSE 'NONE' - END AS defect_turning_point -FROM trend_analysis -ORDER BY metric_date DESC; - - --- ===================================================== --- 补充查询1：周度趋势汇总 --- ===================================================== -WITH weekly_metrics AS ( - SELECT - DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, - COUNT(DISTINCT lr.worker_name) AS worker_count, - SUM(lr.duration_minutes) / 60.0 AS total_hours, - SUM(lr.report_qty) AS total_output, - CASE WHEN SUM(lr.duration_minutes) > 0 - THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) - ELSE 0 END AS hourly_efficiency - FROM fact_labor_report lr - GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp) -), -weekly_quality AS ( - SELECT - DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, - SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate - FROM fact_quality_inspection qi - GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp) -), -weekly_combined AS ( - SELECT - wm.week_start, - wm.worker_count, - wm.total_hours, - wm.total_output, - wm.hourly_efficiency, - COALESCE(wq.defect_rate, 0) AS defect_rate - FROM weekly_metrics wm - LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start -), -weekly_with_lag AS ( - SELECT - *, - LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, - LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, - LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate - FROM weekly_combined -) -SELECT - week_start, - worker_count, - ROUND(total_hours, 1) AS total_hours, - ROUND(total_output, 0) AS total_output, - ROUND(hourly_efficiency, 2) AS hourly_output, - ROUND(defect_rate, 2) AS defect_rate, - CASE - WHEN prev_efficiency IS NULL THEN 'NONE' - WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' - WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' - ELSE 'STABLE' - END AS efficiency_trend, - CASE - WHEN prev_output IS NULL THEN 'NONE' - WHEN total_output > prev_output * 1.1 THEN 'RISING' - WHEN total_output < prev_output * 0.9 THEN 'FALLING' - ELSE 'STABLE' - END AS output_trend, - CASE - WHEN prev_defect_rate IS NULL THEN 'NONE' - WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' - WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' - ELSE 'STABLE' - END AS defect_trend, - ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct -FROM weekly_with_lag -ORDER BY week_start DESC; - - --- ===================================================== --- 补充查询2：指标趋势汇总报告 --- ===================================================== -WITH -recent_data AS ( - SELECT - DATE(lr.event_time_utc::timestamp) AS metric_date, - SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, - SUM(lr.report_qty) AS total_output - FROM fact_labor_report lr - WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' - GROUP BY DATE(lr.event_time_utc::timestamp) -), -recent_quality AS ( - SELECT - DATE(qi.event_time_utc::timestamp) AS metric_date, - SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate - FROM fact_quality_inspection qi - WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' - GROUP BY DATE(qi.event_time_utc::timestamp) -), -period_stats AS ( - SELECT - AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, - AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, - AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, - AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d - FROM recent_data rd -), -quality_stats AS ( - SELECT - AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, - AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d - FROM recent_quality rq -) -SELECT - 'efficiency_per_hour' AS metric_name, - ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, - ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, - ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, - CASE - WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' - WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' - ELSE 'STABLE' - END AS trend, - CASE - WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' - ELSE 'NORMAL' - END AS warning -FROM period_stats ps - -UNION ALL - -SELECT - 'daily_output', - ROUND(ps.avg_output_7d, 0), - ROUND(ps.avg_output_prev7d, 0), - ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), - CASE - WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' - WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' - ELSE 'STABLE' - END, - CASE - WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' - ELSE 'NORMAL' - END -FROM period_stats ps - -UNION ALL - -SELECT - 'defect_rate_pct', - ROUND(qs.avg_defect_7d, 2), - ROUND(qs.avg_defect_prev7d, 2), - ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), - CASE - WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' - WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' - ELSE 'STABLE' - END, - CASE - WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' - WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' - ELSE 'NORMAL' - END -FROM quality_stats qs; - - --- ===================================================== --- 补充查询3：拐点预警汇总 --- ===================================================== -WITH daily_eff AS ( - SELECT - DATE(lr.event_time_utc::timestamp) AS metric_date, - SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency - FROM fact_labor_report lr - GROUP BY DATE(lr.event_time_utc::timestamp) -), -with_ma_step1 AS ( - SELECT - metric_date, - hourly_efficiency, - AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 - FROM daily_eff -), -with_ma AS ( - SELECT - metric_date, - hourly_efficiency, - ma7, - LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, - LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 - FROM with_ma_step1 -), -turning_points AS ( - SELECT - metric_date, - hourly_efficiency, - ma7, - prev_ma7, - prev2_ma7, - CASE - WHEN prev2_ma7 IS NOT NULL - AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' - WHEN prev2_ma7 IS NOT NULL - AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' - ELSE NULL - END AS turning_type - FROM with_ma -) -SELECT - metric_date, - ROUND(hourly_efficiency, 2) AS daily_efficiency, - ROUND(ma7, 2) AS ma7_line, - turning_type, - CASE - WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' - WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' - ELSE 'NONE' - END AS recommendation -FROM turning_points -WHERE turning_type IS NOT NULL -ORDER BY metric_date DESC -LIMIT 10; diff --git a/lzwcai_mcpskills_mfg_data_agent/sql/订单延迟预警分析.sql b/lzwcai_mcpskills_mfg_data_agent/sql/订单延迟预警分析.sql deleted file mode 100644 index 77fc5b5..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/sql/订单延迟预警分析.sql +++ /dev/null @@ -1,154 +0,0 @@ --- 订单延迟预警分析（最终优化版） --- Order Delay Warning Analysis -WITH --- 1. 历史生产周期统计（全局平均，仅统计已完成工单） -production_cycle_stats AS ( - SELECT - COALESCE( - AVG( - GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc)) - ), - 0 - ) AS avg_production_days - FROM fact_work_order - WHERE status = 'CLOSED' - AND last_updated_utc >= event_time_utc -), - --- 2. 物流延误统计（按客户维度） -logistics_delay_stats AS ( - SELECT - customer_id, - AVG( - GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc)) - ) AS avg_logistics_delay_days, - SUM( - CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 - THEN 1 ELSE 0 END - ) AS delay_count - FROM fact_sales_shipment - WHERE doc_date_utc IS NOT NULL - AND event_time_utc IS NOT NULL - GROUP BY customer_id -), - --- 3. 质量问题统计（全局平均） -quality_issue_stats AS ( - SELECT - COALESCE( - ROUND( - SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), - 2 - ), - 0 - ) AS defect_rate_pct - FROM fact_quality_inspection - WHERE pass_qty IS NOT NULL - AND fail_qty IS NOT NULL -), - --- 4. 报废率（全局） -scrap_stats AS ( - SELECT - COALESCE( - (SELECT COUNT(*) FROM fact_scrap) * 100.0 / - NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), - 0 - ) AS scrap_rate_pct -), - --- 5. 当前生产滞后风险（全局） -active_work_order_risk AS ( - SELECT - COUNT(*) AS active_wo_count, - COALESCE( - SUM( - CASE - WHEN planned_qty > 0 - AND (completed_qty / planned_qty) < 0.3 - AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 - THEN 1 - ELSE 0 - END - ), - 0 - ) AS lagging_wo_count - FROM fact_work_order - WHERE status IN ('OPEN', 'STARTED') -), - --- 6. 合并全局指标为单行 -global_metrics AS ( - SELECT - pcs.avg_production_days, - qis.defect_rate_pct, - ss.scrap_rate_pct, - awr.active_wo_count, - awr.lagging_wo_count - FROM production_cycle_stats pcs, - quality_issue_stats qis, - scrap_stats ss, - active_work_order_risk awr -) - --- 主查询 -SELECT - so.sales_order_number AS order_number, - c.customer_name AS customer_name, - so.order_date_utc::DATE AS order_date, - so.deal_amount AS order_amount, - so.payment_status AS payment_status, - - -- 风险指标 - ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, - ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, - COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, - ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, - ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, - gm.active_wo_count::INT AS active_work_order_count, - gm.lagging_wo_count::INT AS lagging_work_order_count, - - -- 延迟概率计算 - ROUND( - LEAST(100, GREATEST(0, - LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + - LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + - LEAST(25, gm.defect_rate_pct * 2.5) + - LEAST(20, gm.lagging_wo_count * 10) - ))::NUMERIC, 1 - ) AS delay_probability_pct, - - -- 预警等级 - CASE - WHEN ( - LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + - LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + - LEAST(25, gm.defect_rate_pct * 2.5) + - LEAST(20, gm.lagging_wo_count * 10) - ) >= 60 THEN 'RED' - WHEN ( - LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + - LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + - LEAST(25, gm.defect_rate_pct * 2.5) + - LEAST(20, gm.lagging_wo_count * 10) - ) >= 30 THEN 'YELLOW' - ELSE 'GREEN' - END AS warning_level, - - -- 主要风险因素 - CASE - WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' - WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' - WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' - WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' - ELSE 'NORMAL' - END AS primary_risk_factor - -FROM fact_sales_order so -LEFT JOIN dim_customer c - ON so.customer_id = c.customer_id - AND c.is_current = 't' -CROSS JOIN global_metrics gm -LEFT JOIN logistics_delay_stats lds - ON so.customer_id = lds.customer_id -ORDER BY delay_probability_pct DESC, so.order_date_utc DESC; diff --git a/lzwcai_mcpskills_mfg_data_agent/test.py b/lzwcai_mcpskills_mfg_data_agent/test.py deleted file mode 100644 index 25ca3b2..0000000 --- a/lzwcai_mcpskills_mfg_data_agent/test.py +++ /dev/null @@ -1,111 +0,0 @@ -import requests -import json - -def main(**kwargs) -> dict: - """ - 函数节点的入口函数 - - Args: - **kwargs: 从前端配置的参数传入，可通过变量引用获取工作流上下文 - - Returns: - dict: 返回结果将作为节点输出，可被后续节点引用 - """ - # 从 kwargs 获取参数，如果没有提供则使用默认值 - url = kwargs.get('url', 'http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema') - - # 请求头（从kwargs获取或使用默认值） - authorization_token = kwargs.get('authorization_token', 'Bearer eyJhbGciOiJIUzUxMiJ9.eyJ0b2tlbl90eXBlIjoiTE9HSU4iLCJsb2dpbl91c2VyX2tleSI6IjAxNzZiNjEyLTc2YWItNDdhMS1iYTRiLTdjNWU2ZTMxNDlmZCJ9.w7aOfDJDHtA4bwNKIvUVK2cf1yO_2F27d_eYuos-p1-XGrSQOX0D4ny0b8Js36MhXwBnF4GDcy8V1VobEN6zBA') - headers = { - 'Authorization': authorization_token - } - - # 请求体参数（从kwargs获取或使用默认值） - payload_id = kwargs.get('id', '2006300000000000001') - business_name = kwargs.get('businessName', 'OrderDelayWarningAnalysis') - business_description = kwargs.get('businessDescription', '订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级') - datasource_id = kwargs.get('datasourceId', '57') - sql_template = kwargs.get('sqlTemplate', 'WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = \'CLOSED\' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = \'CLOSED\'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN (\'OPEN\', \'STARTED\')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN \'RED\' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN \'YELLOW\' ELSE \'GREEN\' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN \'PRODUCTION_SEVERELY_DELAYED\' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN \'HIGH_LOGISTICS_DELAY_RISK\' WHEN gm.avg_production_days > 15 THEN \'LONG_PRODUCTION_CYCLE\' WHEN gm.defect_rate_pct > 10 THEN \'QUALITY_ISSUES\' ELSE \'NORMAL\' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = \'t\' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id WHERE EXTRACT(YEAR FROM so.order_date_utc) = 2025 ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 30') - parameters = kwargs.get('parameters', {}) - - # 请求体（从kwargs获取或使用默认值） - payload = { - "id": payload_id, - "businessName": business_name, - "businessDescription": business_description, - "datasourceId": datasource_id, - "sqlTemplate": sql_template, - "parameters": parameters - } - - # 超时时间（从kwargs获取或使用默认值） - timeout = kwargs.get('timeout', 30) - - try: - # 发送POST请求 - response = requests.post( - url=url, - headers=headers, - json=payload, - timeout=timeout - ) - - # 检查HTTP响应状态码 - response.raise_for_status() - - # 解析响应结果 - result = response.json() - - # 返回接口调用结果 - return { - "status": "success", - "status_code": response.status_code, - "result": result - } - - except requests.exceptions.Timeout: - return { - "status": "error", - "error_type": "timeout", - "message": f"接口调用超时（{timeout}秒）", - "result": None - } - - except requests.exceptions.ConnectionError: - return { - "status": "error", - "error_type": "connection_error", - "message": "无法连接到目标服务器", - "result": None - } - - except requests.exceptions.HTTPError as e: - return { - "status": "error", - "error_type": "http_error", - "status_code": response.status_code if 'response' in locals() else None, - "message": f"HTTP请求错误: {str(e)}", - "response_text": response.text if 'response' in locals() else "", - "result": None - } - - except json.JSONDecodeError: - return { - "status": "error", - "error_type": "json_decode_error", - "message": "接口返回的不是有效的JSON格式", - "response_text": response.text if 'response' in locals() else "", - "result": None - } - - except Exception as e: - return { - "status": "error", - "error_type": "unknown_error", - "message": f"接口调用失败: {str(e)}", - "result": None - } - -if __name__ == "__main__": - result = main() - print(json.dumps(result, ensure_ascii=False, indent=2)) \ No newline at end of file diff --git a/lzwcai_mcpskills_mfg_data_agentv2/README.md b/lzwcai_mcpskills_mfg_data_agentv2/README.md deleted file mode 100644 index 46ac7be..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/README.md +++ /dev/null @@ -1,138 +0,0 @@ -# lzwcai-mcpskills-mfg-data-agent (制造业数据智能体) - -一个基于 MCP (Model Context Protocol) 的制造业数据智能体服务器，支持从 JSON 配置文件动态生成查询工具。 - -## 功能特性 - -- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具 -- 🔧 灵活配置：支持自定义业务查询和参数验证 -- 📝 完整日志：详细的操作日志记录 -- 🌐 中文支持：工具名称自动转换为拼音 -- 🏭 制造业场景：专注于制造业数据分析与智能决策 - -## 安装 - -### 使用 pip 安装 - -```bash -pip install lzwcai-mcpskills-mfg-data-agent -``` - -### 从源码安装 - -```bash -git clone -cd lzwcai_mcpskills_mfg_data_agent -pip install -e . -``` - -### 使用 uv 安装（推荐） - -```bash -uv pip install lzwcai-mcpskills-mfg-data-agent -``` - -## 使用方法 - -### 命令行启动 - -安装后，可以直接通过命令启动： - -```bash -lzwcai-mcpskills-mfg-data-agent -``` - -### 作为 Python 模块运行 - -```bash -python -m lzwcai_mcpskills_mfg_data_agent.main -``` - -### 配置到 MCP 客户端 - -在你的 MCP 客户端配置文件中添加： - -```json -{ - "mcpServers": { - "mfg-data-agent": { - "command": "lzwcai-mcpskills-mfg-data-agent" - } - } -} -``` - -## 配置说明 - -### businessQueries.json - -在 `businessQueries.json` 中定义你的业务查询： - -```json -[ - { - "id": "query-001", - "businessName": "生产订单查询", - "businessDescription": "根据订单ID查询生产订单信息", - "sqlTemplate": "SELECT * FROM production_orders WHERE order_id = {{orderId}}", - "parameters": { - "type": "object", - "required": ["orderId"], - "properties": { - "orderId": { - "type": "string", - "description": "生产订单的唯一标识符", - "examples": ["PO-2024-001"] - } - } - } - } -] -``` - -## 开发 - -### 依赖项 - -- Python >= 3.13 -- httpx >= 0.28.1 -- mcp[cli] >= 1.10.1 -- pypinyin >= 0.53.0 - -### 本地开发 - -```bash -# 克隆仓库 -git clone -cd lzwcai_mcpskills_mfg_data_agent - -# 安装开发依赖 -pip install -e . - -# 运行服务器 -python -m lzwcai_mcpskills_mfg_data_agent.main -``` - -## 构建与发布 - -### 使用 build 构建 - -```bash -pip install build -python -m build -``` - -### 发布到 PyPI - -```bash -pip install twine -twine upload dist/* -``` - -## 许可证 - -MIT License - -## 作者 - -lzwcai diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/.gitignore b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/.gitignore deleted file mode 100644 index 505a3b1..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/.gitignore +++ /dev/null @@ -1,10 +0,0 @@ -# Python-generated files -__pycache__/ -*.py[oc] -build/ -dist/ -wheels/ -*.egg-info - -# Virtual environments -.venv diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/.python-version b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/.python-version deleted file mode 100644 index 24ee5b1..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.13 diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/README.md b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/README.md deleted file mode 100644 index 0ec9671..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/README.md +++ /dev/null @@ -1,154 +0,0 @@ -# lzwcai-mcpskills-analyzeWorkOrder - -一个基于 MCP (Model Context Protocol) 的 SQL 查询执行服务器，支持从 JSON 配置文件动态生成查询工具。 - -## 功能特性 - -- 🚀 动态工具生成：从 `businessQueries.json` 自动生成 MCP 工具 -- 🔧 灵活配置：支持自定义业务查询和参数验证 -- 📝 完整日志：详细的操作日志记录（仅输出到文件，不干扰MCP通信） -- 🌐 中文支持：工具名称自动转换为拼音 - -## 安装 - -### 使用 pip 安装 - -```bash -pip install lzwcai-mcpskills-analyzeWorkOrder -``` - -### 从源码安装 - -```bash -git clone -cd lzwcai_mcp_sqlexecutor -pip install -e . -``` - -### 使用 uv 安装（推荐） - -```bash -uv pip install lzwcai-mcpskills-analyzeWorkOrder -``` - -## 使用方法 - -### 命令行启动 - -安装后，可以直接通过命令启动： - -```bash -lzwcai-mcpskills-analyzeWorkOrder -``` - -### 作为 Python 模块运行 - -```bash -python -m lzwcai_mcp_sqlexecutor.main -``` - -### 配置到 MCP 客户端 - -在你的 MCP 客户端配置文件中添加： - -```json -{ - "mcpServers": { - "lzwcai-sqlexecutor": { - "command": "lzwcai-mcpskills-analyzeWorkOrder" - } - } -} -``` - -## 配置说明 - -### businessQueries.json - -在 `businessQueries.json` 中定义你的业务查询： - -```json -[ - { - "id": "query-001", - "businessName": "用户订单查询", - "businessDescription": "根据用户ID查询订单信息", - "sqlTemplate": "SELECT * FROM orders WHERE user_id = {{userId}}", - "parameters": { - "type": "object", - "required": ["userId"], - "properties": { - "userId": { - "type": "integer", - "description": "用户的唯一标识符", - "examples": [10086] - } - } - } - } -] -``` - -## 开发 - -### 依赖项 - -- Python >= 3.13 -- httpx >= 0.28.1 -- mcp[cli] >= 1.10.1 -- pypinyin >= 0.53.0 - -### 本地开发 - -```bash -# 克隆仓库 -git clone -cd lzwcai_mcp_sqlexecutor - -# 安装开发依赖 -pip install -e . - -# 运行服务器 -python -m lzwcai_mcp_sqlexecutor.main -``` - -## 构建与发布 - -### 使用 build 构建 - -```bash -pip install build -python -m build -``` - -### 发布到 PyPI - -```bash -pip install twine -twine upload dist/* -``` - -## 常见问题 - -### MCP Inspector 显示 JSON 解析错误 - -如果在使用 MCP Inspector 测试时遇到 `SyntaxError: Unexpected non-whitespace character after JSON` 错误，这是因为： - -1. **原因**：MCP 协议使用 stdio（标准输入输出）进行 JSON-RPC 通信，任何输出到 stdout 的内容（如 print 语句或控制台日志）都会破坏 JSON 格式。 - -2. **解决方案**：本服务器已将所有日志输出配置为仅写入文件（位于 `logs/` 目录），不输出到控制台。日志文件包括： - - `lzwcai_mcp_sqlexecutor.log` - 主日志文件 - - `lzwcai_mcp_sqlexecutor_error.log` - 错误日志 - - `lzwcai_mcp_sqlexecutor_daily.log` - 按日期滚动的日志 - - `mcp_services.log` - MCP 服务专用日志 - -3. **查看日志**：如果需要调试，请查看 `logs/` 目录下的日志文件。 - -## 许可证 - -MIT License - -## 作者 - -lzwcai - diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/__init__.py b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/__init__.py deleted file mode 100644 index 790ee8d..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -""" -lzwcai-mcpskills-mfg-data-agentv2 - MCP server for manufacturing data intelligence -""" - -__version__ = "0.1.2" -__author__ = "lzwcai" - -__all__ = [] - diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json deleted file mode 100644 index b1c6f8e..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json +++ /dev/null @@ -1,50 +0,0 @@ -[ - { - "id": "2006300000000000002", - "businessName": "SupplierEvaluationAndSmartReplenishment", - "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。", - "datasourceId": "19", - "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' LIMIT 15 ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name LIMIT 15 ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000003", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "datasourceId": "19", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name LIMIT 15 ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE LIMIT 15 ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC LIMIT 15 ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000004", - "businessName": "SalesPerformanceIntelligentStatistics", - "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。", - "datasourceId": "19", - "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n LIMIT 15 ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000005", - "businessName": "FinancialAnalyticsDashboard", - "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。", - "datasourceId": "19", - "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym LIMIT 15 ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym LIMIT 15 ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d LIMIT 15 ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC LIMIT 15 ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000006", - "businessName": "SmartCostPredictionModel", - "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。", - "datasourceId": "19", - "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' LIMIT 15 ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code LIMIT 15 ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC LIMIT 15 ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC LIMIT 15", - "parameters": {} - }, - { - "id": "2006300000000000007", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "datasourceId": "19", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position LIMIT 15 ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 15 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 15 ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0)::numeric / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym LIMIT 15 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC LIMIT 15", - "parameters": {} - } -] \ No newline at end of file diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor.log b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor.log deleted file mode 100644 index 04d9750..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor.log +++ /dev/null @@ -1,496 +0,0 @@ -2026-01-09 18:44:45 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs -2026-01-09 18:44:45 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 18:44:46 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 18:44:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:44:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 18:44:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:44:47 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:44:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:49:19 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:49:19 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-09 18:49:19 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesPerformanceIntelligentStatistics", - "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。", - "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:49:19 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:49:19 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:49:24 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:49:24 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-09 18:49:24 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "FinancialAnalyticsDashboard", - "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。", - "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:49:24 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:49:24 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:49:27 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:49:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel -2026-01-09 18:49:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SmartCostPredictionModel", - "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。", - "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = 'RAW' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = 'RAW' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = 'RAW' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:49:27 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:49:27 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:57:52 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:57:52 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 18:57:52 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:57:52 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:57:52 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:58:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:58:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-09 18:58:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplierEvaluationAndSmartReplenishment", - "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。", - "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = 'RAW' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = 'RAW' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:58:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:58:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:58:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:58:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 18:58:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:58:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:58:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:02:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:02:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:02:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:02:47 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:02:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:31:56 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs -2026-01-09 19:31:56 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 19:31:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 19:32:01 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:01 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-09 19:32:01 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplierEvaluationAndSmartReplenishment", - "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。", - "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:01 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:01 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:32:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:04 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-09 19:32:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesPerformanceIntelligentStatistics", - "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。", - "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-09 19:32:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "FinancialAnalyticsDashboard", - "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。", - "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:16 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:16 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel -2026-01-09 19:32:16 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SmartCostPredictionModel", - "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。", - "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:16 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:16 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:32:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:33 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:33 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:34 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:34 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:34:32 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:34:32 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:34:32 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:34:32 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:34:32 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:48:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:48:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-09 19:48:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplierEvaluationAndSmartReplenishment", - "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。", - "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:48:17 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:48:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:57:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:57:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-09 19:57:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:57:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:57:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesPerformanceIntelligentStatistics", - "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。", - "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:57:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:57:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:57:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:02:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:02:14 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-09 20:02:14 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "FinancialAnalyticsDashboard", - "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。", - "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:02:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:02:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:06:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:06:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel -2026-01-09 20:06:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SmartCostPredictionModel", - "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。", - "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:06:58 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:06:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:23:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:23:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:23:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:23:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:23:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:26:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:26:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:26:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:26:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:26:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:26:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:26:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:26:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:38:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:38:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:38:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:38:58 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:38:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:43:32 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs -2026-01-09 20:43:32 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 20:43:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 20:43:35 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:43:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:43:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:43:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:43:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:03 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs -2026-01-16 12:40:03 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-16 12:40:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-16 12:40:05 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:05 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-16 12:40:05 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplierEvaluationAndSmartReplenishment", - "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。", - "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name LIMIT 30 ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code LIMIT 30 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:05 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:05 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:06 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:06 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-16 12:40:06 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name LIMIT 30 ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE LIMIT 30 ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC LIMIT 30 ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:06 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:06 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-16 12:40:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesPerformanceIntelligentStatistics", - "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。", - "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n LIMIT 30 ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC LIMIT 30 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:08 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:08 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-16 12:40:08 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:08 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:08 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "FinancialAnalyticsDashboard", - "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。", - "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d LIMIT 30 ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC LIMIT 30 ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym LIMIT 30 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:09 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:09 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:09 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:11 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:11 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-16 12:40:11 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position LIMIT 30 ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0)::numeric / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC LIMIT 30 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:11 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:11 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log deleted file mode 100644 index b786195..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log +++ /dev/null @@ -1,94 +0,0 @@ -2026-01-16 12:40:03 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs -2026-01-16 12:40:03 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-16 12:40:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-16 12:40:05 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:05 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-16 12:40:05 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplierEvaluationAndSmartReplenishment", - "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。", - "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name LIMIT 30 ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code LIMIT 30 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:05 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:05 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:06 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:06 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-16 12:40:06 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name LIMIT 30 ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE LIMIT 30 ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC LIMIT 30 ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:06 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:06 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-16 12:40:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesPerformanceIntelligentStatistics", - "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。", - "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n LIMIT 30 ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC LIMIT 30 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:08 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:08 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-16 12:40:08 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:08 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:08 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "FinancialAnalyticsDashboard", - "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。", - "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d LIMIT 30 ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC LIMIT 30 ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym LIMIT 30 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:09 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:09 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:09 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:11 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-16 12:40:11 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-16 12:40:11 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position LIMIT 30 ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0)::numeric / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC LIMIT 30 ;", - "parameters": {}, - "testParams": {} -} -2026-01-16 12:40:11 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-16 12:40:11 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-09 b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-09 deleted file mode 100644 index 2d05afb..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-09 +++ /dev/null @@ -1,402 +0,0 @@ -2026-01-09 18:44:45 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs -2026-01-09 18:44:45 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 18:44:46 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 18:44:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:44:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 18:44:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:44:47 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:44:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:49:19 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:49:19 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-09 18:49:19 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesPerformanceIntelligentStatistics", - "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。", - "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:49:19 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:49:19 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:49:24 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:49:24 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-09 18:49:24 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "FinancialAnalyticsDashboard", - "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。", - "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:49:24 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:49:24 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:49:27 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:49:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel -2026-01-09 18:49:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SmartCostPredictionModel", - "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。", - "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = 'RAW' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = 'RAW' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = 'RAW' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:49:27 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:49:27 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:57:52 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:57:52 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 18:57:52 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:57:52 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:57:52 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:58:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:58:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-09 18:58:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplierEvaluationAndSmartReplenishment", - "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。", - "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = 'RAW' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = 'RAW' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:58:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:58:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:58:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 18:58:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 18:58:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 18:58:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 18:58:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:02:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:02:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:02:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:02:47 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:02:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:31:56 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs -2026-01-09 19:31:56 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 19:31:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 19:32:01 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:01 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-09 19:32:01 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplierEvaluationAndSmartReplenishment", - "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。", - "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:01 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:01 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:32:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:04 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-09 19:32:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesPerformanceIntelligentStatistics", - "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。", - "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-09 19:32:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "FinancialAnalyticsDashboard", - "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。", - "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:16 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:16 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel -2026-01-09 19:32:16 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SmartCostPredictionModel", - "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。", - "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:16 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:16 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:32:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:32:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:33 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:32:33 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:32:34 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:32:34 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:32:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:34:32 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:34:32 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:34:32 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesBIIntelligentAnalyticsPlatform", - "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。", - "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:34:32 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:34:32 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:48:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:48:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-09 19:48:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SupplierEvaluationAndSmartReplenishment", - "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。", - "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:48:17 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:48:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:57:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 19:57:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-09 19:57:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:57:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 19:57:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SalesPerformanceIntelligentStatistics", - "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。", - "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 19:57:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 19:57:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 19:57:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:02:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:02:14 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-09 20:02:14 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "FinancialAnalyticsDashboard", - "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。", - "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:02:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:02:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:06:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:06:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel -2026-01-09 20:06:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "SmartCostPredictionModel", - "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。", - "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:06:58 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:06:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:23:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:23:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:23:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:23:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:23:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:26:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:26:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:26:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:26:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:26:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:26:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:26:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:26:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:38:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:38:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:38:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:38:58 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:38:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:43:32 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs -2026-01-09 20:43:32 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 20:43:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 20:43:35 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-09 20:43:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:43:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema -2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: { - "datasourceId": "19", - "businessName": "HumanResourcesAnalytics", - "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。", - "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;", - "parameters": {}, - "testParams": {} -} -2026-01-09 20:43:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 " -2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功 -2026-01-09 20:43:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_error.log b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_error.log deleted file mode 100644 index e69de29..0000000 diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/mcp_services.log b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/mcp_services.log deleted file mode 100644 index 886fc14..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/mcp_services.log +++ /dev/null @@ -1,148 +0,0 @@ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 18:44:45 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 18:44:46 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 18:44:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 18:44:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:44:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:49:19 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-09 18:49:19 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:49:19 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:49:24 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-09 18:49:24 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:49:24 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:49:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel -2026-01-09 18:49:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:49:27 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:57:52 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 18:57:52 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:57:52 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:58:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-09 18:58:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:58:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 18:58:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 18:58:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 18:58:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:02:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:02:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:02:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 19:31:56 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 19:31:58 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 19:32:01 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-09 19:32:01 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:01 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:32:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-09 19:32:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-09 19:32:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:16 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel -2026-01-09 19:32:16 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:16 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:32:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:32:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:32:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:34:32 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-09 19:34:32 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:34:32 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:48:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-09 19:48:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:48:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 19:57:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-09 19:57:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 19:57:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:02:14 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-09 20:02:14 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:02:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:06:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel -2026-01-09 20:06:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:06:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:23:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:23:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:23:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:26:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:26:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:26:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:38:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:38:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:38:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-09 20:43:32 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-09 20:43:33 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-09 20:43:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-09 20:43:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-09 20:43:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:313] - ============================================================ -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:316] - ============================================================ -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088 -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:324] - ============================================================ -2026-01-16 12:40:03 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接... -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:156] - 收到列出工具请求 -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）... -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置 -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条 -2026-01-16 12:40:04 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具 -2026-01-16 12:40:05 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment -2026-01-16 12:40:05 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:05 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:06 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform -2026-01-16 12:40:06 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:06 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics -2026-01-16 12:40:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:08 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard -2026-01-16 12:40:08 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:09 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 -2026-01-16 12:40:11 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics -2026-01-16 12:40:11 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API... -2026-01-16 12:40:11 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功 diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/main.py b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/main.py deleted file mode 100644 index 3a4efb3..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/main.py +++ /dev/null @@ -1,373 +0,0 @@ -from pathlib import Path -from typing import Any -import asyncio -import logging - -# 支持直接运行和模块导入两种方式 -try: - from .utils import load_json, generate_tool_name, generate_input_schema - from .utils import get_skill_by_id, DataSourceAPIClient, process_skill_response, test_sql_with_schema - from .utils import get_database_id, get_datasource_id, get_skill_id, get_env_config - from .utils.logger_config import logger_config -except ImportError: - from utils import load_json, generate_tool_name, generate_input_schema - from utils import get_skill_by_id, DataSourceAPIClient, process_skill_response, test_sql_with_schema - from utils import get_database_id, get_datasource_id, get_skill_id, get_env_config - from utils.logger_config import logger_config - -from mcp.server.models import InitializationOptions -from mcp.server import NotificationOptions, Server -import mcp.types as types - -# 初始化 MCP 专用日志器 -mcp_logger = logger_config.setup_mcp_logging() - -# ========== 数据源配置 ========== -# 数据源类型常量 -DATA_SOURCE_API = "api" # 仅使用API数据 -DATA_SOURCE_LOCAL = "local" # 仅使用本地JSON数据 -DATA_SOURCE_BOTH = "both" # 合并本地和API数据 - -# 默认数据源（可修改） -DEFAULT_DATA_SOURCE = DATA_SOURCE_LOCAL -# ================================ - - -def get_queries(): - """ - 获取业务查询配置 - - Returns: - list: 包含所有业务查询配置的列表 - """ - try: - # 获取当前文件所在目录 - current_dir = Path(__file__).parent - - # 构建 businessQueries.json 的路径 - json_path = current_dir / "businessQueries.json" - - mcp_logger.debug(f"正在读取业务查询配置文件: {json_path}") - - # 使用 load_json 方法读取 JSON 文件 - queries = load_json(json_path) - - mcp_logger.info(f"成功加载 {len(queries)} 个业务查询配置") - - return queries - except Exception as e: - mcp_logger.error(f"加载业务查询配置失败: {e}", exc_info=True) - raise - - -def generate_tool_schema_from_query(query: dict) -> types.Tool: - """ - 根据查询配置生成 MCP 工具模式 - - Args: - query: 单个查询配置字典 - - Returns: - types.Tool: MCP 工具对象 - """ - try: - # 获取参数定义并生成 inputSchema - parameters = query.get('parameters', {}) - input_schema = generate_input_schema(parameters) - - # 生成工具名称（格式: tool_拼音_id） - # tool_name = generate_tool_name(query['businessName'], query['id']) - tool_name = query['businessName'] - # 构建工具描述，包含业务名称和业务描述 - description = f"{query['businessName']}: {query['businessDescription']}" - - mcp_logger.debug(f"生成工具模式: {tool_name} - {query['businessName']}") - - return types.Tool( - name=tool_name, - description=description, - inputSchema=input_schema - ) - except Exception as e: - mcp_logger.error(f"生成工具模式失败: {query.get('id', 'unknown')}, 错误: {e}", exc_info=True) - raise - - -# 创建 MCP 服务器实例 -server = Server("lzwcai-mcpskills-mfg-data-agentv2") - -# 缓存查询配置，避免重复加载 -_queries_cache = None - - -async def get_queries_cache(source: str = None): - """ - 获取或初始化查询配置缓存 - - Args: - source: 数据源类型（默认使用 DEFAULT_DATA_SOURCE） - - "api": 仅使用API数据 - - "local": 仅使用本地JSON数据 - - "both": 合并本地和API数据 - - Returns: - 查询配置列表 - """ - global _queries_cache - if _queries_cache is None: - source = source or DEFAULT_DATA_SOURCE - mcp_logger.info(f"初始化查询配置（数据源: {source}）...") - - if source == DATA_SOURCE_LOCAL: - _queries_cache = get_queries() - mcp_logger.info(f"本地配置: {len(_queries_cache)} 条") - - elif source == DATA_SOURCE_API: - try: - _queries_cache = await call_third_party_api() - mcp_logger.info(f"API配置: {len(_queries_cache)} 条") - mcp_logger.info(f"API配置数组: {_queries_cache}") - except Exception as e: - mcp_logger.warning(f"API获取失败，降级使用本地配置: {e}") - _queries_cache = get_queries() - - else: # DATA_SOURCE_BOTH - local = get_queries() - try: - api = await call_third_party_api() - except Exception as e: - mcp_logger.warning(f"API获取失败: {e}") - api = [] - _queries_cache = local + api - mcp_logger.info(f"配置总数: {len(_queries_cache)} 条（本地{len(local)}+API{len(api)}）") - - return _queries_cache - - -@server.list_tools() -async def handle_list_tools() -> list[types.Tool]: - """ - 列出所有动态生成的 MCP 工具 - - Returns: - list[types.Tool]: 所有可用的工具列表 - """ - try: - mcp_logger.info("收到列出工具请求") - - queries = await get_queries_cache() - tools = [] - - for query in queries: - tool = generate_tool_schema_from_query(query) - tools.append(tool) - - mcp_logger.info(f"成功生成 {len(tools)} 个 MCP 工具") - mcp_logger.debug(f"工具列表: {[tool.name for tool in tools]}") - - return tools - except Exception as e: - mcp_logger.error(f"列出工具失败: {e}", exc_info=True) - raise - - -@server.call_tool() -async def handle_call_tool( - name: str, - arguments: dict[str, Any] | None -) -> list[types.TextContent]: - """ - 处理工具调用请求 - - Args: - name: 工具名称 - arguments: 工具参数 - - Returns: - list[types.TextContent]: 工具执行结果（返回参数和对应的接口配置） - """ - try: - mcp_logger.info(f"收到工具调用请求: {name}") - mcp_logger.debug(f"工具参数: {arguments}") - - # 获取查询配置缓存 - queries = await get_queries_cache() - - # 根据工具名称查找对应的 item（接口配置） - tool_item = None - for query in queries: - # tool_name = generate_tool_name(query['businessName'], query['id']) - tool_name = query['businessName'] - if tool_name == name: - tool_item = query - break - - # 构建返回结果 - import json - - if tool_item: - request_data = { - "datasourceId": get_datasource_id(), - "businessName": tool_item.get("businessName"), - "businessDescription": tool_item.get("businessDescription"), - "sqlTemplate": tool_item.get("sqlTemplate"), - "parameters": tool_item.get("parameters"), - "testParams": arguments or {} - } - - # 如果 arguments 中有 targetDatabaseName 且有值，添加到 request_data - if arguments and arguments.get("targetDatabaseName"): - request_data["targetDatabaseName"] = arguments["targetDatabaseName"] - mcp_logger.debug(f"添加目标数据库名称: {arguments['targetDatabaseName']}") - - # 调用测试SQL API - try: - mcp_logger.info("正在调用测试SQL API...") - api_response = test_sql_with_schema(request_data) - mcp_logger.info("测试SQL API调用成功") - - # 返回包含 data 字段的结果 - result = { - "success": True, - "data": api_response - } - result_text = json.dumps(result, ensure_ascii=False, indent=2) - - except Exception as e: - error_msg = f"调用测试SQL API失败: {str(e)}" - mcp_logger.error(error_msg, exc_info=True) - result = { - "success": False, - "error": error_msg, - "data": None - } - result_text = json.dumps(result, ensure_ascii=False, indent=2) - else: - error_msg = f"未找到工具 {name} 对应的配置" - result = { - "success": False, - "error": error_msg, - "data": None - } - result_text = json.dumps(result, ensure_ascii=False, indent=2) - - mcp_logger.debug(f"工具调用结果: {result_text}") - - return [ - types.TextContent( - type="text", - text=result_text - ) - ] - except Exception as e: - error_msg = f"工具调用失败: {name}, 错误: {e}" - mcp_logger.error(error_msg, exc_info=True) - return [ - types.TextContent( - type="text", - text=f"错误: {error_msg}" - ) - ] - - -async def call_third_party_api(skill_id: str = None) -> list: - """ - 调用第三方API获取技能信息并返回处理后的数据 - - Args: - skill_id: 技能ID（默认从环境变量 SKILL_ID 读取，如果未设置则使用 1981000305474482178） - - Returns: - 处理后的查询配置列表（businessQueries格式） - - Example: - queries = await call_third_party_api() - # 返回: [{"id": "...", "businessName": "...", ...}, ...] - """ - try: - # 如果没有传入 skill_id，则从环境变量读取 - if skill_id is None: - skill_id = get_skill_id() - - mcp_logger.info(f"调用第三方API，skill_id: {skill_id}") - - # 获取原始数据 - raw_result = get_skill_by_id(skill_id) - - mcp_logger.info(f"成功{raw_result}") - - # 处理并返回 - processed_queries = process_skill_response(raw_result) - - mcp_logger.info(f"成功获取并处理 {len(processed_queries)} 条数据") - return processed_queries - - except Exception as e: - mcp_logger.error(f"API调用失败: {e}", exc_info=True) - raise - - -async def async_main(): - """MCP 服务器异步主函数""" - try: - mcp_logger.info("=" * 60) - mcp_logger.info("正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder") - mcp_logger.info("版本: 0.1.0") - mcp_logger.info("=" * 60) - - # 输出环境配置信息 - env_config = get_env_config() - mcp_logger.info(f"环境配置 - Database ID: {env_config['database_id']}") - mcp_logger.info(f"环境配置 - Datasource ID: {env_config['datasource_id']}") - mcp_logger.info(f"环境配置 - Skill ID: {env_config['skill_id']}") - mcp_logger.info(f"环境配置 - Backend Base URL: {env_config['backend_base_url']}") - mcp_logger.info("=" * 60) - - from mcp.server.stdio import stdio_server - - async with stdio_server() as (read_stream, write_stream): - mcp_logger.info("MCP 服务器已启动，等待客户端连接...") - - await server.run( - read_stream, - write_stream, - InitializationOptions( - server_name="lzwcai-mcpskills-analyzeOrder", - server_version="0.1.0", - capabilities=server.get_capabilities( - notification_options=NotificationOptions(), - experimental_capabilities={}, - ), - ), - ) - - mcp_logger.info("MCP 服务器已关闭") - - except Exception as e: - mcp_logger.error(f"MCP 服务器运行失败: {e}", exc_info=True) - raise - - -def main(): - """入口点函数（用于 console_scripts）""" - try: - # 初始化系统日志 - # MCP协议使用stdio通信，必须禁用控制台输出以避免干扰JSON-RPC通信 - logger_config.setup_logging( - app_name="lzwcai_mcp_sqlexecutor", - log_level=logging.INFO, - console_output=False # 禁用控制台输出 - ) - - mcp_logger.info("开始运行 MCP SQL Executor 服务器") - asyncio.run(async_main()) - - except KeyboardInterrupt: - mcp_logger.info("收到中断信号，正在关闭服务器...") - except Exception as e: - mcp_logger.error(f"程序运行失败: {e}", exc_info=True) - raise - - -if __name__ == "__main__": - main() diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml deleted file mode 100644 index ecfcf7e..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml +++ /dev/null @@ -1,35 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai-mcpskills-mfg-data-agentv2" -version = "0.1.1" -description = "制造业数据智能体 - MCP server for manufacturing data intelligence with dynamic tool generation" -readme = "README.md" -requires-python = ">=3.13" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "sql", "manufacturing", "data", "agent", "智能体"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.13", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", - "pypinyin>=0.53.0", -] - -[project.scripts] -lzwcai-mcpskills-mfg-data-agentv2 = "lzwcai_mcpskills_mfg_data_agentv2.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcpskills_mfg_data_agentv2"] - -[tool.hatch.build.targets.wheel.force-include] -"lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json" = "lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json" diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/sql11 b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/sql11 deleted file mode 100644 index 39b7319..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/sql11 +++ /dev/null @@ -1,169 +0,0 @@ --- ===================================================== --- 交付风险预测：延迟概率与红/黄/绿预警等级 --- 基于历史订单的生产周期、物流延误、设备故障等特征 --- ===================================================== - -WITH --- 1. 全局生产特征（汇总所有工单） -global_production AS ( - SELECT - COUNT(*) AS total_wo_count, - AVG(CASE WHEN planned_qty > 0 THEN completed_qty / planned_qty ELSE 0 END) AS avg_completion_rate, - SUM(CASE WHEN status IN ('OPEN', 'STARTED') THEN 1 ELSE 0 END) AS pending_wo_count, - SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_wo_count - FROM fact_work_order -), - --- 2. 全局质检特征 -global_quality AS ( - SELECT - COUNT(*) AS total_inspection_count, - SUM(COALESCE(pass_qty, 0)) AS total_pass_qty, - SUM(COALESCE(fail_qty, 0)) AS total_fail_qty, - CASE WHEN SUM(COALESCE(pass_qty, 0) + COALESCE(fail_qty, 0)) > 0 - THEN SUM(COALESCE(pass_qty, 0))::FLOAT / SUM(COALESCE(pass_qty, 0) + COALESCE(fail_qty, 0)) - ELSE 1 END AS qc_pass_rate - FROM fact_quality_inspection -), - --- 3. 全局工序不良特征 -global_operation AS ( - SELECT - COUNT(*) AS total_task_count, - SUM(COALESCE(good_qty, 0)) AS total_good_qty, - SUM(COALESCE(bad_qty, 0)) AS total_bad_qty, - CASE WHEN SUM(COALESCE(good_qty, 0) + COALESCE(bad_qty, 0)) > 0 - THEN SUM(COALESCE(bad_qty, 0))::FLOAT / SUM(COALESCE(good_qty, 0) + COALESCE(bad_qty, 0)) - ELSE 0 END AS operation_defect_rate - FROM fact_operation_task -), - --- 4. 客户级别发货统计 -customer_shipment AS ( - SELECT - customer_id, - COUNT(*) AS shipment_count, - SUM(COALESCE(amount, 0)) AS total_shipment_amount - FROM fact_sales_shipment - GROUP BY customer_id -), - --- 5. 客户级别退货统计 -customer_return AS ( - SELECT - customer_id, - COUNT(*) AS return_count, - SUM(COALESCE(amount, 0)) AS total_return_amount - FROM fact_sales_return - GROUP BY customer_id -), - --- 6. 订单风险评估 -order_risk AS ( - SELECT - so.sales_order_id, - so.sales_order_number, - c.customer_name, - so.order_date_utc, - so.deal_amount, - so.payment_status, - - -- 全局生产指标 - gp.avg_completion_rate AS production_completion_rate, - gp.pending_wo_count, - gp.total_wo_count AS work_order_count, - - -- 全局质检指标 - gq.qc_pass_rate, - gq.total_fail_qty, - - -- 全局工序指标 - go.operation_defect_rate, - - -- 客户级别指标 - COALESCE(cs.shipment_count, 0) AS shipment_count, - COALESCE(cr.return_count, 0) AS return_count, - CASE WHEN COALESCE(cs.shipment_count, 0) > 0 - THEN COALESCE(cr.return_count, 0)::FLOAT / cs.shipment_count - ELSE 0 END AS return_rate - - FROM fact_sales_order so - LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = true - CROSS JOIN global_production gp - CROSS JOIN global_quality gq - CROSS JOIN global_operation go - LEFT JOIN customer_shipment cs ON so.customer_id = cs.customer_id - LEFT JOIN customer_return cr ON so.customer_id = cr.customer_id -) - --- 7. 最终输出 -SELECT - sales_order_id, - sales_order_number, - customer_name, - order_date_utc, - deal_amount, - payment_status, - - -- 风险特征 - work_order_count, - ROUND(production_completion_rate::NUMERIC, 2) AS production_completion_rate, - pending_wo_count, - ROUND(qc_pass_rate::NUMERIC, 2) AS qc_pass_rate, - ROUND(operation_defect_rate::NUMERIC, 4) AS operation_defect_rate, - return_count, - ROUND(return_rate::NUMERIC, 4) AS return_rate, - - -- 延迟概率 - ROUND(( - CASE WHEN production_completion_rate < 0.3 THEN 0.30 - WHEN production_completion_rate < 0.5 THEN 0.20 - WHEN production_completion_rate < 0.8 THEN 0.10 - ELSE 0 END - + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 - WHEN qc_pass_rate < 0.9 THEN 0.15 - WHEN qc_pass_rate < 0.95 THEN 0.08 - ELSE 0 END - + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 - WHEN operation_defect_rate > 0.05 THEN 0.12 - WHEN operation_defect_rate > 0.02 THEN 0.05 - ELSE 0 END - + CASE WHEN return_rate > 0.1 THEN 0.15 - WHEN return_rate > 0.05 THEN 0.08 - WHEN return_rate > 0.02 THEN 0.03 - ELSE 0 END - + CASE WHEN payment_status = 'UNPAID' THEN 0.10 - WHEN payment_status = 'PARTIAL' THEN 0.05 - ELSE 0 END - )::NUMERIC, 2) AS delay_probability, - - -- 红/黄/绿预警 - CASE - WHEN ( - CASE WHEN production_completion_rate < 0.3 THEN 0.30 WHEN production_completion_rate < 0.5 THEN 0.20 WHEN production_completion_rate < 0.8 THEN 0.10 ELSE 0 END - + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 WHEN qc_pass_rate < 0.9 THEN 0.15 WHEN qc_pass_rate < 0.95 THEN 0.08 ELSE 0 END - + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 WHEN operation_defect_rate > 0.05 THEN 0.12 WHEN operation_defect_rate > 0.02 THEN 0.05 ELSE 0 END - + CASE WHEN return_rate > 0.1 THEN 0.15 WHEN return_rate > 0.05 THEN 0.08 WHEN return_rate > 0.02 THEN 0.03 ELSE 0 END - + CASE WHEN payment_status = 'UNPAID' THEN 0.10 WHEN payment_status = 'PARTIAL' THEN 0.05 ELSE 0 END - ) >= 0.50 THEN 'RED' - WHEN ( - CASE WHEN production_completion_rate < 0.3 THEN 0.30 WHEN production_completion_rate < 0.5 THEN 0.20 WHEN production_completion_rate < 0.8 THEN 0.10 ELSE 0 END - + CASE WHEN qc_pass_rate < 0.8 THEN 0.25 WHEN qc_pass_rate < 0.9 THEN 0.15 WHEN qc_pass_rate < 0.95 THEN 0.08 ELSE 0 END - + CASE WHEN operation_defect_rate > 0.1 THEN 0.20 WHEN operation_defect_rate > 0.05 THEN 0.12 WHEN operation_defect_rate > 0.02 THEN 0.05 ELSE 0 END - + CASE WHEN return_rate > 0.1 THEN 0.15 WHEN return_rate > 0.05 THEN 0.08 WHEN return_rate > 0.02 THEN 0.03 ELSE 0 END - + CASE WHEN payment_status = 'UNPAID' THEN 0.10 WHEN payment_status = 'PARTIAL' THEN 0.05 ELSE 0 END - ) >= 0.25 THEN 'YELLOW' - ELSE 'GREEN' - END AS risk_level, - - -- 风险原因 - CONCAT_WS(' | ', - CASE WHEN production_completion_rate < 0.5 THEN '生产进度滞后' END, - CASE WHEN qc_pass_rate < 0.9 THEN '质检通过率低' END, - CASE WHEN operation_defect_rate > 0.05 THEN '工序不良率高' END, - CASE WHEN return_rate > 0.05 THEN '历史退货率高' END, - CASE WHEN payment_status = 'UNPAID' THEN '未付款' END - ) AS risk_reasons - -FROM order_risk -ORDER BY delay_probability DESC, deal_amount DESC; diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/__init__.py b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/__init__.py deleted file mode 100644 index 3df2b44..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Utils package for lzwcai_mcp_sqlexecutor""" - -from .json_helper import load_json -from .name_helper import generate_tool_name -from .schema_helper import generate_input_schema, validate_input_schema -from .api_client import DataSourceAPIClient, get_skill_by_id, process_skill_response, test_sql_with_schema -from .env_config import get_database_id, get_datasource_id, get_skill_id, get_backend_base_url, get_env_config, set_env_variable - -__all__ = [ - 'load_json', - 'generate_tool_name', - 'generate_input_schema', - 'validate_input_schema', - 'DataSourceAPIClient', - 'get_skill_by_id', - 'process_skill_response', - 'test_sql_with_schema', - 'get_database_id', - 'get_datasource_id', - 'get_skill_id', - 'get_backend_base_url', - 'get_env_config', - 'set_env_variable' -] - diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/api_client.py b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/api_client.py deleted file mode 100644 index 279d800..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/api_client.py +++ /dev/null @@ -1,332 +0,0 @@ -""" -第三方API调用客户端 -用于调用外部数据源接口 -""" - -import httpx -import logging -import json -from typing import Dict, Any, Optional, List - -# 支持直接运行和模块导入两种方式 -try: - from .env_config import get_backend_base_url -except ImportError: - from env_config import get_backend_base_url - -# 获取日志记录器 -logger = logging.getLogger(__name__) - - -class DataSourceAPIClient: - """数据源API客户端""" - - def __init__( - self, - base_url: Optional[str] = None, - token: Optional[str] = None - ): - """ - 初始化API客户端 - - Args: - base_url: API基础URL（默认从环境变量 BACKEND_BASE_URL 读取，如果未设置则使用 http://192.168.2.236:8088） - token: 认证令牌（Bearer Token） - """ - # 如果没有传入 base_url，则从环境变量读取 - if base_url is None: - base_url = get_backend_base_url() - - self.base_url = base_url.rstrip('/') - self.token = token or "eyJhbGciOiJIUzUxMiJ9.eyJsb2dpbl91c2VyX2tleSI6IjJiYTk4ODllLWM2ZGItNDQ5YS1iZmFjLTQ2YzMxODFlODg5NCJ9.dvi8zm0LsWvJ_h9zD5blnHFRxa4z4_WBm1R487ekE7HlHzrN6dnvqhK8askqT5b1EcE8myHwRzLVMoI8UOjOrw" - self.client = httpx.Client(timeout=30.0) - - def _get_headers(self) -> Dict[str, str]: - """ - 获取请求头 - - Returns: - 请求头字典 - """ - return { - 'Authorization': f'Bearer {self.token}', - } - - def get_skill_by_id(self, skill_id: str) -> Dict[str, Any]: - """ - 根据技能ID获取技能信息 - - Args: - skill_id: 技能ID - - Returns: - API响应数据 - - Raises: - Exception: 请求失败时抛出 - """ - try: - url = f"{self.base_url}/datasource/skill/getBySkillId/{skill_id}" - - logger.info(f"正在调用API: {url}") - logger.info(f"请求参数 - skill_id: {skill_id}") - - response = self.client.get( - url, - headers=self._get_headers() - ) - - # 检查HTTP状态码 - response.raise_for_status() - - # 解析JSON响应 - data = response.json() - - logger.info(f"API调用成功: {url}") - logger.debug(f"响应数据: {data}") - - return data - - except httpx.TimeoutException: - error_msg = f"API请求超时: {url}" - logger.error(error_msg) - raise Exception(error_msg) - - except httpx.HTTPStatusError as e: - error_msg = f"API请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(error_msg) - logger.error(f"错误响应: {e.response.text}") - raise Exception(error_msg) - - except httpx.RequestError as e: - error_msg = f"API请求异常: {url}, 错误: {str(e)}" - logger.error(error_msg) - raise Exception(error_msg) - - except Exception as e: - error_msg = f"处理API响应时出错: {str(e)}" - logger.error(error_msg, exc_info=True) - raise Exception(error_msg) - - def test_sql_with_schema(self, request_data: Dict[str, Any]) -> Dict[str, Any]: - """ - 测试SQL语句并返回执行结果 - - Args: - request_data: 请求数据，包含以下字段： - - datasourceId: 数据源ID - - businessName: 业务名称 - - businessDescription: 业务描述 - - sqlTemplate: SQL模板 - - parameters: 参数定义 - - testParams: 测试参数 - - Returns: - API响应数据 - - Raises: - Exception: 请求失败时抛出 - """ - try: - url = f"{self.base_url}/datasource/sqlExecutionLog/testBatchSqlWithSchema" - - # 构建请求头（包含Content-Type） - headers = self._get_headers() - headers['Content-Type'] = 'application/json' - headers['Accept'] = '*/*' - - logger.info(f"正在调用测试SQL API: {url}") - logger.info(f"请求参数: {json.dumps(request_data, ensure_ascii=False, indent=2)}") - - # 发送POST请求 - response = self.client.post( - url, - headers=headers, - json=request_data - ) - - # 检查HTTP状态码 - response.raise_for_status() - - # 解析JSON响应 - result = response.json() - - logger.info(f"测试SQL API调用成功") - logger.debug(f"响应数据: {json.dumps(result, ensure_ascii=False, indent=2)}") - - # 处理返回数据结构: {code, data: [{errorMessage, data}, ...], msg} - # 检查外层 code - if result.get("code") != 200: - error_msg = result.get("msg", "接口返回错误") - logger.error(f"接口返回错误: code={result.get('code')}, msg={error_msg}") - raise Exception(error_msg) - - # data 是一个数组，每个元素包含 errorMessage 和 data - data_list = result.get("data", []) - - # 如果不是数组，兼容旧格式 - if isinstance(data_list, dict): - if data_list.get("errorMessage"): - error_msg = data_list.get("errorMessage") - logger.error(f"接口业务错误: {error_msg}") - raise Exception(error_msg) - return data_list.get("data") - - # 处理数组格式，提取每个 item 的 data 并组合 - combined_data = [] - for item in data_list: - if item.get("errorMessage"): - error_msg = item.get("errorMessage") - logger.error(f"接口业务错误: {error_msg}") - raise Exception(error_msg) - item_data = item.get("data", []) - combined_data.append(item_data) - - # 返回组合后的数据 [data1, data2, ...] - return combined_data - - except httpx.TimeoutException: - error_msg = f"测试SQL API请求超时: {url}" - logger.error(error_msg) - raise Exception(error_msg) - - except httpx.HTTPStatusError as e: - error_msg = f"测试SQL API请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(error_msg) - logger.error(f"错误响应: {e.response.text}") - raise Exception(error_msg) - - except httpx.RequestError as e: - error_msg = f"测试SQL API请求异常: {url}, 错误: {str(e)}" - logger.error(error_msg) - raise Exception(error_msg) - - except Exception as e: - error_msg = f"处理测试SQL API响应时出错: {str(e)}" - logger.error(error_msg, exc_info=True) - raise Exception(error_msg) - - def close(self): - """关闭HTTP客户端""" - self.client.close() - - -# 创建默认客户端实例 -default_client = DataSourceAPIClient() - - -def get_skill_by_id(skill_id: str, base_url: Optional[str] = None, token: Optional[str] = None) -> Dict[str, Any]: - """ - 便捷函数：根据技能ID获取技能信息 - - Args: - skill_id: 技能ID - base_url: API基础URL（可选，默认从环境变量 BACKEND_BASE_URL 读取） - token: 认证令牌（可选，使用默认值） - - Returns: - API响应数据 - """ - if base_url or token: - client = DataSourceAPIClient( - base_url=base_url, - token=token - ) - return client.get_skill_by_id(skill_id) - else: - return default_client.get_skill_by_id(skill_id) - - -def test_sql_with_schema(request_data: Dict[str, Any], base_url: Optional[str] = None, token: Optional[str] = None) -> Dict[str, Any]: - """ - 便捷函数：测试SQL语句并返回执行结果 - - Args: - request_data: 请求数据，包含以下字段： - - datasourceId: 数据源ID - - businessName: 业务名称 - - businessDescription: 业务描述 - - sqlTemplate: SQL模板 - - parameters: 参数定义 - - testParams: 测试参数 - base_url: API基础URL（可选，默认从环境变量 BACKEND_BASE_URL 读取） - token: 认证令牌（可选，使用默认值） - - Returns: - API响应数据 - """ - if base_url or token: - client = DataSourceAPIClient( - base_url=base_url, - token=token - ) - return client.test_sql_with_schema(request_data) - else: - return default_client.test_sql_with_schema(request_data) - - -def process_skill_response(response: Dict[str, Any]) -> List[Dict[str, Any]]: - """ - 处理API响应数据，映射为businessQueries格式 - - Args: - response: API原始响应数据 - - Returns: - 处理后的查询配置列表 - """ - try: - # 提取data数组 - data_list = response.get("data", []) - - # 默认的员工ID参数schema - default_employee_schema = { - "type": "object", - "required": ["employeeId"], - "properties": { - "employeeId": { - "type": "number", - "description": "员工ID，用于标识员工的唯一数字标识符", - "examples": [1001, 2002] - } - } - } - - # 映射每个skill为businessQuery格式 - queries = [] - for skill in data_list: - # 解析sqlParams字符串为JSON对象 - sql_params = json.loads(skill.get("sqlParams", "{}")) - - # 判断sqlParams是否为空对象 - is_empty_params = ( - not sql_params.get("properties") or - len(sql_params.get("properties", {})) == 0 - ) and ( - not sql_params.get("required") or - len(sql_params.get("required", [])) == 0 - ) - - # 如果是空对象，使用默认的员工ID参数 - if is_empty_params: - logger.info(f"技能 {skill.get('name')} (ID: {skill.get('id')}) 的sqlParams为空，使用默认员工ID参数") - sql_params = default_employee_schema - - # 映射字段 - query = { - "id": skill.get("id"), - "businessName": skill.get("name"), - "businessDescription": skill.get("description"), - "sqlTemplate": skill.get("sqlTemplate"), - "parameters": sql_params, - "datasourceId": skill.get("datasourceId") - } - queries.append(query) - - logger.info(f"成功处理 {len(queries)} 条技能数据") - return queries - - except Exception as e: - logger.error(f"处理API响应数据失败: {e}", exc_info=True) - raise - diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/env_config.py b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/env_config.py deleted file mode 100644 index 585b0a6..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/env_config.py +++ /dev/null @@ -1,106 +0,0 @@ -"""环境变量配置模块""" - -import os -from typing import Optional - - -def get_database_id(default: str = "29") -> str: - """ - 获取数据库ID环境变量 - - Args: - default: 默认值（默认为 "29"） - - Returns: - str: 数据库ID - - Environment Variables: - databaseId: 数据库ID - """ - return os.environ.get("databaseId", default) - - -def get_datasource_id(default: str = "") -> str: - """ - 获取数据源ID环境变量 - - Args: - default: 默认值（默认为 ""） - - Returns: - str: 数据源ID - - Environment Variables: - datasourceId: 数据源ID - """ - return os.environ.get("datasourceId", default) - - -def get_skill_id(default: str = "") -> str: - """ - 获取技能ID环境变量 - - Args: - default: 默认值（默认为 ""） - - Returns: - str: 技能ID - - Environment Variables: - skillId: 技能ID - """ - return os.environ.get("skillId", default) - - -def get_backend_base_url(default: str = "http://lzwcai-demp-corp-manager:8086") -> str: - """ - 获取后端API基础URL环境变量 - - Args: - default: 默认值（默认为 "http://lzwcai-demp-corp-manager:8086"） - - Returns: - str: 后端API基础URL - - Environment Variables: - backendBaseUrl: 后端API基础URL - """ - return os.environ.get("backendBaseUrl", default) - - -def get_env_config() -> dict: - """ - 获取所有环境配置 - - Returns: - dict: 包含所有配置的字典 - - Example: - config = get_env_config() - print(config['database_id']) # 输出: "29" - print(config['datasource_id']) # 输出: "" - print(config['skill_id']) # 输出: "" - print(config['backend_base_url']) # 输出: "http://lzwcai-demp-corp-manager:8086" - """ - return { - "database_id": get_database_id(), - "datasource_id": get_datasource_id(), - "skill_id": get_skill_id(), - "backend_base_url": get_backend_base_url() - } - - -def set_env_variable(key: str, value: str) -> None: - """ - 设置环境变量（仅在当前进程中有效） - - Args: - key: 环境变量名 - value: 环境变量值 - - Example: - set_env_variable("databaseId", "30") - set_env_variable("skillId", "1234567890") - """ - os.environ[key] = value - diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/json_helper.py b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/json_helper.py deleted file mode 100644 index 1a6fc95..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/json_helper.py +++ /dev/null @@ -1,60 +0,0 @@ -"""JSON 文件读取工具""" - -import json -from pathlib import Path -from typing import Any, Union - - -def load_json(json_path: Union[str, Path]) -> Any: - """ - 读取 JSON 文件并返回其内容 - - Args: - json_path: JSON 文件的路径（支持字符串或 Path 对象） - - Returns: - JSON 文件中解析后的数据（可以是字典、列表或其他 JSON 类型） - - Raises: - FileNotFoundError: 当文件不存在时 - json.JSONDecodeError: 当 JSON 格式无效时 - Exception: 其他读取错误 - - Example: - >>> data = load_json('config.json') - >>> print(data) - {'key': 'value'} - - >>> data = load_json(Path('data/users.json')) - >>> print(data) - [{'id': 1, 'name': 'Alice'}] - """ - try: - # 转换为 Path 对象 - path = Path(json_path) - - # 检查文件是否存在 - if not path.exists(): - raise FileNotFoundError(f"JSON 文件不存在: {json_path}") - - # 检查是否为文件 - if not path.is_file(): - raise ValueError(f"路径不是一个文件: {json_path}") - - # 读取并解析 JSON 文件 - with open(path, 'r', encoding='utf-8') as f: - data = json.load(f) - - return data - - except json.JSONDecodeError as e: - raise json.JSONDecodeError( - f"JSON 格式错误: {e.msg}", - e.doc, - e.pos - ) - except FileNotFoundError: - raise - except Exception as e: - raise Exception(f"读取 JSON 文件时发生错误: {str(e)}") - diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/logger_config.py b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/logger_config.py deleted file mode 100644 index 290964f..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/logger_config.py +++ /dev/null @@ -1,489 +0,0 @@ -# -*- coding: utf-8 -*- -""" -统一日志配置模块 -提供系统级别的日志配置和管理 -""" - -import os -import sys -import logging -from logging.handlers import RotatingFileHandler, TimedRotatingFileHandler -from datetime import datetime -from pathlib import Path - - -class LoggerConfig: - """日志配置管理类""" - - def __init__(self, logs_dir: str = None): - """初始化日志配置 - - Args: - logs_dir: 日志目录路径，默认为项目根目录下的logs文件夹 - """ - # 确定日志目录 - if logs_dir: - self.logs_dir = Path(logs_dir) - else: - # 获取项目根目录（logger_config.py 在 utils 目录下，需要上升两层到达项目根目录） - project_root = Path(__file__).parent.parent - self.logs_dir = project_root / "logs" - - # 创建日志目录 - self.logs_dir.mkdir(exist_ok=True) - - # 日志格式 - self.log_format = '%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s' - self.date_format = '%Y-%m-%d %H:%M:%S' - - # 从环境变量获取日志级别，默认为INFO - self.log_level = self._get_log_level_from_env() - - # 是否已初始化 - self._initialized = False - - def _get_log_level_from_env(self) -> int: - """从环境变量获取日志级别 - - Returns: - int: 日志级别 - """ - log_level_str = os.getenv('LOG_LEVEL', 'INFO').upper() - - # 日志级别映射 - level_mapping = { - 'DEBUG': logging.DEBUG, - 'INFO': logging.INFO, - 'WARNING': logging.WARNING, - 'WARN': logging.WARNING, # 兼容性别名 - 'ERROR': logging.ERROR, - 'CRITICAL': logging.CRITICAL, - 'FATAL': logging.CRITICAL # 兼容性别名 - } - - return level_mapping.get(log_level_str, logging.INFO) - - def setup_logging(self, - app_name: str = "lzwcai_mcp_sqlexecutor", - log_level: int = logging.INFO, - max_file_size: int = 10 * 1024 * 1024, # 10MB - backup_count: int = 5, - console_output: bool = True) -> logging.Logger: - """设置系统日志配置 - - Args: - app_name: 应用名称，用于日志文件命名 - log_level: 日志级别 - max_file_size: 单个日志文件最大大小（字节） - backup_count: 保留的备份文件数量 - console_output: 是否输出到控制台 - - Returns: - logging.Logger: 配置好的根日志器 - """ - if self._initialized: - return logging.getLogger() - - # 设置根日志器 - root_logger = logging.getLogger() - root_logger.setLevel(log_level) - - # 清除现有的处理器 - for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) - - # 创建格式化器 - formatter = logging.Formatter(self.log_format, self.date_format) - - # 1. 主日志文件 - 按大小滚动 - main_log_file = self.logs_dir / f"{app_name}.log" - file_handler = RotatingFileHandler( - main_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - file_handler.setLevel(log_level) - file_handler.setFormatter(formatter) - root_logger.addHandler(file_handler) - - # 2. 错误日志文件 - 只记录ERROR及以上级别 - error_log_file = self.logs_dir / f"{app_name}_error.log" - error_handler = RotatingFileHandler( - error_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - error_handler.setLevel(logging.ERROR) - error_handler.setFormatter(formatter) - root_logger.addHandler(error_handler) - - # 3. 按日期滚动的日志文件 - daily_log_file = self.logs_dir / f"{app_name}_daily.log" - daily_handler = TimedRotatingFileHandler( - daily_log_file, - when='midnight', - interval=1, - backupCount=30, # 保留30天 - encoding='utf-8' - ) - daily_handler.setLevel(log_level) - daily_handler.setFormatter(formatter) - daily_handler.suffix = "%Y-%m-%d" - root_logger.addHandler(daily_handler) - - # 4. 控制台输出 - # 重要：MCP协议使用stdio时，必须将日志输出到stderr，stdout仅用于JSON-RPC通信 - if console_output: - console_handler = logging.StreamHandler(sys.stderr) - console_handler.setLevel(log_level) - console_formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s', - self.date_format - ) - console_handler.setFormatter(console_formatter) - root_logger.addHandler(console_handler) - - self._initialized = True - - # 记录初始化信息 - root_logger.info(f"日志系统初始化完成 - 日志目录: {self.logs_dir}") - root_logger.info(f"日志配置 - 级别: {logging.getLevelName(log_level)}, 文件大小限制: {max_file_size//1024//1024}MB, 备份数量: {backup_count}") - - return root_logger - - def get_module_logger(self, module_name: str) -> logging.Logger: - """获取模块专用日志器 - - Args: - module_name: 模块名称 - - Returns: - logging.Logger: 模块日志器 - """ - return logging.getLogger(module_name) - - def create_component_logger(self, - component_name: str, - log_file: str = None, - log_level: int = None) -> logging.Logger: - """为特定组件创建独立的日志器 - - Args: - component_name: 组件名称 - log_file: 独立日志文件名（可选） - log_level: 日志级别（可选） - - Returns: - logging.Logger: 组件日志器 - """ - logger = logging.getLogger(component_name) - - if log_file: - # 为组件创建独立的日志文件 - component_log_file = self.logs_dir / log_file - handler = RotatingFileHandler( - component_log_file, - maxBytes=5 * 1024 * 1024, # 5MB - backupCount=3, - encoding='utf-8' - ) - - formatter = logging.Formatter(self.log_format, self.date_format) - handler.setFormatter(formatter) - - if log_level: - handler.setLevel(log_level) - - logger.addHandler(handler) - logger.info(f"组件日志器创建完成: {component_name} -> {component_log_file}") - - return logger - - def setup_mqtt_logging(self) -> logging.Logger: - """设置MQTT专用日志 - - Returns: - logging.Logger: MQTT日志器 - """ - return self.create_component_logger( - "mqtt_communication", - "mqtt_communication.log", - logging.DEBUG - ) - - def setup_mcp_logging(self) -> logging.Logger: - """设置MCP专用日志 - - Returns: - logging.Logger: MCP日志器 - """ - return self.create_component_logger( - "mcp_services", - "mcp_services.log", - logging.DEBUG - ) - - def setup_api_logging(self) -> logging.Logger: - """设置API专用日志 - - Returns: - logging.Logger: API日志器 - """ - return self.create_component_logger( - "api_requests", - "api_requests.log", - logging.INFO - ) - - def get_logs_info(self) -> dict: - """获取日志系统信息 - - Returns: - dict: 日志系统信息 - """ - log_files = [] - if self.logs_dir.exists(): - for log_file in self.logs_dir.glob("*.log*"): - stat = log_file.stat() - log_files.append({ - "name": log_file.name, - "size": stat.st_size, - "modified": datetime.fromtimestamp(stat.st_mtime).isoformat() - }) - - return { - "logs_directory": str(self.logs_dir), - "initialized": self._initialized, - "log_files": log_files, - "total_files": len(log_files) - } - - def cleanup_old_logs(self, days: int = 30): - """清理旧日志文件 - - Args: - days: 保留天数 - """ - if not self.logs_dir.exists(): - return - - from datetime import timedelta - cutoff_time = datetime.now() - timedelta(days=days) - - cleaned_files = [] - for log_file in self.logs_dir.glob("*.log*"): - if log_file.stat().st_mtime < cutoff_time.timestamp(): - try: - log_file.unlink() - cleaned_files.append(log_file.name) - except Exception as e: - logging.error(f"清理日志文件失败: {log_file.name}, 错误: {e}") - - if cleaned_files: - logging.info(f"清理了 {len(cleaned_files)} 个旧日志文件: {cleaned_files}") - - def set_log_level(self, level: int, logger_name: str = None): - """动态调整日志级别 - - Args: - level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL) - logger_name: 指定日志器名称，None表示调整根日志器 - """ - if logger_name: - logger = logging.getLogger(logger_name) - else: - logger = logging.getLogger() - - old_level = logger.level - logger.setLevel(level) - - # 同时调整所有处理器的级别 - for handler in logger.handlers: - if not isinstance(handler, logging.StreamHandler) or handler.stream not in (sys.stdout, sys.stderr): - # 不调整控制台处理器的级别，保持原有设置 - handler.setLevel(level) - - level_name = logging.getLevelName(level) - old_level_name = logging.getLevelName(old_level) - target = logger_name or "根日志器" - - logger.info(f"日志级别已调整: {target} {old_level_name} -> {level_name}") - - def set_temporary_log_level(self, level: int, logger_name: str = None): - """临时调整日志级别（会保存原始级别用于恢复） - - Args: - level: 临时日志级别 - logger_name: 指定日志器名称，None表示调整根日志器 - """ - if not hasattr(self, '_original_levels'): - self._original_levels = {} - - target_name = logger_name or 'root' - - if logger_name: - logger = logging.getLogger(logger_name) - else: - logger = logging.getLogger() - - # 保存原始级别 - if target_name not in self._original_levels: - self._original_levels[target_name] = logger.level - - # 设置新级别 - self.set_log_level(level, logger_name) - - level_name = logging.getLevelName(level) - target = logger_name or "根日志器" - logger.info(f"临时调整日志级别: {target} -> {level_name} (可通过restore_log_level恢复)") - - def restore_log_level(self, logger_name: str = None): - """恢复日志级别到调整前的状态 - - Args: - logger_name: 指定日志器名称，None表示恢复根日志器 - """ - if not hasattr(self, '_original_levels'): - logging.warning("没有找到保存的原始日志级别") - return - - target_name = logger_name or 'root' - - if target_name not in self._original_levels: - logging.warning(f"没有找到 {target_name} 的原始日志级别") - return - - original_level = self._original_levels[target_name] - self.set_log_level(original_level, logger_name) - - # 清除保存的级别 - del self._original_levels[target_name] - - target = logger_name or "根日志器" - level_name = logging.getLevelName(original_level) - logging.info(f"已恢复日志级别: {target} -> {level_name}") - - def get_current_log_levels(self) -> dict: - """获取当前所有日志器的级别信息 - - Returns: - dict: 日志器级别信息 - """ - levels_info = {} - - # 根日志器 - root_logger = logging.getLogger() - levels_info['root'] = { - 'level': root_logger.level, - 'level_name': logging.getLevelName(root_logger.level), - 'handlers_count': len(root_logger.handlers) - } - - # 其他已创建的日志器 - for name, logger in logging.Logger.manager.loggerDict.items(): - if isinstance(logger, logging.Logger): - levels_info[name] = { - 'level': logger.level, - 'level_name': logging.getLevelName(logger.level), - 'handlers_count': len(logger.handlers) - } - - return levels_info - - -# 全局日志配置实例 -logger_config = LoggerConfig() - - -def setup_system_logging(app_name: str = "lzwcai_mcp_sqlexecutor", - log_level: int = logging.INFO) -> logging.Logger: - """系统日志初始化快捷函数 - - Args: - app_name: 应用名称 - log_level: 日志级别 - - Returns: - logging.Logger: 根日志器 - """ - return logger_config.setup_logging(app_name, log_level) - - -def get_logger(name: str) -> logging.Logger: - """获取日志器的快捷函数 - - Args: - name: 日志器名称 - - Returns: - logging.Logger: 日志器实例 - """ - return logger_config.get_module_logger(name) - - -def set_log_level(level: int, logger_name: str = None): - """动态调整日志级别的快捷函数 - - Args: - level: 新的日志级别 (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL) - logger_name: 指定日志器名称，None表示调整根日志器 - - Examples: - # 调整根日志器为DEBUG级别 - set_log_level(logging.DEBUG) - - # 调整特定模块日志器为WARNING级别 - set_log_level(logging.WARNING, "agent_ontology.core") - """ - logger_config.set_log_level(level, logger_name) - - -def set_temporary_log_level(level: int, logger_name: str = None): - """临时调整日志级别的快捷函数 - - Args: - level: 临时日志级别 - logger_name: 指定日志器名称，None表示调整根日志器 - - Examples: - # 临时调整为DEBUG级别进行调试 - set_temporary_log_level(logging.DEBUG) - # ... 进行调试 ... - # 恢复原始级别 - restore_log_level() - """ - logger_config.set_temporary_log_level(level, logger_name) - - -def restore_log_level(logger_name: str = None): - """恢复日志级别的快捷函数 - - Args: - logger_name: 指定日志器名称，None表示恢复根日志器 - """ - logger_config.restore_log_level(logger_name) - - -def get_current_log_levels() -> dict: - """获取当前日志级别信息的快捷函数 - - Returns: - dict: 日志器级别信息 - - Examples: - levels = get_current_log_levels() - print(f"根日志器级别: {levels['root']['level_name']}") - """ - return logger_config.get_current_log_levels() - - -# 便捷的日志级别常量 -class LogLevel: - """日志级别常量类""" - DEBUG = logging.DEBUG - INFO = logging.INFO - WARNING = logging.WARNING - ERROR = logging.ERROR - CRITICAL = logging.CRITICAL \ No newline at end of file diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/name_helper.py b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/name_helper.py deleted file mode 100644 index d66cf32..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/name_helper.py +++ /dev/null @@ -1,41 +0,0 @@ -# -*- coding: utf-8 -*- -""" -名称生成工具模块 -""" - -from pypinyin import lazy_pinyin, Style -import logging - -logger = logging.getLogger(__name__) - - -def generate_tool_name(business_name: str, tool_id: str) -> str: - """ - 根据业务名称和ID生成工具名称 - 格式: tool_拼音_id - - Args: - business_name: 业务名称（中文） - tool_id: 工具ID - - Returns: - str: 格式化的工具名称 - """ - try: - # 将中文转换为拼音（无音调，小写） - pinyin_list = lazy_pinyin(business_name, style=Style.NORMAL) - # 拼接拼音 - pinyin_str = ''.join(pinyin_list) - - # 将 ID 中的 '-' 替换为 '_' - formatted_id = tool_id.replace('-', '_') - - # 组合成最终的工具名称 - tool_name = f"tool_{pinyin_str}_{formatted_id}" - - return tool_name - except Exception as e: - logger.error(f"生成工具名称失败: {business_name}, {tool_id}, 错误: {e}", exc_info=True) - # 降级处理：如果拼音转换失败，使用 ID - return f"tool_{tool_id.replace('-', '_')}" - diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/schema_helper.py b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/schema_helper.py deleted file mode 100644 index 626559f..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/utils/schema_helper.py +++ /dev/null @@ -1,166 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Schema 生成工具模块 -""" - -from typing import Any, Dict, List - - -def generate_input_schema(parameters: Dict[str, Any]) -> Dict[str, Any]: - """ - 从查询配置的参数定义生成 MCP 工具的 inputSchema - - 此函数会保留完整的 JSON Schema 信息，包括： - - type: Schema 类型（通常是 "object"） - - required: 必填字段列表 - - properties: 属性定义（包括每个属性的 type, description, format, examples 等） - - description: Schema 的整体描述（如果有） - - 以及其他任何 JSON Schema 标准字段 - - 此函数还会自动添加以下字段（如果原始 parameters 中未定义）： - - targetDatabaseName: 目标数据库名称（非必填，默认为空字符串） - - Args: - parameters: 查询配置中的参数定义字典，应该是一个完整的 JSON Schema 对象 - - Returns: - Dict[str, Any]: 符合 JSON Schema 规范的 inputSchema 对象 - - Example: - >>> params = { - ... "type": "object", - ... "required": ["userId", "startTime"], - ... "properties": { - ... "userId": { - ... "type": "integer", - ... "description": "用户的唯一标识符", - ... "examples": [10086] - ... }, - ... "startTime": { - ... "type": "string", - ... "format": "date-time", - ... "description": "查询的起始时间", - ... "examples": ["2023-01-01 00:00:00"] - ... } - ... } - ... } - >>> schema = generate_input_schema(params) - >>> # schema 将包含所有原始信息，包括 format 和 examples - >>> # 同时会自动添加 targetDatabaseName 字段 - """ - # 如果 parameters 本身就是一个完整的 JSON Schema 对象，直接使用 - # 但确保至少包含 type 和 properties - if not parameters: - # 如果 parameters 为空，返回一个空的 object schema - return { - "type": "object", - "properties": {}, - "required": [] - } - - # 检查 parameters 是否已经是完整的 JSON Schema 格式 - # 完整格式应该有 "type": "object" 和 "properties" 字段 - if "type" in parameters and parameters.get("type") == "object" and "properties" in parameters: - # 已经是完整的 JSON Schema 格式，直接使用 - input_schema = dict(parameters) - else: - # parameters 是简化格式（直接是参数定义），需要转换为 JSON Schema 格式 - # 例如: {"workOrderNumber": {"type": "string", "description": "...", "required": true}} - properties = {} - required = [] - - for param_name, param_def in parameters.items(): - if isinstance(param_def, dict): - # 提取 required 标记 - if param_def.get("required", False): - required.append(param_name) - - # 复制参数定义，但移除 required 字段（它应该在顶层的 required 数组中） - prop_def = {k: v for k, v in param_def.items() if k != "required"} - properties[param_name] = prop_def - - input_schema = { - "type": "object", - "properties": properties, - "required": required - } - - # 确保必需的字段存在 - if "type" not in input_schema: - input_schema["type"] = "object" - - if "properties" not in input_schema: - input_schema["properties"] = {} - - if "required" not in input_schema: - input_schema["required"] = [] - - # 添加 targetDatabaseName 字段（如果不存在） - if "targetDatabaseName" not in input_schema["properties"]: - input_schema["properties"]["targetDatabaseName"] = { - "type": "string", - "description": "目标数据库名称", - "default": "" - } - - # 保留所有其他字段，如 description, examples, format 等 - # JSON Schema 标准支持的字段都会被保留： - # - additionalProperties - # - patternProperties - # - minProperties / maxProperties - # - dependencies - # - 等等 - - return input_schema - - -def validate_input_schema(schema: Dict[str, Any]) -> tuple[bool, str]: - """ - 验证 inputSchema 是否符合基本的 JSON Schema 规范 - - Args: - schema: 要验证的 schema 对象 - - Returns: - tuple[bool, str]: (是否有效, 错误消息或成功消息) - - Example: - >>> schema = {"type": "object", "properties": {"id": {"type": "string"}}} - >>> is_valid, msg = validate_input_schema(schema) - >>> print(is_valid, msg) - True, "Schema 验证通过" - """ - if not isinstance(schema, dict): - return False, "Schema 必须是一个字典对象" - - if schema.get("type") != "object": - return False, "Schema 的 type 字段必须是 'object'" - - if "properties" not in schema: - return False, "Schema 必须包含 properties 字段" - - if not isinstance(schema.get("properties"), dict): - return False, "Schema 的 properties 字段必须是一个字典对象" - - # 验证 required 字段（如果存在） - if "required" in schema: - required = schema["required"] - if not isinstance(required, list): - return False, "Schema 的 required 字段必须是一个列表" - - # 验证所有 required 的字段都在 properties 中定义 - properties = schema["properties"] - for field in required: - if field not in properties: - return False, f"必填字段 '{field}' 未在 properties 中定义" - 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-## 需求概述 - -本模型基于 `module/` 目录下 52 份业务导出 Excel，面向制造企业端到端经营与生产分析（线索→商机→合同→订单→采购/生产→入出库→质检→开票/收付款/核销→资金流→分佣），抽象形成可扩展的分析型数据模型。 - -设计目标： - -- 兼容 ISA-95 / IEC 62264：区分 Level 3（生产执行）与 Level 4（经营管理）数据边界 -- 分析型优先：星型模型 + 事实星座（Conformed Dimensions） -- 主数据一致性：维度表按 SCD Type 2 管理（`RowValidFrom/RowValidTo/IsCurrent`） -- 事务可追溯：事实表以 `EventTimeUTC` 作为事件时间分区/水印字段 - -范围说明： - -- 当前 Excel 以“编号/名称”作为主要业务键（例如 `产品编码`、`合同编号`、`工单编号`）。分析层统一引入代理主键 `{Entity}Id`。 -- 多数单据缺少“行明细”粒度（例如销售订单/采购订单仅见单头字段），模型保留单头事实为主，后续如补充明细可平滑扩展。 - -## 术语表 - -| 术语 | 含义 | -|---|---| -| 业务键（Business Key） | 来自源系统的编码/编号/单号等唯一标识，如 `ProductCode`、`ContractNumber` | -| 代理键（Surrogate Key） | 分析层自增/雪花主键，如 `ProductId` | -| SCD Type 2 | 维度历史保留：通过 `RowValidFrom/RowValidTo/IsCurrent` 记录有效期 | -| 退化维度（Degenerate Dimension） | 直接保存在事实表中的单据号、状态等维度属性 | -| EventTimeUTC | 统一事件时间（UTC），作为事实表分区/增量水印字段 | - -## 主题域总览（ISA-95 视角） - -| 主题域 | ISA-95 层级 | 覆盖 Excel | -|---|---|---| -| 主数据（产品/BOM/工艺/资源） | L3/L4 共用 | 产品列表、物料清单、工艺路线、工序、仓库、供应商、客户、设备、质检原因 | -| 生产执行 | Level 3 | 工单、任务、报工、装配工单 | -| 质量 | Level 3 | 质检记录、质检不通过原因 | -| 库存与仓储作业 | Level 3 | 出入库明细、盘点、调拨、其他出入库、报废 | -| 销售与履约 | Level 4 | 销售报价、销售订单、销售出库、销售退货 | -| 采购与供应 | Level 4 | 采购申请、采购订单、采购入库、采购退货 | -| 财务与资金 | Level 4 | 发票、回款、收款/付款、预收/预付、核销、资金流向、资金转账、其他收支、分佣 | -| CRM（市场到合同） | Level 4 | 线索、商机、合同 | - -## 实体清单（维度/事实） - -维度（SCD2）： - -- `DimPerson`（人员维度） -- `DimProduct`（产品维度） -- `DimCustomer`（客户维度） -- `DimSupplier`（供应商维度） -- `DimWarehouse`（仓库维度） -- `DimBom`（物料清单维度） -- `DimRouting`（工艺路线维度） -- `DimOperation`（工序维度） -- `DimEquipment`（设备维度） -- `DimQcReason`（质检不通过原因维度） -- `DimContract`（合同维度，可选：合同也可作为事实） - -事实（按 `EventTimeUTC` 分区）： - -- `FactInventoryMovement`（出入库流水事实） -- `FactWorkOrder`（工单事实） -- `FactOperationTask`（工序任务事实） -- `FactLaborReport`（报工事实） -- `FactQualityInspection`（质检事实） -- `FactSalesOrder`（销售订单事实） -- `FactSalesShipment`（销售出库事实） -- `FactSalesReturn`（销售退货事实） -- `FactPurchaseRequest`（采购申请事实） -- `FactPurchaseOrder`（采购订单事实） -- `FactPurchaseReceipt`（采购入库事实） -- `FactPurchaseReturn`（采购退货事实） -- `FactInventoryTransfer`（库存调拨事实） -- `FactInventoryCount`（库存盘点事实） -- `FactScrap`（报废事实） -- `FactArReceipt`（应收收款事实） -- `FactApPayment`（应付付款事实） -- `FactInvoice`（开票申请事实） -- `FactWriteOff`（核销事实） -- `FactCashFlow`（资金流水事实） -- `FactCommission`（分佣事实） -- `FactLead`（线索事实） -- `FactOpportunity`（商机事实） -- `FactProductionPlan`（生产计划/装配工单汇总事实，可选） - -## ER 图（事实星座） - -```mermaid -erDiagram - DimProduct ||--o{ FactInventoryMovement : "by_product (关联产品)" - DimWarehouse ||--o{ FactInventoryMovement : "by_warehouse (关联仓库)" - DimBom ||--o{ FactInventoryMovement : "by_bom (关联BOM)" - DimPerson ||--o{ FactInventoryMovement : "operated_by (操作人)" - - DimProduct ||--o{ FactWorkOrder : "produces (生产产品)" - DimSupplier ||--o{ FactWorkOrder : "outsourced_to (委外供应商)" - DimRouting ||--o{ FactWorkOrder : "uses_routing (使用工艺)" - DimPerson ||--o{ FactWorkOrder : "managed_by (生产主管)" - - DimOperation ||--o{ FactOperationTask : "executes (执行工序)" - DimProduct ||--o{ FactOperationTask : "for_product (关联产品)" - DimPerson ||--o{ FactOperationTask : "worker (执行工人)" - - DimProduct ||--o{ FactLaborReport : "reports_on (报工产品)" - DimOperation ||--o{ FactLaborReport : "at_operation (报工工序)" - DimPerson ||--o{ FactLaborReport : "reported_by (报工人员)" - - DimQcReason ||--o{ FactQualityInspection : "fail_reason (不合格原因)" - DimProduct ||--o{ FactQualityInspection : "inspects (质检产品)" - DimPerson ||--o{ FactQualityInspection : "inspected_by (质检员)" - - DimCustomer ||--o{ FactSalesOrder : "buys (购买)" - DimPerson ||--o{ FactSalesOrder : "sold_by (销售员)" - DimCustomer ||--o{ FactSalesShipment : "receives (收货)" - DimPerson ||--o{ FactSalesShipment : "shipped_by (发货员)" - DimCustomer ||--o{ FactSalesReturn : "returns (退货)" - DimPerson ||--o{ FactSalesReturn : "handled_by (处理人)" - - DimSupplier ||--o{ FactPurchaseOrder : "supplies (供应)" - DimPerson ||--o{ FactPurchaseOrder : "purchased_by (采购员)" - DimSupplier ||--o{ FactPurchaseReceipt : "delivers (送货)" - DimPerson ||--o{ FactPurchaseReceipt : "received_by (收货员)" - DimSupplier ||--o{ FactPurchaseReturn : "takes_back (退回)" - - DimContract ||--o{ FactInvoice : "invoiced_for (开票合同)" - DimContract ||--o{ FactArReceipt : "cash_in (收款合同)" - DimContract ||--o{ FactApPayment : "cash_out (付款合同)" - DimContract ||--o{ FactWriteOff : "writeoff (核销合同)" - DimPerson ||--o{ FactContract : "owned_by (合同负责人)" - DimPerson ||--o{ FactLead : "owned_by (线索负责人)" - DimPerson ||--o{ FactOpportunity : "owned_by (商机负责人)" -``` - -## 业务数据流转全景图 - -本流程图展示了从商机线索到最终产品交付的端到端数据流转过程，以及各环节涉及的关键数据实体。 - -```mermaid -flowchart TD - %% 节点样式定义 - classDef process fill:#e1f5fe,stroke:#01579b,stroke-width:2px; - classDef decision fill:#fff9c4,stroke:#fbc02d,stroke-width:2px; - classDef table fill:#f3e5f5,stroke:#7b1fa2,stroke-width:1px,stroke-dasharray: 5 5; - - %% 1. CRM阶段 - subgraph Stage_CRM [CRM与销售阶段] - direction TB - Node_Lead[("线索处理
In: FactLead
Logic: 线索清洗/转化")]:::process - Node_Opp[("商机确认
In: FactLead
Out: FactOpportunity")]:::process - Decision_Win{商机赢单?}:::decision - Node_Contract[("合同签订
In: FactOpportunity
Out: DimContract")]:::process - Node_SO[("销售订单生成
In: DimContract
Out: FactSalesOrder")]:::process - - Table_Lead[("FactLead")]:::table - Table_Opp[("FactOpportunity")]:::table - Table_SO[("FactSalesOrder")]:::table - end - - %% 2. 计划阶段 - subgraph Stage_Plan [计划与排程阶段] - direction TB - Node_MPS[("主生产计划(MPS)
In: FactSalesOrder
Logic: 需求合并/产能平衡
Out: FactProductionPlan")]:::process - Node_MRP[("MRP运算/BOM展开
In: FactProductionPlan, DimBom
Logic: 净需求计算
Out: FactWorkOrder(Planned)")]:::process - Decision_MakeBuy{自制/外购?}:::decision - Node_PR[("采购申请生成
Out: FactPurchaseRequest")]:::process - - Table_Plan[("FactProductionPlan")]:::table - Table_Bom[("DimBom")]:::table - Table_Stock[("FactInventoryMovement
(当前库存)")]:::table - end - - %% 新增：3. 采购与供应阶段 - subgraph Stage_Procure [采购与供应阶段] - direction TB - Node_PO[("采购订单(PO)
In: FactPurchaseRequest
Out: FactPurchaseOrder")]:::process - Node_Receive[("收货与IQC
Logic: 验收/质检
Out: FactQualityInspection")]:::process - Decision_IQC{IQC合格?}:::decision - Node_Return[("采购退货
Out: FactPurchaseReturn")]:::process - Node_Mat_In[("原材料入库
Logic: 批次录入/更新库存
Out: FactPurchaseReceipt
FactInventoryMovement")]:::process - Node_AP[("应付挂账(AP)
Logic: 三单匹配
Out: FactApPayment")]:::process - - Table_PO[("FactPurchaseOrder")]:::table - Table_Receipt[("FactPurchaseReceipt")]:::table - end - - %% 4. 生产执行阶段 (原 Stage_Make) - subgraph Stage_Make [生产执行阶段] - direction TB - Node_WO_Release[("工单下达
In: FactWorkOrder(Planned)
Logic: 物料齐套/锁定资源
Out: FactWorkOrder(Released)")]:::process - Node_Task[("工序任务派工
In: FactWorkOrder, DimRouting
Out: FactOperationTask")]:::process - Node_Labor[("生产报工
In: FactOperationTask
Logic: 投入/产出/工时记录
Out: FactLaborReport")]:::process - - Table_WO[("FactWorkOrder")]:::table - Table_Task[("FactOperationTask")]:::table - Table_Labor[("FactLaborReport")]:::table - end - - %% 5. 质量与入库阶段 (原 Stage_QC) - subgraph Stage_QC [质量与仓储阶段] - direction TB - Node_QC[("成品检验(FQC)
In: FactLaborReport
Logic: 检验标准比对
Out: FactQualityInspection")]:::process - Decision_Pass{质检合格?}:::decision - Node_Scrap[("报废处理
Logic: 记录不良原因
Out: FactScrap")]:::process - Node_FG_In[("成品入库
In: FactWorkOrder(Closed)
Logic: 更新库存余额
Out: FactInventoryMovement(IN)")]:::process - - Table_QC[("FactQualityInspection")]:::table - Table_Inv[("FactInventoryMovement")]:::table - end - - %% 6. 交付阶段 (原 Stage_Deliver) - subgraph Stage_Deliver [交付阶段] - direction TB - Node_Ship[("销售发货
In: FactSalesOrder, FactInventoryMovement
Logic: 拣货/出库扣减
Out: FactSalesShipment")]:::process - Node_End((交付完成)) - - Table_Ship[("FactSalesShipment")]:::table - end - - %% 新增：7. 财务与资金阶段 - subgraph Stage_Finance [财务与资金阶段] - direction TB - %% AR Flow - Node_Invoice[("销售开票
In: FactSalesShipment
Out: FactInvoice")]:::process - Node_ArRec[("销售收款
Out: FactArReceipt")]:::process - Node_ArOff[("应收核销
Logic: 核销发票与收款
Out: FactWriteOff")]:::process - - %% AP Flow - Node_ApPay[("采购付款
Out: FactApPayment")]:::process - Node_ApOff[("应付核销
Logic: 核销应付与付款
Out: FactWriteOff")]:::process - - %% Cash Flow - Node_Cash[("资金流水
Logic: 账户变动记录
Out: FactCashFlow")]:::process - - Table_Inv[("FactInvoice")]:::table - Table_Ar[("FactArReceipt")]:::table - Table_Ap[("FactApPayment")]:::table - Table_Cash[("FactCashFlow")]:::table - end - - %% 关系连线 - Node_Lead --> Node_Opp - Node_Opp --> Decision_Win - Decision_Win -- Yes --> Node_Contract - Node_Contract --> Node_SO - Node_SO --> Node_MPS - - Node_MPS --> Node_MRP - Node_MRP --> Decision_MakeBuy - Decision_MakeBuy -- 外购 --> Node_PR - Decision_MakeBuy -- 自制 --> Node_WO_Release - - %% 采购流程连线 - Node_PR --> Node_PO - Node_PO --> Node_Receive - Node_Receive --> Decision_IQC - Decision_IQC -- No --> Node_Return - Decision_IQC -- Yes --> Node_Mat_In - Node_Mat_In --> Node_AP - Node_Mat_In --> Node_WO_Release - - Node_WO_Release --> Node_Task - Node_Task --> Node_Labor - Node_Labor --> Node_QC - - Node_QC --> Decision_Pass - Decision_Pass -- No --> Node_Scrap - Decision_Pass -- Yes --> Node_FG_In - - Node_FG_In --> Node_Ship - Node_Ship --> Node_End - - %% 财务流程连线 - Node_Ship --> Node_Invoice - Node_Invoice --> Node_ArRec - Node_ArRec --> Node_ArOff - - Node_AP --> Node_ApPay - Node_ApPay --> Node_ApOff - - Node_ArRec --> Node_Cash - Node_ApPay --> Node_Cash - - %% 数据表关联（虚线） - Table_Lead -.-> Node_Lead - Node_Opp -.-> Table_Opp - Node_Contract -.-> Table_SO - Table_SO -.-> Node_MPS - Node_MPS -.-> Table_Plan - Table_Plan -.-> Node_MRP - Table_Bom -.-> Node_MRP - Table_Stock -.-> Node_MRP - - Node_PR -.-> Table_PO - Node_PO -.-> Table_PO - Node_Mat_In -.-> Table_Receipt - Node_Mat_In -.-> Table_Inv - - Node_WO_Release -.-> Table_WO - Node_Task -.-> Table_Task - Node_Labor -.-> Table_Labor - Node_QC -.-> Table_QC - Node_Receive -.-> Table_QC - Node_FG_In -.-> Table_Inv - Node_Ship -.-> Table_Ship - - Node_Invoice -.-> Table_Inv - Node_ArRec -.-> Table_Ar - Node_ApPay -.-> Table_Ap - Node_Cash -.-> Table_Cash -``` - -### 关键业务规则说明 - -1. **商机转订单**： - * 当 `FactOpportunity` 状态确认为“赢单”且 `DimContract` 签订后，系统生成 `FactSalesOrder`。 - * 此时确立销售对象（Customer）、产品（Product）及交付日期（DeliveryDate）。 - -2. **计划运算 (MPS/MRP)**： - * `FactProductionPlan` 汇总销售订单需求，结合 `FactInventoryMovement` 计算出的当前库存快照，进行净需求计算。 - * 利用 `DimBom` 展开物料清单，区分自制件（生成 `FactWorkOrder`）与采购件（生成 `FactPurchaseRequest`）。 - -3. **采购闭环管理**： - * **采购订单 (PO)**：`FactPurchaseOrder` 经审批后生效，明确供应商、价格、交期。 - * **入库验收 (IQC)**：物料到达后必须进行 IQC（`FactQualityInspection`），合格品方可办理入库（`FactPurchaseReceipt`）并增加库存（`FactInventoryMovement`）。 - * **财务对接 (AP)**：系统自动匹配 PO（采购单）、Receipt（入库单）和供应商发票，生成应付账款（`FactApPayment`），确保“三单一致”。 - * **供应商管理**：IQC 合格率与交期准确率回写至 `DimSupplier`，用于季度评估与优选名录维护。 - -4. **生产执行闭环**： - * 工单下达前需校验物料齐套性（依赖采购入库的 `FactInventoryMovement`）。 - * 现场工人通过 `FactLaborReport` 上报实际投入工时与产出数量，该数据是成本核算的核心依据。 - -5. **质量控制 (FQC)**： - * `FactQualityInspection` 记录成品检验结果。 - * 质检合格触发成品入库，不合格则根据原因（`DimQcReason`）触发返工或报废（`FactScrap`）。 - -6. **库存与交付**： - * 所有实物库存变动（入库、出库、报废）统一写入 `FactInventoryMovement`，确保库存台账的一致性。 - * `FactSalesShipment` 记录发货动作，作为收入确认（Revenue Recognition）的前置事件。 - -7. **财务资金流转**： - * **应收 (AR)**：销售发货后生成发票申请（`FactInvoice`），回款后（`FactArReceipt`）进行核销（`FactWriteOff`）。 - * **应付 (AP)**：采购入库确认应付后（`FactApPayment` 中的挂账状态），执行付款（`FactApPayment` 中的实付）并核销。 - * **资金 (Cash)**：所有收付款动作最终汇总至 `FactCashFlow`，提供实时的资金日记账分析。 - -## 表结构详设 - -类型说明（可按目标数据库微调）： - -- `bigint`：代理主键/外键 -- `varchar(n)`：业务键、名称、状态等 -- `decimal(18,4)`：数量与金额（金额可按需要改 `decimal(18,2)`） -- `datetime2`：统一时间字段（UTC） - -### DimPerson（人员维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PersonId | bigint | N | autoincr | 主键 | | - | PersonCode | varchar(64) | N | | 人员编码（业务键） | 来源：用户ID/工号 | - | PersonName | varchar(255) | N | | 姓名 | | - | Department | varchar(100) | Y | | 部门 | | - | Position | varchar(100) | Y | | 职位/岗位 | | - | Phone | varchar(32) | Y | | 电话 | | - | Email | varchar(100) | Y | | 邮箱 | | - | Status | varchar(20) | Y | | 状态 | ACTIVE, INACTIVE | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | -| Birthday | date | Y | | 出生日期 | | -| Employment_day | date | Y | | 入职日期 | | -| Job_description | varchar(255) | Y | | 职务描述 | | - -### DimProduct（产品维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ProductId | bigint | N | autoincr | 主键 | | - | ProductCode | varchar(64) | N | | 产品编码（业务键） | 来源：产品列表.产品编码 | - | ProductName | varchar(255) | N | | 产品名称 | | - | ProductSpec | varchar(255) | Y | | 产品规格 | | - | ProductCategory | varchar(100) | Y | | 产品类别 | | - | ProductType | varchar(100) | Y | | 产品类型 | | - | UomCode | varchar(16) | Y | | 主单位 | kg,pcs,L,m | - | Brand | varchar(100) | Y | | 品牌 | | - | Origin | varchar(100) | Y | | 产地 | | - | TransportCondition | varchar(100) | Y | | 运输条件 | | - | Barcode | varchar(64) | Y | | 条形码 | | - | DrawingNumber | varchar(64) | Y | | 图号 | | - | Material | varchar(100) | Y | | 材料 | | - | SurfaceTreatment | varchar(100) | Y | | 表面处理 | | - | MinStockQty | decimal(18,4) | Y | | 最低库存数量 | | - | SafetyStockQty | decimal(18,4) | Y | | 安全库存数量 | | - | MaxStockQty | decimal(18,4) | Y | | 最高库存数量 | | - | SourceSystem | varchar(50) | Y | | 数据来源 | 来源：数据来源 | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimCustomer（客户维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| CustomerId | bigint | N | autoincr | 主键 | | - | CustomerName | varchar(255) | N | | 客户名称（业务键） | 来源：客户列表.客户名称 | - | CustomerSource | varchar(100) | Y | | 客户来源 | | - | Phone | varchar(32) | Y | | 手机 | | - | Telephone | varchar(32) | Y | | 电话 | | - | Email | varchar(100) | Y | | 邮箱 | | - | CustomerLevel | varchar(50) | Y | | 客户级别 | | - | Industry | varchar(100) | Y | | 客户行业 | | - | Address | varchar(500) | Y | | 详细地址 | | - | IsLocked | bit | Y | | 锁定状态 | 0,1 | - | DealStatus | varchar(50) | Y | | 成交状态 | | - | LastFollowUpTimeUTC | datetime2 | Y | | 最后跟进时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimSupplier（供应商维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| SupplierId | bigint | N | autoincr | 主键 | | - | SupplierCode | varchar(64) | N | | 供应商编码（业务键） | 来源：供应商列表.供应商编码 | - | SupplierName | varchar(255) | N | | 供应商名称 | | - | SupplierCategory | varchar(100) | Y | | 供应商分类 | | - | SupplierLevel | varchar(50) | Y | | 供应商级别 | | - | Address | varchar(500) | Y | | 地址 | | - | DefaultCurrencyCode | varchar(16) | Y | | 默认币别 | CNY,USD,EUR | - | SettlementTerm | varchar(50) | Y | | 结算期限 | | - | ContractExpiryDateUTC | datetime2 | Y | | 合同到期日（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimWarehouse（仓库维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| WarehouseId | bigint | N | autoincr | 主键 | | - | WarehouseCode | varchar(64) | N | | 仓库编码（业务键） | 来源：仓库列表.仓库编码 | - | WarehouseName | varchar(255) | N | | 仓库名称 | | - | WarehouseType | varchar(50) | Y | | 仓库类型 | | - | Status | varchar(20) | Y | | 仓库状态 | ENABLED,DISABLED | - | Address | varchar(500) | Y | | 仓库地址 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimBom（物料清单维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| BomId | bigint | N | autoincr | 主键 | | - | BomNumber | varchar(64) | N | | 清单编号（业务键） | 来源：物料清单.清单编号 | - | BomTopic | varchar(100) | Y | | 清单主题 | 来源：清单主题 | -| IsPrimary | bit | Y | | 主辅清单标识 | 0,1 | -| LevelNumber | int | Y | | 层级 | | - | ProductCode | varchar(64) | Y | | 关联产品编码 | 来源：产品编号 | - | UnitUsageQty | decimal(18,4) | Y | | 单位用量 | | - | UomCode | varchar(16) | Y | | 产品单位 | | - | SupplierName | varchar(255) | Y | | 默认供应商（退化属性） | 来源：供应商 | -| Remark | varchar(500) | Y | | 备注 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimRouting（工艺路线维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| RoutingId | bigint | N | autoincr | 主键 | | - | RoutingNumber | varchar(64) | N | | 工艺路线编号（业务键） | 来源：工艺路线.工艺路线编号 | - | RoutingName | varchar(255) | N | | 工艺路线名称 | | - | OperationListText | varchar(2000) | Y | | 工序/工艺路线列表（原文） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimOperation（工序维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| OperationId | bigint | N | autoincr | 主键 | | - | OperationNumber | varchar(64) | N | | 工序编号（业务键） | 来源：工序.工序编号 | - | OperationName | varchar(255) | N | | 工序名称 | | - | ReportPermission | varchar(50) | Y | | 报工权限 | | - | ReportRatio | decimal(18,6) | Y | | 报工数配比 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimEquipment（设备维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| EquipmentId | bigint | N | autoincr | 主键 | | - | EquipmentNumber | varchar(64) | N | | 设备编号（业务键） | 来源：设备.设备编号 | - | EquipmentName | varchar(255) | N | | 设备名称 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimQcReason（质检不通过原因维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| QcReasonId | bigint | N | autoincr | 主键 | | - | QcReasonNumber | varchar(64) | N | | 不通过原因编号（业务键） | 来源：质检原因.不通过原因编号 | - | QcReasonName | varchar(255) | N | | 不通过原因 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### DimContract（合同维度） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ContractId | bigint | N | autoincr | 主键 | | - | ContractNumber | varchar(64) | N | | 合同编号（业务键） | 来源：合同.合同编号 | - | ContractName | varchar(255) | Y | | 合同名称 | | - | OwnerId | bigint | Y | | 负责人 (外键 → DimPerson) | | - | CustomerName | varchar(255) | Y | | 客户名称（退化） | | - | CustomerId | bigint | Y | | 关联客户（外键 → DimCustomer） | | - | ContractAmount | decimal(18,4) | Y | | 合同金额 | | - | OrderTimeUTC | datetime2 | Y | | 下单时间（UTC） | | -| PaidAmount | decimal(18,4) | Y | | 回款金额 | | -| UnpaidAmount | decimal(18,4) | Y | | 未回款金额 | | -| Stage | varchar(50) | Y | | 当前阶段 | | -| Result | varchar(50) | Y | | 结果 | | - | ContractType | varchar(50) | Y | | 合同类型 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| RowValidFrom | datetime2 | N | | SCD2 生效时间（UTC） | | -| RowValidTo | datetime2 | N | | SCD2 失效时间（UTC） | | -| IsCurrent | bit | N | 1 | 是否当前版本 | 0,1 | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactInventoryMovement（出入库流水事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| InventoryMovementId | bigint | N | autoincr | 主键 | | - | EventTimeUTC | datetime2 | N | | 出入库事件时间（UTC） | 来源：出入库日期/入库时间/出库时间 | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | - | WarehouseId | bigint | Y | | 关联DimWarehouse (外键 → DimWarehouse) | | - | BomId | bigint | Y | | 关联DimBom (外键 → DimBom) | | -| OperatorId | bigint | Y | | 外键 → DimPerson（操作人） | | -|SalesOrderId | bigint | Y | | 关联FactSalesOrder (外键 → FactSalesOrder) | | -|PurchaseOrderId | bigint | Y | | 关联FactPurchaseOrder (外键 → FactPurchaseOrder) | | - | DocNumber | varchar(64) | N | | 出入库单号 | 来源：出入库单号/入库单编号/出库单编号 | - | DocType | varchar(30) | N | | 单据类型 | IN,OUT,TRANSFER,OTHER_IN,OTHER_OUT | - | MovementQty | decimal(18,4) | N | 0 | 出入库数量（入为正，出为负） | | -| UomCode | varchar(16) | Y | | 单位 | | -| UnitPrice | decimal(18,4) | Y | | 单价 | | -| DiscountUnitPrice | decimal(18,4) | Y | | 折后单价 | | -| Amount | decimal(18,4) | Y | | 总价 | | -| DiscountAmount | decimal(18,4) | Y | | 折后总价 | | -| OnhandQtyAfter | decimal(18,4) | Y | | 发生后库存量（源字段） | | -| ResponsiblePerson | varchar(100) | Y | | 负责人 | | -| AuditStatus | varchar(20) | Y | | 审核状态 | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactWorkOrder（工单事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| WorkOrderId | bigint | N | autoincr | 主键 | | - | WorkOrderNumber | varchar(64) | N | | 工单编号（退化维度） | 来源：工单编号 | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | -| SupplierId | bigint | Y | | 外键 → DimSupplier（委外时） | | -| RoutingId | bigint | Y | | 外键 → DimRouting | | -| ProductionManagerId | bigint | Y | | 外键 → DimPerson（生产主管） | | - | PlannedQty | decimal(18,4) | Y | | 计划生产数 | | - | CompletedQty | decimal(18,4) | Y | | 完成数 | | -| OperationGoodQty | decimal(18,4) | Y | | 工序良品数 | | -| OperationBadQty | decimal(18,4) | Y | | 工序不良品数 | | -| QcGoodQty | decimal(18,4) | Y | | 工单质检良品数 | | -| QcBadQty | decimal(18,4) | Y | | 工单质检不良品数 | | - | Status | varchar(30) | Y | | 工单状态 | OPEN,STARTED,CLOSED,SCRAP | - | EventTimeUTC | datetime2 | N | | 工单状态变更/抽取时间（UTC） | | -| SourceDocNumber | varchar(64) | Y | | 单据编号（退化） | 来源：单据编号 | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactOperationTask（工序任务事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| OperationTaskId | bigint | N | autoincr | 主键 | | -| WorkOrderNumber | varchar(64) | N | | 工单编号（退化维度） | 来源：工单编号 | -| OperationId | bigint | Y | | 关联DimOperation (外键 → DimOperation) | | -| ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | -| PlannedStartTimeUTC | datetime2 | Y | | 计划开始时间（UTC） | | -| PlannedEndTimeUTC | datetime2 | Y | | 计划结束时间（UTC） | | -| ActualStartTimeUTC | datetime2 | Y | | 实际开始时间（UTC） | | -| ActualEndTimeUTC | datetime2 | Y | | 实际结束时间（UTC） | | -| PlannedQty | decimal(18,4) | Y | | 计划数 | | -| GoodQty | decimal(18,4) | Y | | 良品数 | | -| BadQty | decimal(18,4) | Y | | 不良品数 | | -| BadReasonText | varchar(500) | Y | | 不良品项（原文） | | -| EventTimeUTC | datetime2 | N | | 任务事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactLaborReport（报工事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| LaborReportId | bigint | N | autoincr | 主键 | | - | WorkOrderNumber | varchar(64) | N | | 工单（退化维度） | 来源：报工列表.工单 | -| TaskName | varchar(255) | Y | | 任务（退化维度） | 来源：报工列表.任务 | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | -| OperationStatus | varchar(30) | Y | | 工序状态 | | - | WorkerName | varchar(100) | Y | | 生产人员 | | - | ReportQty | decimal(18,4) | N | 0 | 报工数 | | - | GoodQty | decimal(18,4) | Y | | 良品数 | | - | BadQty | decimal(18,4) | Y | | 良品数 | | -| DurationMinutes | decimal(18,2) | Y | | 报工时长（分钟） | | -| UomCode | varchar(16) | Y | | 单位 | | -| BadReasonText | varchar(500) | Y | | 不良品项（原文） | | - | EventTimeUTC | datetime2 | N | | 报工事件时间（UTC） | | -| SupplierId | bigint | Y | | 外键 → DimSupplier（委外时） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactQualityInspection（质检事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| QualityInspectionId | bigint | N | autoincr | 主键 | | - | InspectionNumber | varchar(64) | N | | 质检记录编号（退化维度） | 来源：质检记录编号 | -| InspectionBatchNumber | varchar(64) | Y | | 质检批次号 | | - | WorkOrderNumber | varchar(64) | Y | | 工单（退化维度） | | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | -| SupplierId | bigint | Y | | 外键 → DimSupplier | | -| QcReasonId | bigint | Y | | 外键 → DimQcReason（不通过原因） | | - | InspectorId | bigint | Y | | 质检员 (外键 → DimPerson) | | -| QcType | varchar(30) | Y | | 质检类型 | IN_PROCESS,FINAL,INCOMING | - | PassQty | decimal(18,4) | Y | | 通过数 | | - | FailQty | decimal(18,4) | Y | | 不通过数 | | -| SalesOrderNumber | varchar(64) | Y | | 销售订单（退化维度） | | -| CustomerName | varchar(255) | Y | | 客户（退化维度） | | -| ShipQty | decimal(18,4) | Y | | 发货数 | | - | EventTimeUTC | datetime2 | N | | 质检事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactSalesOrder（销售订单事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| SalesOrderId | bigint | N | autoincr | 主键 | | - | SalesOrderNumber | varchar(64) | N | | 订单编号（退化维度） | 来源：销售订单.订单编号 | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | SalesPersonId | bigint | Y | | 关联销售员 (外键 → DimPerson) | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化维度） | | - | ContractId | bigint | Y | | 关联合同 (外键 → DimContract) | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | OrderDateUTC | datetime2 | Y | | 订单单据日期（UTC） | | -| DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | - | DealAmount | decimal(18,4) | Y | | 成交金额 | | -| PaymentStatus | varchar(30) | Y | | 收款状态/付款状态 | | -| TransportMode | varchar(50) | Y | | 运输方式 | | -| Packaging | varchar(100) | Y | | 包材 | | -| CustomsNumber | varchar(64) | Y | | 报关单号 | | -| ProductionDocNumber | varchar(64) | Y | | 生产单据编号 | | -| Stage | varchar(50) | Y | | 当前阶段 | | - | Result | varchar(50) | Y | | 结果 | | - | EventTimeUTC | datetime2 | N | | 订单事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactSalesShipment（销售出库事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| SalesShipmentId | bigint | N | autoincr | 主键 | | - | ShipmentNumber | varchar(64) | N | | 出库单编号（退化维度） | 来源：销售出库单.出库单编号 | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | SalesPersonId | bigint | Y | | 关联销售员 (外键 → DimPerson) | | -| OperatorId | bigint | Y | | 外键 → DimPerson（发货员） | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化维度） | | - | ContractId | bigint | Y | | 关联合同 (外键 → DimContract) | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | DocDateUTC | datetime2 | Y | | 出库单单据日期（UTC） | | -| DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | -| TransportMode | varchar(50) | Y | | 运输方式 | | -| CustomsNumber | varchar(64) | Y | | 报关单号 | | - | Amount | decimal(18,4) | Y | | 成交金额/单据金额 | | - | EventTimeUTC | datetime2 | N | | 出库事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactSalesReturn（销售退货事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| SalesReturnId | bigint | N | autoincr | 主键 | | - | SalesReturnNumber | varchar(64) | N | | 销售退货单编号（退化维度） | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | SalesPersonId | bigint | Y | | 销售员 (外键 → DimPerson) | | -| OperatorId | bigint | Y | | 外键 → DimPerson（处理人） | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化维度） | | -| ReturnReason | varchar(255) | Y | | 退货原因 | | -| IsReship | bit | Y | | 是否退货重发 | 0,1 | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | -| DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | Amount | decimal(18,4) | Y | | 单据金额 | | - | EventTimeUTC | datetime2 | N | | 退货事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactPurchaseRequest（采购申请事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PurchaseRequestId | bigint | N | autoincr | 主键 | | - | PurchaseRequestNumber | varchar(64) | N | | 采购申请单编号（退化维度） | | - | SupplierId | bigint | Y | | 建议供应商 (外键 → DimSupplier) | | - | ApplicantId | bigint | Y | | 申请人 (外键 → DimPerson) | | - | CurrencyCode | varchar(16) | Y | | 币别 | | - | FxRate | decimal(18,6) | Y | | 汇率 | | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | - | TotalAmount | decimal(18,4) | Y | | 采购总价 | | - | Status | varchar(30) | Y | | 采购状态 | | - | RelatedOrderNumber | varchar(64) | Y | | 订单编号（退化） | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化） | | - | EventTimeUTC | datetime2 | N | | 采购申请事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactPurchaseOrder（采购订单事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PurchaseOrderId | bigint | N | autoincr | 主键 | | - | PurchaseOrderNumber | varchar(64) | N | | 采购订单编号（退化维度） | | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | - | BuyerId | bigint | Y | | 关联采购员 (外键 → DimPerson) | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | -| DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化） | | - | ContractId | bigint | Y | | 关联合同 (外键 → DimContract) | | -| DeliveryNoteNumber | varchar(64) | Y | | 送货单号 | | -| SettlementDateUTC | datetime2 | Y | | 结算日期（UTC） | | -| SettlementTerm | varchar(50) | Y | | 结算期限 | | - | EventTimeUTC | datetime2 | N | | 采购订单事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactPurchaseReceipt（采购入库事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PurchaseReceiptId | bigint | N | autoincr | 主键 | | - | PurchaseReceiptNumber | varchar(64) | N | | 采购入库单编号（退化维度） | | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | -| BuyerId | bigint | Y | | 外键 → DimPerson（采购员） | | -| OperatorId | bigint | Y | | 外键 → DimPerson（收货员） | | - | PurchaseOrderNumber | varchar(64) | Y | | 采购订单（退化维度） | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | -| DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | -| DeliveryDateUTC | datetime2 | Y | | 交货日期（UTC） | | -| ContractNumber | varchar(64) | Y | | 合同编号（退化） | | - | Amount | decimal(18,4) | Y | | 成交金额 | | -| EstimatedCost | decimal(18,4) | Y | | 预计采购费用 | | -| ReceiptQtyTotal | decimal(18,4) | Y | | 入库总数 | | -| PaidAmount | decimal(18,4) | Y | | 已付款金额 | | -| UnpaidAmount | decimal(18,4) | Y | | 未付款金额 | | -| PaymentStatus | varchar(30) | Y | | 付款状态 | | -| ReturnedQty | decimal(18,4) | Y | | 已退货数 | | -| NotReturnedQty | decimal(18,4) | Y | | 未退货数 | | -| ReturnStatus | varchar(30) | Y | | 退货状态 | | - | EventTimeUTC | datetime2 | N | | 入库事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactPurchaseReturn（采购退货事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| PurchaseReturnId | bigint | N | autoincr | 主键 | | - | PurchaseReturnNumber | varchar(64) | N | | 采购退货单编号（退化维度） | | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | - | BuyerId | bigint | Y | | 采购员 (外键 → DimPerson) | | - | OperatorId | bigint | Y | | 处理人 (外键 → DimPerson) | | - | CurrencyCode | varchar(16) | Y | | 币别 | | - | FxRate | decimal(18,6) | Y | | 汇率 | | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | ReturnReason | varchar(255) | Y | | 退货原因 | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化） | | - | RelatedOrderNumber | varchar(64) | Y | | 订单号（退化） | | - | EventTimeUTC | datetime2 | N | | 退货事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactInventoryTransfer（库存调拨事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| InventoryTransferId | bigint | N | autoincr | 主键 | | - | TransferNumber | varchar(64) | N | | 调拨单编号（退化维度） | | - | OperatorId | bigint | Y | | 操作人 (外键 → DimPerson) | | - | TransferName | varchar(255) | Y | | 调拨名称 | | - | Tag | varchar(100) | Y | | 单据标签 | | - | TransferTimeUTC | datetime2 | Y | | 调拨时间（UTC） | | - | PlateNumber | varchar(32) | Y | | 车牌号 | | - | DeliveryMode | varchar(50) | Y | | 交货方式 | | - | EventTimeUTC | datetime2 | N | | 调拨事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactInventoryCount（库存盘点事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| InventoryCountId | bigint | N | autoincr | 主键 | | - | CountNumber | varchar(64) | N | | 盘点编号（退化维度） | | - | OperatorId | bigint | Y | | 盘点人 (外键 → DimPerson) | | - | WarehouseName | varchar(255) | Y | | 盘点仓库（退化） | | - | CountName | varchar(255) | Y | | 盘点名称 | | - | CountTimeUTC | datetime2 | Y | | 盘点时间（UTC） | | - | EventTimeUTC | datetime2 | N | | 盘点事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactScrap（报废事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ScrapId | bigint | N | autoincr | 主键 | | - | ScrapNumber | varchar(64) | N | | 报废编号（退化维度） | | - | OperatorId | bigint | Y | | 操作人 (外键 → DimPerson) | | - | WarehouseName | varchar(255) | Y | | 仓库（退化） | | - | ScrapTimeUTC | datetime2 | Y | | 报废日期（UTC） | | - | EventTimeUTC | datetime2 | N | | 报废事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactArReceipt（应收收款事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ArReceiptId | bigint | N | autoincr | 主键 | | - | DocNumber | varchar(64) | N | | 单据编号（退化维度） | 来源：收款单/回款/预收/其他收入等 | - | DocType | varchar(30) | N | | 单据类型 | RECEIPT,REFUND,ADVANCE,ADVANCE_REFUND,OTHER_INCOME | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | -| DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | Amount | decimal(18,4) | Y | | 收款总金额/回款金额/应收金额 | | -| AmountBase | decimal(18,4) | Y | | 本位币金额 | 来源：本位币字段 | -| FeeAmount | decimal(18,4) | Y | | 手续费 | | -| DiscountAmount | decimal(18,4) | Y | | 优惠金额 | | -| WriteOffAmount | decimal(18,4) | Y | | 核销总金额 | | -| WrittenOffAmount | decimal(18,4) | Y | | 已核销金额 | | -| UnwrittenOffAmount | decimal(18,4) | Y | | 未核销金额 | | -| RelatedDocNumber | varchar(64) | Y | | 关联单编号/销售订单编号 | | - | EventTimeUTC | datetime2 | N | | 收款事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactApPayment（应付付款事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ApPaymentId | bigint | N | autoincr | 主键 | | - | DocNumber | varchar(64) | N | | 单据编号（退化维度） | 来源：付款单/预付款/其他支出等 | - | DocType | varchar(30) | N | | 单据类型 | PAYMENT,REFUND,ADVANCE,ADVANCE_REFUND,OTHER_EXPENSE | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | -| DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | -| CurrencyCode | varchar(16) | Y | | 币别 | | -| FxRate | decimal(18,6) | Y | | 汇率 | | - | Amount | decimal(18,4) | Y | | 付款总金额/应付金额 | | -| AmountBase | decimal(18,4) | Y | | 本位币金额 | | -| FeeAmount | decimal(18,4) | Y | | 手续费 | | -| DiscountAmount | decimal(18,4) | Y | | 优惠金额 | | -| WriteOffAmount | decimal(18,4) | Y | | 核销总金额 | | -| WrittenOffAmount | decimal(18,4) | Y | | 已核销金额 | | -| UnwrittenOffAmount | decimal(18,4) | Y | | 未核销金额 | | -| RelatedDocNumber | varchar(64) | Y | | 关联单编号/采购订单编号 | | - | EventTimeUTC | datetime2 | N | | 付款事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactInvoice（开票申请事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| InvoiceId | bigint | N | autoincr | 主键 | | - | InvoiceRequestNumber | varchar(64) | N | | 发票申请编号（退化维度） | 来源：发票申请编号 | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | - | ContractNumber | varchar(64) | Y | | 合同编号（退化维度） | | -| HandlerId | bigint | Y | | 外键 → DimPerson（经办人） | | -| CustomerId | bigint | Y | | 外键 → DimCustomer | | - | InvoiceAmount | decimal(18,4) | Y | | 开票金额 | | -| ContractAmount | decimal(18,4) | Y | | 合同金额 | | - | InvoiceDateUTC | datetime2 | Y | | 开票日期（UTC） | | - | ActualInvoiceDateUTC | datetime2 | Y | | 开票日期（UTC） | | -| InvoiceType | varchar(50) | Y | | 开票类型 | | -| Stage | varchar(50) | Y | | 当前阶段 | | -| Result | varchar(50) | Y | | 结果 | | - | EventTimeUTC | datetime2 | N | | 开票事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactWriteOff（核销事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| WriteOffId | bigint | N | autoincr | 主键 | | - | WriteOffNumber | varchar(64) | N | | 核销单编号（退化维度） | 来源：核销单编号 | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | - | HandlerId | bigint | Y | | 经办人 (外键 → DimPerson) | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | SupplierId | bigint | Y | | 关联DimSupplier (外键 → DimSupplier) | | - | WriteOffType | varchar(30) | Y | | 核销类型 | AR,AP | - | CurrencyCode | varchar(16) | Y | | 币别 | | - | FxRate | decimal(18,6) | Y | | 汇率 | | - | DiscountAmount | decimal(18,4) | Y | | 优惠金额 | | - | WriteOffAmount | decimal(18,4) | Y | | 核销总金额 | | - | EventTimeUTC | datetime2 | N | | 核销事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactCashFlow（资金流水事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| CashFlowId | bigint | N | autoincr | 主键 | | - | DocNumber | varchar(64) | Y | | 单据编号（退化维度） | 来源：资金流向.单据编号/转账单编号 | - | DocType | varchar(30) | N | | 单据类型 | CASHFLOW,TRANSFER | - | AccountName | varchar(255) | Y | | 账户名称 | | - | DocDateUTC | datetime2 | Y | | 单据日期（UTC） | | - | BusinessType | varchar(100) | Y | | 业务类型 | | - | IncomeAmount | decimal(18,4) | Y | | 收入 | | - | ExpenseAmount | decimal(18,4) | Y | | 支出 | | - | BalanceAmount | decimal(18,4) | Y | | 余额 | | - | Counterparty | varchar(255) | Y | | 往来单位 | | - | Remark | varchar(500) | Y | | 单据备注 | | - | EventTimeUTC | datetime2 | N | | 资金事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactCommission（分佣事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| CommissionId | bigint | N | autoincr | 主键 | | - | CommissionPerson | varchar(100) | N | | 分佣人 | | - | Department | varchar(100) | Y | | 部门 | | - | CommissionRatePct | decimal(9,4) | Y | | 提成比例（%） | | - | Coefficient | decimal(18,6) | Y | | 系数 | | - | ReceivableCommission | decimal(18,4) | Y | | 应收佣金 | | - | ReceivedCommission | decimal(18,4) | Y | | 实收佣金 | | - | ContractId | bigint | Y | | 关联DimContract (外键 → DimContract) | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | ProductName | varchar(255) | Y | | 产品名称（退化） | | - | OpportunityName | varchar(255) | Y | | 商机名称（退化） | | - | ContractAmount | decimal(18,4) | Y | | 合同金额 | | - | DealTimeUTC | datetime2 | Y | | 成交时间（UTC） | | - | IsInvoiced | bit | Y | | 是否开票 | 0,1 | - | EventTimeUTC | datetime2 | N | | 分佣事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactLead（线索事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| LeadId | bigint | N | autoincr | 主键 | | - | LeadName | varchar(255) | N | | 线索名称 | | - | OwnerId | bigint | Y | | 负责人 (外键 → DimPerson) | | - | LeadSource | varchar(100) | Y | | 线索来源 | | -| Phone | varchar(32) | Y | | 手机 | | -| Telephone | varchar(32) | Y | | 电话 | | -| Email | varchar(100) | Y | | 邮箱 | | -| Address | varchar(500) | Y | | 地址 | | -| Industry | varchar(100) | Y | | 客户行业 | | -| CustomerLevel | varchar(50) | Y | | 客户级别 | | -| NextContactTimeUTC | datetime2 | Y | | 下次联系时间（UTC） | | -| FollowUpStatus | varchar(50) | Y | | 跟进状态 | | -| IsConverted | bit | Y | | 是否已转化 | 0,1 | -| Stage | varchar(50) | Y | | 当前阶段 | | -| Result | varchar(50) | Y | | 结果 | | - | EventTimeUTC | datetime2 | N | | 线索事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactOpportunity（商机事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| OpportunityId | bigint | N | autoincr | 主键 | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | OpportunityAmount | decimal(18,4) | Y | | 商机金额 | | - | ExpectedDealDateUTC | datetime2 | Y | | 预计成交日期（UTC） | | -| NextContactTimeUTC | datetime2 | Y | | 下次联系时间（UTC） | | -| StatusGroup | varchar(50) | Y | | 商机状态组 | | -| Stage | varchar(50) | Y | | 当前阶段 | | - | Result | varchar(50) | Y | | 结果 | | - | EventTimeUTC | datetime2 | N | | 商机事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -### FactProductionPlan（生产计划_装配工单汇总事实） -| 字段 | 类型 | 空 | 缺省 | 描述 | 枚举/单位 | -|---|---|---|---|---|---| -| ProductionPlanId | bigint | N | autoincr | 主键 | | - | AssemblyWorkOrderNumber | varchar(64) | N | | 装配工单编号（退化维度） | 来源：装配工单编号 | - | WorkOrderCount | int | Y | | 工单数 | | - | FinishedCount | int | Y | | 已结束数 | | - | PlannedQty | decimal(18,4) | Y | | 计划数 | | - | CompletedQty | decimal(18,4) | Y | | 完工数 | | - | PlanStatus | varchar(50) | Y | | 生产计划状态 | | - | ProgressText | varchar(100) | Y | | 工单生成/用料生成/单据进度（原文） | | - | CustomerName | varchar(255) | Y | | 客户（退化） | | - | CustomerId | bigint | Y | | 关联DimCustomer (外键 → DimCustomer) | | - | ProductId | bigint | Y | | 关联DimProduct (外键 → DimProduct) | | - | SalesOrderId | bigint | Y | | 关联FactSalesOrder (外键 → FactSalesOrder) | | - | EventTimeUTC | datetime2 | N | | 计划事件时间（UTC） | | -| SourceSystem | varchar(50) | Y | | 数据来源 | | -| LastUpdatedUTC | datetime2 | N | sysutcdatetime | 行级更新时间 | | - -## KPI 映射（示例） - -| KPI | 口径 | 表/字段 | -|---|---|---| -| 报工良率 | `sum(GoodQty) / nullif(sum(ReportQty),0)` | `FactLaborReport.GoodQty/ReportQty` | -| 质检良率 | `sum(PassQty) / nullif(sum(PassQty+FailQty),0)` | `FactQualityInspection.PassQty/FailQty` | -| 采购未付款 | `sum(UnpaidAmount)` | `FactPurchaseReceipt.UnpaidAmount` | -| AR 未核销 | `sum(UnwrittenOffAmount)` | `FactArReceipt.UnwrittenOffAmount` | -| AP 未核销 | `sum(UnwrittenOffAmount)` | `FactApPayment.UnwrittenOffAmount` | - - -## 数据字典 - -以下枚举值仅供参考，实际实施时应根据业务系统配置进行调整。 - -### 1. 通用类 (Common) - -| 字典代码 | 字典名称 | 枚举值 (Code: Name) | 适用字段 | -|---|---|---|---| -| **CurrencyCode** | 币种 | CNY: 人民币
USD: 美元
EUR: 欧元
JPY: 日元
HKD: 港币 | `FactSalesOrder.CurrencyCode`
`FactPurchaseOrder.CurrencyCode`
`FactArReceipt.CurrencyCode`
... | -| **UomCode** | 计量单位 | pcs: 个/件
kg: 千克
m: 米
L: 升
box: 箱
set: 套 | `DimProduct.UomCode`
`DimBom.UomCode`
`FactInventoryMovement.UomCode` | -| **Boolean** | 布尔标识 | 0: 否 (No/False)
1: 是 (Yes/True) | `IsCurrent`, `IsLocked`, `IsPrimary`, `IsReship`, `IsConverted`, `IsInvoiced` | - -### 2. 人员与组织 (Person & Org) - -| 字典代码 | 字典名称 | 枚举值 | 适用字段 | -|---|---|---|---| -| **PersonStatus** | 人员状态 | ACTIVE: 在职
INACTIVE: 离职
LEAVE: 休假 | `DimPerson.Status` | -| **Department** | 部门 | SALES: 销售部
MFG: 生产部
PUR: 采购部
WH: 仓储部
FIN: 财务部
QC: 质检部 | `DimPerson.Department` | -| **SupplierLevel** | 供应商等级 | STRATEGIC: 战略供应商
PREFERRED: 优选供应商
APPROVED: 合格供应商
PROBATION: 考察供应商
EXIT: 淘汰供应商 | `DimSupplier.SupplierLevel` | -| **CustomerLevel** | 客户等级 | KA: 重点客户
VIP: 重要客户
NORMAL: 普通客户
POTENTIAL: 潜在客户 | `DimCustomer.CustomerLevel`
`FactLead.CustomerLevel` | -| **Industry** | 行业 | MFG: 制造
RETAIL: 零售
IT: 互联网
FIN: 金融
EDU: 教育 | `DimCustomer.Industry`
`FactLead.Industry` | - -### 3. 生产与库存 (Production & Inventory) - -| 字典代码 | 字典名称 | 枚举值 | 适用字段 | -|---|---|---|---| -| **WorkOrderStatus** | 工单状态 | PLANNED: 计划中
RELEASED: 已下达
STARTED: 生产中
PAUSED: 暂停
COMPLETED: 已完工
CLOSED: 已结案
SCRAP: 报废 | `FactWorkOrder.Status` | -| **MovementType** | 出入库类型 | IN_PURCHASE: 采购入库
IN_FG: 完工入库
IN_RETURN: 销售退货入库
IN_OTHER: 其他入库
OUT_SALES: 销售出库
OUT_MATERIAL: 生产领料
OUT_RETURN: 采购退货出库
OUT_SCRAP: 报废出库
TRANSFER: 调拨 | `FactInventoryMovement.DocType` | -| **WarehouseType** | 仓库类型 | RAW: 原材料仓
FG: 成品仓
WIP: 线边仓/半成品仓
SCRAP: 废品仓
VMI: 供应商库存仓 | `DimWarehouse.WarehouseType` | -| **QcType** | 质检类型 | IQC: 来料检验
IPQC: 制程检验
FQC: 终检
OQC: 出货检验 | `FactQualityInspection.QcType` | -| **ProductType** | 产品类型 | FINISHED: 成品
SEMI: 半成品
RAW: 原材料
SERVICE: 服务 | `DimProduct.ProductType` | -| **TransportCondition** | 运输条件 | NORMAL: 常温
COLD: 冷链
FRAGILE: 易碎
HAZARDOUS: 危险品 | `DimProduct.TransportCondition` | -| **PlanStatus** | 计划状态 | DRAFT: 草稿
CONFIRMED: 已确认
EXECUTING: 执行中
COMPLETED: 已完成 | `FactProductionPlan.PlanStatus` | - -### 4. 销售与采购 (Sales & Purchase) - -| 字典代码 | 字典名称 | 枚举值 | 适用字段 | -|---|---|---|---| -| **LeadSource** | 线索来源 | WEB: 官网
REFERRAL: 推荐
EXHIBITION: 展会
COLD_CALL: 陌拜
OTHER: 其他 | `FactLead.LeadSource` | -| **SalesStage** | 销售阶段 | NEW: 新建
QUOTED: 已报价
NEGOTIATING: 谈判中
WON: 赢单
LOST: 输单 | `FactOpportunity.Stage`
`FactLead.Stage` | -| **OppStatusGroup** | 商机状态组 | OPEN: 开启
WON: 赢单
LOST: 输单 | `FactOpportunity.StatusGroup` | -| **PurchaseStatus** | 采购状态 | DRAFT: 草稿
APPROVED: 已审批
SENT: 已发送
PARTIAL_RECEIVED: 部分收货
RECEIVED: 全部收货
CLOSED: 关闭 | `FactPurchaseRequest.Status` | -| **PaymentStatus** | 支付状态 | UNPAID: 未付款
PARTIAL: 部分付款
PAID: 已付款
OVERDUE: 逾期 | `FactSalesOrder.PaymentStatus`
`FactPurchaseReceipt.PaymentStatus` | -| **TransportMode** | 运输方式 | LAND: 陆运
SEA: 海运
AIR: 空运
RAIL: 铁运
EXPRESS: 快递 | `FactSalesOrder.TransportMode`
`FactSalesShipment.TransportMode` | - -### 5. 财务 (Finance) - -| 字典代码 | 字典名称 | 枚举值 | 适用字段 | -|---|---|---|---| -| **FinDocType** | 财务单据类型 | RECEIPT: 收款单
PAYMENT: 付款单
INVOICE: 发票
REFUND: 退款
ADVANCE: 预收/预付
WRITEOFF: 核销 | `FactArReceipt.DocType`
`FactApPayment.DocType` | -| **WriteOffType** | 核销类型 | AR: 应收核销
AP: 应付核销 | `FactWriteOff.WriteOffType` | -| **InvoiceType** | 发票类型 | VAT_SPECIAL: 增值税专票
VAT_NORMAL: 增值税普票
ELECTRONIC: 电子发票 | `FactInvoice.InvoiceType` | - - -## 变更记录 - -| 版本 | 日期 | 作者 | 变更描述 | 脚本影响 | -|---|---|---|---|---| -| v1.0.0 | 2025-12-25 | GPT-5.2 | 首次输出：维度SCD2 + 事实星座 + ER图 + 字段清单 | 未生成 | -| v1.1.0 | 2025-12-28 | Trae | 新增 DimPerson（人员维度）并关联销售、采购、生产、审批等事实表 | 待生成 | -| v1.2.0 | 2025-12-29 | Trae | 新增「业务数据流转全景图」，包含商机-订单-计划-生产-交付全链路 Mermaid 流程图 | 无 | -| v1.3.0 | 2025-12-29 | Trae | 升级全景图，新增外部采购闭环（PO管理、入库验收IQC、三单匹配AP、供应商管理） | 无 | -| v1.4.0 | 2025-12-29 | Trae | 升级全景图，新增财务与资金阶段（开票Invoice、收付款AP/AR、核销WriteOff、资金CashFlow） | 无 | -| v1.5.0 | 2025-12-30 | Trae | 新增「数据字典」章节，规范化通用类、人员组织、生产库存、销售采购及财务类枚举值 | 无 | diff --git a/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml b/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml deleted file mode 100644 index 25ea00e..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml +++ /dev/null @@ -1,38 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai-mcpskills-mfg-data-agentv2" -version = "0.1.6" -description = "制造业数据智能体 - MCP server for manufacturing data intelligence with dynamic tool generation" -readme = "README.md" -requires-python = ">=3.10" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "sql", "manufacturing", "data", "agent", "智能体"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", - "pypinyin>=0.53.0", -] - -[project.scripts] -lzwcai-mcpskills-mfg-data-agentv2 = "lzwcai_mcpskills_mfg_data_agentv2.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcpskills_mfg_data_agentv2"] - -[tool.hatch.build.targets.wheel.force-include] -"lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json" = "lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json" diff --git a/lzwcai_mcpskills_mfg_data_agentv2/技能清单.md b/lzwcai_mcpskills_mfg_data_agentv2/技能清单.md deleted file mode 100644 index 61e84aa..0000000 --- a/lzwcai_mcpskills_mfg_data_agentv2/技能清单.md +++ /dev/null @@ -1,10 +0,0 @@ -# 制造业数据智能分析技能清单 - -| 技能名称 | 编码 | 技能描述 | -|---------|------|---------| -| 供应商评估与智能补货 | SupplierEvaluationAndSmartReplenishment | 基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量 | -| 销售BI智能分析平台 | SalesBIIntelligentAnalyticsPlatform | 面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察 | -| 销售业绩智能统计系统 | SalesPerformanceIntelligentStatistics | 基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名 | -| 财务数据分析看板 | FinancialAnalyticsDashboard | 覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解 | -| 智能成本预测模型 | SmartCostPredictionModel | 基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演 | -| 人力资源数据分析 | HumanResourcesAnalytics | 提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析 | diff --git a/lzwcai_mcpskills_template/README.md b/lzwcai_mcpskills_template/README.md deleted file mode 100644 index 7ffaa1f..0000000 --- a/lzwcai_mcpskills_template/README.md +++ /dev/null @@ -1,65 +0,0 @@ -# lzwcai-mcpskills-template - -MCP Server 模板项目，用于快速创建新的 MCP 服务。 - -## 功能 - -- 提供 MCP Server 基础框架 -- 支持动态工具注册 -- 内置日志系统 -- 支持环境变量配置 - -## 安装 - -```bash -pip install lzwcai-mcpskills-template -``` - -## 使用 - -```bash -# 设置环境变量 -export API_KEY="your-api-key" -export BASE_URL="http://your-api-server" - -# 运行 -lzwcai-mcpskills-template -``` - -## 环境变量 - -| 变量名 | 说明 | 默认值 | -|--------|------|--------| -| API_KEY | API密钥 | - | -| BASE_URL | 后端API地址 | http://localhost:8080 | - -## 项目结构 - -``` -lzwcai_mcpskills_template/ -├── main.py # 入口文件 -├── pyproject.toml # 项目配置 -├── README.md # 说明文档 -└── lzwcai_mcpskills_template/ # 核心代码 - ├── main.py # MCP Server 主逻辑 - ├── schema_converter.py # Schema 转换器 - └── utils/ # 工具模块 - ├── __init__.py - ├── api_client.py # API 客户端 - ├── env_config.py # 环境变量配置 - ├── json_helper.py # JSON 工具 - └── logger_config.py # 日志配置 -``` - -## 开发 - -基于此模板创建新项目： - -1. 复制整个目录 -2. 修改 `pyproject.toml` 中的项目名称 -3. 修改 `lzwcai_mcpskills_template` 目录名 -4. 在 `main.py` 中实现你的工具逻辑 - -## License - -MIT diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/.python-version b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/.python-version deleted file mode 100644 index 24ee5b1..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.13 diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/__pycache__/main.cpython-312.pyc b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/__pycache__/main.cpython-312.pyc deleted file mode 100644 index b1c8a03a6d62eb52ba019d65281f7425c22794a8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 14593 zcmcJ0Yjjg(y71n~&i&f^y`)f(AT76Y712@(lwt(|l`1C_vo|Frm%5Vxt?Af798yIo 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zlCJvh+x5t7yKbsWnCieKg1G62>c_TDY)x3VCym>|EyC120blT6R%|UtUzFEuZBl;G zNI@&9!8|=&@BA)t97z7ZD)&;(8TM0x1!{x&>t(v6US6;kX4H9y2M)1V(G}d?5FMeQ zMu8fb(VV0ALgp2I1H>oiy;yO`|3Q#)VXnv72kD?)t|N-qcdnn2Ed@1436so~k7qx) zP^9>t3xEF(5|1d+9^QKlvQ;}U z;PEMTs`$;gk1qycOYRYm{k?$W7P%Vo#aNcV7{Pcm;j@Ho(7v2^nZ0 z!LlKv<&68JJJu6Flrk)jwx%uBBd)Wqcvs4@DrxB$J$hyEy}|LuFBPfg4oH~Ft436eoeOYl_(#pOG$Hg!n%7qz} zWN!PQR*olbd)sXs;hhS}$OeQk&w9KF^t1o=JOMo3zyt>r&ZE;JWQ|~ zfyNI@xTXX1yN2(WU+sM zLD-SPUr_L;7t{t!*dG$!gMTCN3U+7rw43}vChTLMA(7+|9s5=TBMc=g{ z%J_FwnN|Ogq8=G-%_96B+dsa3f}UW;Hzzi9+$B@l!-^)VdTjl;?E`lfL3g75E`DX7 zqbevhCWP16zPtF9U59An@L*C|BYQXVV*L4R)sl6^9fR7Uj<(-1qoq%d_obHXOwhH_ z*5QWO%CBkNa7Ubu9i7xKouZfF;f`2mY~M)d+0OIovHcTkrz*Bh+S(@d+o$Lq^71pW S_DTJ+uV{OAF`{dU-v0qEIt -2026-01-28 09:58:34 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-28 09:58:34 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest -2026-01-28 09:58:34 - lzwcai_mcpskills_template.main - INFO - [main.py:142] - 收到 ListTools 请求，当前配置数量: 7 -2026-01-28 09:58:34 - lzwcai_mcpskills_template.main - INFO - [main.py:161] - ListTools 响应: 返回 7 个工具 -2026-01-28 09:58:34 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:58:36 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:58:36 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:58:36 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:58:36 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_mixed, arguments=None -2026-01-28 09:58:36 - lzwcai_mcpskills_template.main - INFO - [main.py:187] - 工具执行成功: demo_mixed -2026-01-28 09:58:36 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:58:55 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:58:55 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:58:55 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:58:55 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_error, arguments=None -2026-01-28 09:58:55 - lzwcai_mcpskills_template.main - ERROR - [main.py:190] - 工具执行失败: 这是一个演示用的模拟错误！执行被中断。 -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\main.py", line 186, in handle_call_tool - result_contents = execute_tool(name, arguments or {}, tool_config) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\main.py", line 259, in execute_tool - raise ValueError("这是一个演示用的模拟错误！执行被中断。") -ValueError: 这是一个演示用的模拟错误！执行被中断。 -2026-01-28 09:58:55 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:59:01 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:59:01 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:59:01 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:59:01 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_resource, arguments=None -2026-01-28 09:59:01 - lzwcai_mcpskills_template.main - INFO - [main.py:187] - 工具执行成功: demo_resource -2026-01-28 09:59:01 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:59:04 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:59:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:59:04 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:59:04 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_image, arguments=None -2026-01-28 09:59:04 - lzwcai_mcpskills_template.main - INFO - [main.py:187] - 工具执行成功: demo_image -2026-01-28 09:59:04 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:59:09 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:59:09 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:59:09 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:59:09 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_image, arguments=None -2026-01-28 09:59:09 - lzwcai_mcpskills_template.main - INFO - [main.py:187] - 工具执行成功: demo_image -2026-01-28 09:59:09 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 10:02:13 - root - INFO - [logger_config.py:112] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\logs -2026-01-28 10:02:13 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'mcpskills_template_server' -2026-01-28 10:02:13 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest -2026-01-28 10:02:13 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:322] - ================================================== -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:323] - MCP Skills Template Server 启动 -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:324] - ================================================== -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:329] - 命令行参数: {'mode': 'local', 'json_path': None} -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:332] - 使用模式: local -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:83] - 从本地加载 8 条配置: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\tools_config.json -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:137] - 已加载 8 个工具配置 -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:339] - 开始运行 MCP Server (stdio 模式) -2026-01-28 10:02:13 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor -2026-01-28 10:02:13 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0') -2026-01-28 10:02:40 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 10:02:40 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-28 10:02:40 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest -2026-01-28 10:02:40 - lzwcai_mcpskills_template.main - INFO - [main.py:143] - 收到 ListTools 请求，当前配置数量: 8 -2026-01-28 10:02:40 - lzwcai_mcpskills_template.main - INFO - [main.py:162] - ListTools 响应: 返回 8 个工具 -2026-01-28 10:02:40 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 10:02:44 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 10:02:44 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 10:02:44 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 10:02:44 - lzwcai_mcpskills_template.main - INFO - [main.py:172] - 收到 CallTool 请求: name=demo_complex, arguments={"user_info": {}} -2026-01-28 10:02:44 - lzwcai_mcpskills_template.main - INFO - [main.py:189] - 工具执行成功: demo_complex -2026-01-28 10:02:44 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 10:02:50 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 10:02:50 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 10:02:50 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 10:02:50 - lzwcai_mcpskills_template.main - INFO - [main.py:172] - 收到 CallTool 请求: name=demo_mixed, arguments=None -2026-01-28 10:02:50 - lzwcai_mcpskills_template.main - INFO - [main.py:189] - 工具执行成功: demo_mixed -2026-01-28 10:02:50 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/logs/lzwcai_mcpskills_template_daily.log b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/logs/lzwcai_mcpskills_template_daily.log deleted file mode 100644 index 14e452d..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/logs/lzwcai_mcpskills_template_daily.log +++ /dev/null @@ -1,89 +0,0 @@ -2026-01-28 09:58:33 - root - INFO - [logger_config.py:112] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\logs -2026-01-28 09:58:33 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'mcpskills_template_server' -2026-01-28 09:58:33 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest -2026-01-28 09:58:33 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest -2026-01-28 09:58:33 - lzwcai_mcpskills_template.main - INFO - [main.py:304] - ================================================== -2026-01-28 09:58:33 - lzwcai_mcpskills_template.main - INFO - [main.py:305] - MCP Skills Template Server 启动 -2026-01-28 09:58:33 - lzwcai_mcpskills_template.main - INFO - [main.py:306] - ================================================== -2026-01-28 09:58:33 - lzwcai_mcpskills_template.main - INFO - [main.py:311] - 命令行参数: {'mode': 'local', 'json_path': None} -2026-01-28 09:58:33 - lzwcai_mcpskills_template.main - INFO - [main.py:314] - 使用模式: local -2026-01-28 09:58:33 - lzwcai_mcpskills_template.main - INFO - [main.py:82] - 从本地加载 7 条配置: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\tools_config.json -2026-01-28 09:58:33 - lzwcai_mcpskills_template.main - INFO - [main.py:136] - 已加载 7 个工具配置 -2026-01-28 09:58:33 - lzwcai_mcpskills_template.main - INFO - [main.py:321] - 开始运行 MCP Server (stdio 模式) -2026-01-28 09:58:33 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor -2026-01-28 09:58:33 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0') -2026-01-28 09:58:34 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:58:34 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-28 09:58:34 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest -2026-01-28 09:58:34 - lzwcai_mcpskills_template.main - INFO - [main.py:142] - 收到 ListTools 请求，当前配置数量: 7 -2026-01-28 09:58:34 - lzwcai_mcpskills_template.main - INFO - [main.py:161] - ListTools 响应: 返回 7 个工具 -2026-01-28 09:58:34 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:58:36 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:58:36 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:58:36 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:58:36 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_mixed, arguments=None -2026-01-28 09:58:36 - lzwcai_mcpskills_template.main - INFO - [main.py:187] - 工具执行成功: demo_mixed -2026-01-28 09:58:36 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:58:55 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:58:55 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:58:55 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:58:55 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_error, arguments=None -2026-01-28 09:58:55 - lzwcai_mcpskills_template.main - ERROR - [main.py:190] - 工具执行失败: 这是一个演示用的模拟错误！执行被中断。 -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\main.py", line 186, in handle_call_tool - result_contents = execute_tool(name, arguments or {}, tool_config) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\main.py", line 259, in execute_tool - raise ValueError("这是一个演示用的模拟错误！执行被中断。") -ValueError: 这是一个演示用的模拟错误！执行被中断。 -2026-01-28 09:58:55 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:59:01 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:59:01 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:59:01 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:59:01 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_resource, arguments=None -2026-01-28 09:59:01 - lzwcai_mcpskills_template.main - INFO - [main.py:187] - 工具执行成功: demo_resource -2026-01-28 09:59:01 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:59:04 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:59:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:59:04 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:59:04 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_image, arguments=None -2026-01-28 09:59:04 - lzwcai_mcpskills_template.main - INFO - [main.py:187] - 工具执行成功: demo_image -2026-01-28 09:59:04 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 09:59:09 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 09:59:09 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 09:59:09 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 09:59:09 - lzwcai_mcpskills_template.main - INFO - [main.py:171] - 收到 CallTool 请求: name=demo_image, arguments=None -2026-01-28 09:59:09 - lzwcai_mcpskills_template.main - INFO - [main.py:187] - 工具执行成功: demo_image -2026-01-28 09:59:09 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 10:02:13 - root - INFO - [logger_config.py:112] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\logs -2026-01-28 10:02:13 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'mcpskills_template_server' -2026-01-28 10:02:13 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest -2026-01-28 10:02:13 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:322] - ================================================== -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:323] - MCP Skills Template Server 启动 -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:324] - ================================================== -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:329] - 命令行参数: {'mode': 'local', 'json_path': None} -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:332] - 使用模式: local -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:83] - 从本地加载 8 条配置: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\tools_config.json -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:137] - 已加载 8 个工具配置 -2026-01-28 10:02:13 - lzwcai_mcpskills_template.main - INFO - [main.py:339] - 开始运行 MCP Server (stdio 模式) -2026-01-28 10:02:13 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor -2026-01-28 10:02:13 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0') -2026-01-28 10:02:40 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 10:02:40 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest -2026-01-28 10:02:40 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest -2026-01-28 10:02:40 - lzwcai_mcpskills_template.main - INFO - [main.py:143] - 收到 ListTools 请求，当前配置数量: 8 -2026-01-28 10:02:40 - lzwcai_mcpskills_template.main - INFO - [main.py:162] - ListTools 响应: 返回 8 个工具 -2026-01-28 10:02:40 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 10:02:44 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 10:02:44 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 10:02:44 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 10:02:44 - lzwcai_mcpskills_template.main - INFO - [main.py:172] - 收到 CallTool 请求: name=demo_complex, arguments={"user_info": {}} -2026-01-28 10:02:44 - lzwcai_mcpskills_template.main - INFO - [main.py:189] - 工具执行成功: demo_complex -2026-01-28 10:02:44 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent -2026-01-28 10:02:50 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: -2026-01-28 10:02:50 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest -2026-01-28 10:02:50 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest -2026-01-28 10:02:50 - lzwcai_mcpskills_template.main - INFO - [main.py:172] - 收到 CallTool 请求: name=demo_mixed, arguments=None -2026-01-28 10:02:50 - lzwcai_mcpskills_template.main - INFO - [main.py:189] - 工具执行成功: demo_mixed -2026-01-28 10:02:50 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/logs/lzwcai_mcpskills_template_error.log b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/logs/lzwcai_mcpskills_template_error.log deleted file mode 100644 index d73ec05..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/logs/lzwcai_mcpskills_template_error.log +++ /dev/null @@ -1,8 +0,0 @@ -2026-01-28 09:58:55 - lzwcai_mcpskills_template.main - ERROR - [main.py:190] - 工具执行失败: 这是一个演示用的模拟错误！执行被中断。 -Traceback (most recent call last): - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\main.py", line 186, in handle_call_tool - result_contents = execute_tool(name, arguments or {}, tool_config) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_template\lzwcai_mcpskills_template\main.py", line 259, in execute_tool - raise ValueError("这是一个演示用的模拟错误！执行被中断。") -ValueError: 这是一个演示用的模拟错误！执行被中断。 diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/main.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/main.py deleted file mode 100644 index 2cf6e94..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/main.py +++ /dev/null @@ -1,345 +0,0 @@ -#!/usr/bin/env python3 -""" -MCP Server 模板 -支持从本地 JSON 或 API 动态加载工具配置 - -使用方式: -- local: 从本地 JSON 文件加载工具配置 -- api: 从远程 API 加载工具配置 -""" -import json -import os -import logging -import argparse -import anyio -import asyncio - -import mcp.types as types -from mcp.server import NotificationOptions, Server -from mcp.server.models import InitializationOptions -from mcp.server.stdio import stdio_server - -try: - from .schema_converter import convert_sql_params_to_input_schema - from .utils.api_client import call_api - from .utils.env_config import get_api_key, get_base_url - from .utils.logger_config import setup_system_logging, get_logger -except ImportError: - from schema_converter import convert_sql_params_to_input_schema - from utils.api_client import call_api - from utils.env_config import get_api_key, get_base_url - from utils.logger_config import setup_system_logging, get_logger - -# 初始化日志系统 -setup_system_logging(app_name="lzwcai_mcpskills_template", log_level=logging.DEBUG) -logger = get_logger(__name__) - -# 初始化 MCP Server -server = Server("mcpskills_template_server") - -# 全局配置 -_tools_config: list[dict] = [] -_config: dict = {} - - -def parse_arguments(): - """解析命令行参数""" - parser = argparse.ArgumentParser(description="MCP Skills Template Server") - parser.add_argument( - "--mode", - type=str, - choices=["local", "api"], - default="local", - help="数据加载模式: local(本地JSON，默认) 或 api(远程API)" - ) - parser.add_argument( - "--json-path", - type=str, - default=None, - help="本地 JSON 文件路径 (local 模式)" - ) - return parser.parse_args() - - -class DataLoader: - """数据加载器基类""" - - def load(self) -> list[dict]: - raise NotImplementedError - - -class LocalLoader(DataLoader): - """本地 JSON 文件加载器""" - - def __init__(self, json_path: str = None): - if json_path is None: - json_path = os.path.join(os.path.dirname(__file__), "tools_config.json") - self.json_path = json_path - - def load(self) -> list[dict]: - try: - with open(self.json_path, "r", encoding="utf-8") as f: - data = json.load(f) - logger.info(f"从本地加载 {len(data)} 条配置: {self.json_path}") - return data - except FileNotFoundError: - logger.error(f"配置文件不存在: {self.json_path}") - return [] - except json.JSONDecodeError as e: - logger.error(f"JSON 解析错误: {e}") - return [] - - -class ApiLoader(DataLoader): - """API 远程加载器""" - - def __init__(self): - self.base_url = get_base_url() - self.api_key = get_api_key() - logger.debug(f"ApiLoader 初始化，base_url: {self.base_url}") - - def load(self) -> list[dict]: - try: - # TODO: 根据实际 API 修改此处 - logger.info(f"开始从 API 加载工具配置") - response = call_api("/api/tools", method="GET") - - if response.get("code") != 200: - logger.error(f"获取工具配置失败: {response.get('msg')}") - return [] - - data = response.get("data", []) - if isinstance(data, dict): - data = [data] - - logger.info(f"从 API 加载工具配置成功，数量: {len(data)}") - return data - - except Exception as e: - logger.error(f"API 请求失败: {e}", exc_info=True) - return [] - - -def create_loader(mode: str, **kwargs) -> DataLoader: - """创建数据加载器""" - if mode == "local": - return LocalLoader(json_path=kwargs.get("json_path")) - elif mode == "api": - return ApiLoader() - else: - raise ValueError(f"不支持的模式: {mode}") - - -def init_tools(loader: DataLoader): - """初始化工具配置""" - global _tools_config - _tools_config = loader.load() - logger.info(f"已加载 {len(_tools_config)} 个工具配置") - - -@server.list_tools() -async def handle_list_tools() -> list[types.Tool]: - """列出所有可用工具""" - logger.info(f"收到 ListTools 请求，当前配置数量: {len(_tools_config)}") - tools = [] - - for tool in _tools_config: - name = tool.get("name", "") - description = tool.get("description") or tool.get("toolPrompt", "") - sql_params = tool.get("sqlParams", "[]") - - # 转换参数为 inputSchema - input_schema = convert_sql_params_to_input_schema(sql_params) - - tools.append( - types.Tool( - name=name, - description=description, - inputSchema=input_schema - ) - ) - - logger.info(f"ListTools 响应: 返回 {len(tools)} 个工具") - return tools - - -@server.call_tool() -async def handle_call_tool( - name: str, - arguments: dict | None -) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: - """调用工具""" - logger.info(f"收到 CallTool 请求: name={name}, arguments={json.dumps(arguments, ensure_ascii=False) if arguments else 'None'}") - - # 查找对应的工具配置 - tool_config = None - for tool in _tools_config: - if tool.get("name") == name: - tool_config = tool - break - - if tool_config is None: - logger.error(f"未找到工具配置: {name}") - raise ValueError(f"未知工具: {name}") - - # TODO: 在这里实现你的工具逻辑 - try: - # execute_tool 现在是 async 的 - result_contents = await execute_tool(name, arguments or {}, tool_config) - logger.info(f"工具执行成功: {name}") - return result_contents - except Exception as e: - logger.error(f"工具执行失败: {e}", exc_info=True) - # 重新抛出异常，以便 MCP SDK 能够捕获并将其格式化为错误响应 - raise e - - -async def execute_tool(name: str, arguments: dict, config: dict) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: - """ - 执行工具逻辑并返回 MCP 内容列表 (Async) - """ - # 1. 示例：Hello World (返回纯文本) - if name == "example_hello_world": - msg = f"Hello, {arguments.get('name', 'World')}!" - return [types.TextContent(type="text", text=msg)] - - # 2. 示例：计算器 (返回 JSON 格式的文本) - elif name == "example_calculator": - a = float(arguments.get("a", 0)) - b = float(arguments.get("b", 0)) - op = arguments.get("operation", "+") - - if op == "+": - result = a + b - elif op == "-": - result = a - b - elif op == "*": - result = a * b - elif op == "/": - result = a / b if b != 0 else "除数不能为0" - else: - result = "未知运算符" - - output = {"result": result, "expression": f"{a} {op} {b}"} - return [types.TextContent(type="text", text=json.dumps(output, ensure_ascii=False))] - - # 3. 演示：纯文本 - elif name == "demo_text": - return [types.TextContent(type="text", text="这是一个标准的纯文本输出示例。")] - - # 4. 演示：图片输出 - elif name == "demo_image": - # 这是一个 1x1 的红色像素点的 Base64 - red_dot_base64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==" - return [ - types.ImageContent( - type="image", - data=red_dot_base64, - mimeType="image/png" - ) - ] - - # 5. 演示：资源输出 - elif name == "demo_resource": - # 模拟返回一个嵌入式资源 - return [ - types.EmbeddedResource( - type="resource", - resource=types.TextResourceContents( - uri="file:///logs/app.log", - text="[INFO] System started\n[WARN] Low memory", - mimeType="text/plain" - ) - ) - ] - - # 6. 演示：错误状态 (带 observation) - elif name == "demo_error": - # 尝试模拟用户要求的带 observation 的错误结构 - # 注意：标准 MCP 协议中 TextContent 可能不支持 observation 字段 - # 这里演示如何抛出异常，这是最标准的错误反馈方式 - raise ValueError("执行失败: 网络超时，请检查连接") - - # 如果客户端支持非标准字段，可以尝试返回如下结构（需 SDK 支持）： - # return [ - # types.TextContent(type="text", text="执行失败") # 无法直接添加 observation - # ] - - # 7. 演示：混合输出 (文本 + 图片) - elif name == "demo_mixed": - red_dot_base64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==" - return [ - types.TextContent(type="text", text="下面是一张图片："), - types.ImageContent( - type="image", - data=red_dot_base64, - mimeType="image/png" - ), - types.TextContent(type="text", text="图片展示完毕。") - ] - - # 8. 演示：复杂参数 - elif name == "demo_complex": - user_info = arguments.get("user_info", {}) - tags = arguments.get("tags", []) - - # 模拟异步处理 - await asyncio.sleep(0.1) - - result_text = f"接收到复杂参数:\n用户: {json.dumps(user_info, ensure_ascii=False)}\n标签: {tags}" - return [types.TextContent(type="text", text=result_text)] - - # 默认返回参数 - default_result = { - "tool_name": name, - "arguments": arguments, - "message": "工具执行成功（默认实现）" - } - return [types.TextContent(type="text", text=json.dumps(default_result, ensure_ascii=False, indent=2))] - - -async def run_server(): - """运行 MCP Server (stdio 模式)""" - async with stdio_server() as streams: - await server.run( - streams[0], - streams[1], - InitializationOptions( - server_name="mcpskills_template_server", - server_version="0.1.0", - capabilities=server.get_capabilities( - notification_options=NotificationOptions(), - experimental_capabilities={}, - ), - ), - ) - - -def main(): - """主入口""" - global _config - - logger.info("=" * 50) - logger.info("MCP Skills Template Server 启动") - logger.info("=" * 50) - - # 解析命令行参数 - args = parse_arguments() - _config = vars(args) - logger.info(f"命令行参数: {_config}") - - # 创建加载器并初始化工具 - logger.info(f"使用模式: {args.mode}") - loader = create_loader( - mode=args.mode, - json_path=args.json_path - ) - init_tools(loader) - - logger.info("开始运行 MCP Server (stdio 模式)") - # 运行服务器 - anyio.run(run_server) - - -if __name__ == "__main__": - main() diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/schema_converter.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/schema_converter.py deleted file mode 100644 index 64423d4..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/schema_converter.py +++ /dev/null @@ -1,185 +0,0 @@ -""" -Schema 转换器 -将 sqlParams 数组格式转换为 MCP 工具需要的 JSON Schema 格式 - -支持的类型: -- string: 文本输入 -- paragraph: 段落/多行文本 -- select: 下拉选项 -- number: 数字输入 -""" -import json -from typing import Any - - -def convert_param_to_schema_property(param: dict) -> tuple[str, dict, bool]: - """ - 将单个参数转换为 JSON Schema property - - Args: - param: 参数配置，格式如: - { - "type": "string", - "name": "param_name", - "displayName": "参数显示名", - "maxLength": 200, - "defaultValue": "", - "required": true, - "options": ["选项1", "选项2"] # 仅 select 类型 - } - - Returns: - tuple: (property_name, property_schema, is_required) - """ - param_type = param.get("type", "string") - param_name = param.get("name", "") - display_name = param.get("displayName", param_name) - default_value = param.get("defaultValue", "") - max_length = param.get("maxLength") - is_required = param.get("required", False) - options = param.get("options", []) - - property_schema = { - "description": display_name - } - - if param_type == "string": - property_schema["type"] = "string" - if max_length: - property_schema["maxLength"] = max_length - - elif param_type == "paragraph": - property_schema["type"] = "string" - property_schema["format"] = "paragraph" - if max_length: - property_schema["maxLength"] = max_length - - elif param_type == "select": - property_schema["type"] = "string" - if options: - property_schema["enum"] = options - - elif param_type == "number": - property_schema["type"] = "number" - - elif param_type == "boolean": - property_schema["type"] = "boolean" - # Boolean default handling - if default_value is not None and str(default_value).lower() in ("true", "false", "1", "0"): - property_schema["default"] = str(default_value).lower() in ("true", "1") - - elif param_type == "array": - property_schema["type"] = "array" - # Simple array of strings by default if no item type specified - # For more complex arrays, we might need extended config in sqlParams - property_schema["items"] = {"type": "string"} - - elif param_type == "object": - property_schema["type"] = "object" - # Allow any object structure by default - property_schema["additionalProperties"] = True - - else: - # 默认当作 string 处理 - property_schema["type"] = "string" - - # 添加默认值 - if default_value not in (None, ""): - if param_type == "number": - try: - property_schema["default"] = int(default_value) if str(default_value).isdigit() else float(default_value) - except (ValueError, TypeError): - property_schema["default"] = default_value - else: - property_schema["default"] = default_value - - return param_name, property_schema, is_required - - -def convert_sql_params_to_input_schema(sql_params: str | list) -> dict: - """ - 将 sqlParams 转换为 MCP 工具的 inputSchema - - Args: - sql_params: sqlParams 字段值，可以是 JSON 字符串或已解析的列表 - 格式: [{"type": "string", "name": "xxx", ...}, ...] - - Returns: - dict: MCP 工具的 inputSchema，格式如: - { - "type": "object", - "properties": {...}, - "required": [...] - } - """ - # 解析 JSON 字符串 - if isinstance(sql_params, str): - try: - params_list = json.loads(sql_params) - except json.JSONDecodeError: - return {"type": "object", "properties": {}, "required": []} - else: - params_list = sql_params - - if not isinstance(params_list, list): - return {"type": "object", "properties": {}, "required": []} - - input_schema = { - "type": "object", - "properties": {}, - "required": [] - } - - for param in params_list: - if not isinstance(param, dict): - continue - - name, schema, is_required = convert_param_to_schema_property(param) - - if name: - input_schema["properties"][name] = schema - if is_required: - input_schema["required"].append(name) - - return input_schema - - -def convert_tool_config_to_mcp_tool(config: dict) -> dict: - """ - 将单个工具配置转换为 MCP Tool 配置 - - Args: - config: 工具配置对象 - - Returns: - dict: MCP Tool 配置 - """ - name = config.get("name", "") - description = config.get("description") or config.get("toolPrompt", "") - sql_params = config.get("sqlParams", "[]") - - input_schema = convert_sql_params_to_input_schema(sql_params) - - return { - "name": name, - "description": description, - "inputSchema": input_schema, - "_raw": config # 保留原始数据 - } - - -# 测试用 -if __name__ == "__main__": - # 测试单个参数转换 - test_param = { - "type": "select", - "name": "operation", - "displayName": "运算符", - "required": True, - "options": ["+", "-", "*", "/"] - } - - name, schema, required = convert_param_to_schema_property(test_param) - print(f"参数名: {name}") - print(f"Schema: {json.dumps(schema, ensure_ascii=False, indent=2)}") - print(f"必填: {required}") diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/tools_config.json b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/tools_config.json deleted file mode 100644 index b642048..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/tools_config.json +++ /dev/null @@ -1,58 +0,0 @@ -[ - { - "id": "example_tool_001", - "name": "example_hello_world2", - "description": "示例工具 - Hello World2", - "toolPrompt": "这是一个示例工具，用于演示 MCP 工具的基本结构", - "sqlParams": "[{\"type\":\"string\",\"name\":\"name2\",\"displayName\":\"名称\",\"maxLength\":100,\"defaultValue\":\"World\",\"required\":true}]" - }, - { - "id": "example_tool_002", - "name": "example_calculator", - "description": "示例工具 - 简单计算器", - "toolPrompt": "执行简单的数学计算", - "sqlParams": "[{\"type\":\"number\",\"name\":\"a\",\"displayName\":\"数字A\",\"required\":true},{\"type\":\"number\",\"name\":\"b\",\"displayName\":\"数字B\",\"required\":true},{\"type\":\"select\",\"name\":\"operation\",\"displayName\":\"运算符\",\"required\":true,\"options\":[\"+\",\"-\",\"*\",\"/\"]}]" - }, - { - "id": "demo_001", - "name": "demo_text", - "description": "演示纯文本输出", - "toolPrompt": "返回一段简单的文本信息", - "sqlParams": "[]" - }, - { - "id": "demo_002", - "name": "demo_image", - "description": "演示图片输出", - "toolPrompt": "返回一张 Base64 编码的图片", - "sqlParams": "[]" - }, - { - "id": "demo_003", - "name": "demo_resource", - "description": "演示资源输出", - "toolPrompt": "返回一个嵌入式资源", - "sqlParams": "[]" - }, - { - "id": "demo_004", - "name": "demo_error", - "description": "演示错误状态", - "toolPrompt": "触发一个执行错误", - "sqlParams": "[]" - }, - { - "id": "demo_005", - "name": "demo_mixed", - "description": "演示混合输出", - "toolPrompt": "同时返回文本和图片", - "sqlParams": "[]" - }, - { - "id": "demo_006", - "name": "demo_complex", - "description": "演示复杂参数输入", - "toolPrompt": "接受对象和数组类型的参数", - "sqlParams": "[{\"type\":\"object\",\"name\":\"user_info\",\"displayName\":\"用户信息\",\"required\":true},{\"type\":\"array\",\"name\":\"tags\",\"displayName\":\"标签列表\",\"required\":false}]" - } -] \ No newline at end of file diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/__init__.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/__init__.py deleted file mode 100644 index 1b74368..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Utils package for lzwcai_mcpskills_template""" - -from .json_helper import load_json -from .api_client import call_api, APIClient, get_default_client -from .env_config import get_api_key, get_base_url, get_env_config, set_env_variable -from .logger_config import setup_system_logging, get_logger - -__all__ = [ - 'load_json', - 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+0,0 @@ -""" -API 调用客户端 -通用的 HTTP API 客户端封装 -""" - -import httpx -import json -from typing import Dict, Any, Optional - -try: - from .env_config import get_base_url, get_api_key - from .logger_config import get_logger -except ImportError: - from env_config import get_base_url, get_api_key - from logger_config import get_logger - -logger = get_logger(__name__) - -# 默认超时配置（秒） -DEFAULT_TIMEOUT = 30.0 -LONG_TIMEOUT = 300.0 - - -class APIClient: - """通用 API 客户端""" - - def __init__( - self, - base_url: Optional[str] = None, - api_key: Optional[str] = None, - default_timeout: float = DEFAULT_TIMEOUT - ): - """ - 初始化 API 客户端 - - Args: - base_url: API 基础 URL（默认从环境变量读取） - api_key: API 密钥（默认从环境变量读取） - default_timeout: 默认超时时间（秒） - """ - self.base_url = (base_url or get_base_url()).rstrip('/') - self.api_key = api_key or get_api_key() - self.default_timeout = default_timeout - self._client: Optional[httpx.Client] = None - - logger.info(f"[客户端初始化] base_url={self.base_url}") - - @property - def client(self) -> httpx.Client: - """懒加载 HTTP 客户端""" - if self._client is None: - self._client = httpx.Client(timeout=self.default_timeout) - return self._client - - def _get_headers(self) -> Dict[str, str]: - """获取请求头""" - headers = { - 'Content-Type': 'application/json', - 'Accept': 'application/json' - } - if self.api_key: - headers['X-API-Key'] = self.api_key - return headers - - def request( - self, - endpoint: str, - method: str = "GET", - data: Optional[Dict[str, Any]] = None, - params: Optional[Dict[str, Any]] = None, - timeout: Optional[float] = None - ) -> Dict[str, Any]: - """ - 发送 HTTP 请求 - - Args: - endpoint: API 端点路径 - method: HTTP 方法 - data: 请求体数据 - params: URL 查询参数 - timeout: 超时时间 - - Returns: - API 响应数据 - """ - url = f"{self.base_url}{endpoint}" - timeout = timeout or self.default_timeout - - try: - logger.info(f"[API请求] {method} {url}") - - if method.upper() == "GET": - response = self.client.get( - url, - headers=self._get_headers(), - params=params, - timeout=timeout - ) - elif method.upper() == "POST": - response = self.client.post( - url, - headers=self._get_headers(), - json=data, - params=params, - timeout=timeout - ) - elif method.upper() == "PUT": - response = self.client.put( - url, - headers=self._get_headers(), - json=data, - params=params, - timeout=timeout - ) - elif method.upper() == "DELETE": - response = self.client.delete( - url, - headers=self._get_headers(), - params=params, - timeout=timeout - ) - else: - raise ValueError(f"不支持的 HTTP 方法: {method}") - - logger.info(f"[API响应] HTTP {response.status_code}") - response.raise_for_status() - - return response.json() - - except httpx.TimeoutException: - error_msg = f"API 请求超时: {url}" - logger.error(f"[API错误] {error_msg}") - raise Exception(error_msg) - - except httpx.HTTPStatusError as e: - error_msg = f"API 请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(f"[API错误] {error_msg}") - raise Exception(error_msg) - - except Exception as e: - error_msg = f"API 请求异常: {str(e)}" - logger.error(f"[API错误] {error_msg}", exc_info=True) - raise Exception(error_msg) - - def close(self): - """关闭 HTTP 客户端""" - if self._client is not None: - self._client.close() - self._client = None - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - self.close() - return False - - -# 懒加载的默认客户端 -_default_client: Optional[APIClient] = None - - -def get_default_client() -> APIClient: - """获取默认客户端（懒加载）""" - global _default_client - if _default_client is None: - _default_client = APIClient() - return _default_client - - -def call_api( - endpoint: str, - method: str = "GET", - data: Optional[Dict[str, Any]] = None, - params: Optional[Dict[str, Any]] = None, - timeout: Optional[float] = None -) -> Dict[str, Any]: - """ - 便捷函数：调用 API - - Args: - endpoint: API 端点路径 - method: HTTP 方法 - data: 请求体数据 - params: URL 查询参数 - timeout: 超时时间 - - Returns: - API 响应数据 - """ - return get_default_client().request( - endpoint=endpoint, - method=method, - data=data, - params=params, - timeout=timeout - ) diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/env_config.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/env_config.py deleted file mode 100644 index fba59a9..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/env_config.py +++ /dev/null @@ -1,60 +0,0 @@ -"""环境变量配置模块""" - -import os -from typing import Optional - - -def get_api_key(default: str = "") -> str: - """ - 获取 API 密钥环境变量 - - Args: - default: 默认值 - - Returns: - str: API 密钥 - - Environment Variables: - API_KEY: API 密钥 - """ - return os.environ.get("API_KEY", default) - - -def get_base_url(default: str = "http://localhost:8080") -> str: - """ - 获取后端 API 基础 URL 环境变量 - - Args: - default: 默认值 - - Returns: - str: 后端 API 基础 URL - - Environment Variables: - BASE_URL: 后端 API 基础 URL - """ - return os.environ.get("BASE_URL", default) - - -def get_env_config() -> dict: - """ - 获取所有环境配置 - - Returns: - dict: 包含所有配置的字典 - """ - return { - "api_key": get_api_key(), - "base_url": get_base_url() - } - - -def set_env_variable(key: str, value: str) -> None: - """ - 设置环境变量（仅在当前进程中有效） - - Args: - key: 环境变量名 - value: 环境变量值 - """ - os.environ[key] = value diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/json_helper.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/json_helper.py deleted file mode 100644 index 00ac6cc..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/json_helper.py +++ /dev/null @@ -1,41 +0,0 @@ -"""JSON 文件读取工具""" - -import json -from pathlib import Path -from typing import Any, Union - - -def load_json(json_path: Union[str, Path]) -> Any: - """ - 读取 JSON 文件并返回其内容 - - Args: - json_path: JSON 文件的路径 - - Returns: - JSON 文件中解析后的数据 - - Raises: - FileNotFoundError: 当文件不存在时 - json.JSONDecodeError: 当 JSON 格式无效时 - """ - try: - path = Path(json_path) - - if not path.exists(): - raise FileNotFoundError(f"JSON 文件不存在: {json_path}") - - if not path.is_file(): - raise ValueError(f"路径不是一个文件: {json_path}") - - with open(path, 'r', encoding='utf-8') as f: - data = json.load(f) - - return data - - except json.JSONDecodeError as e: - raise json.JSONDecodeError(f"JSON 格式错误: {e.msg}", e.doc, e.pos) - except FileNotFoundError: - raise - except Exception as e: - raise Exception(f"读取 JSON 文件时发生错误: {str(e)}") diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/logger_config.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/logger_config.py deleted file mode 100644 index b423c64..0000000 --- a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/logger_config.py +++ /dev/null @@ -1,130 +0,0 @@ -# -*- coding: utf-8 -*- -""" -统一日志配置模块 -提供系统级别的日志配置和管理 -""" - -import os -import sys -import logging -from logging.handlers import RotatingFileHandler, TimedRotatingFileHandler -from pathlib import Path - - -class LoggerConfig: - """日志配置管理类""" - - def __init__(self, logs_dir: str = None): - """初始化日志配置""" - if logs_dir: - self.logs_dir = Path(logs_dir) - else: - project_root = Path(__file__).parent.parent - self.logs_dir = project_root / "logs" - - self.logs_dir.mkdir(exist_ok=True) - - self.log_format = '%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s' - self.date_format = '%Y-%m-%d %H:%M:%S' - self.log_level = self._get_log_level_from_env() - self._initialized = False - - def _get_log_level_from_env(self) -> int: - log_level_str = os.getenv('LOG_LEVEL', 'INFO').upper() - level_mapping = { - 'DEBUG': logging.DEBUG, - 'INFO': logging.INFO, - 'WARNING': logging.WARNING, - 'WARN': logging.WARNING, - 'ERROR': logging.ERROR, - 'CRITICAL': logging.CRITICAL, - 'FATAL': logging.CRITICAL - } - return level_mapping.get(log_level_str, logging.INFO) - - def setup_logging(self, - app_name: str = "lzwcai_mcpskills_template", - log_level: int = logging.INFO, - max_file_size: int = 10 * 1024 * 1024, - backup_count: int = 5, - console_output: bool = True) -> logging.Logger: - if self._initialized: - return logging.getLogger() - - root_logger = logging.getLogger() - root_logger.setLevel(log_level) - - for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) - - formatter = logging.Formatter(self.log_format, self.date_format) - - # 1. 主日志文件 - 按大小滚动 - main_log_file = self.logs_dir / f"{app_name}.log" - file_handler = RotatingFileHandler( - main_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - file_handler.setLevel(log_level) - file_handler.setFormatter(formatter) - root_logger.addHandler(file_handler) - - # 2. 错误日志文件 - error_log_file = self.logs_dir / f"{app_name}_error.log" - error_handler = RotatingFileHandler( - error_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - error_handler.setLevel(logging.ERROR) - error_handler.setFormatter(formatter) - root_logger.addHandler(error_handler) - - # 3. 按日期滚动的日志文件 - daily_log_file = self.logs_dir / f"{app_name}_daily.log" - daily_handler = TimedRotatingFileHandler( - daily_log_file, - when='midnight', - interval=1, - backupCount=30, - encoding='utf-8' - ) - daily_handler.setLevel(log_level) - daily_handler.setFormatter(formatter) - daily_handler.suffix = "%Y-%m-%d" - root_logger.addHandler(daily_handler) - - # 4. 控制台输出 (MCP协议使用stdio时，必须将日志输出到stderr) - if console_output: - console_handler = logging.StreamHandler(sys.stderr) - console_handler.setLevel(log_level) - console_formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s', - self.date_format - ) - console_handler.setFormatter(console_formatter) - root_logger.addHandler(console_handler) - - self._initialized = True - root_logger.info(f"日志系统初始化完成 - 日志目录: {self.logs_dir}") - - return root_logger - - def get_module_logger(self, module_name: str) -> logging.Logger: - return logging.getLogger(module_name) - - -# 全局日志配置实例 -logger_config = LoggerConfig() - - -def setup_system_logging(app_name: str = "lzwcai_mcpskills_template", - log_level: int = logging.INFO) -> logging.Logger: - return logger_config.setup_logging(app_name, log_level) - - -def get_logger(name: str) -> logging.Logger: - return logger_config.get_module_logger(name) diff --git a/lzwcai_mcpskills_template/main.py b/lzwcai_mcpskills_template/main.py deleted file mode 100644 index c79f492..0000000 --- a/lzwcai_mcpskills_template/main.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -Entry point for lzwcai-mcpskills-template -MCP Server 模板项目入口 -""" - -import os - -if __name__ == "__main__": - # 设置环境变量（根据实际需求修改） - os.environ["API_KEY"] = "your-api-key" - os.environ["BASE_URL"] = "http://localhost:8080" - - # Import and run the actual MCP server - from lzwcai_mcpskills_template.main import main - main() diff --git a/lzwcai_mcpskills_template/pyproject.toml b/lzwcai_mcpskills_template/pyproject.toml deleted file mode 100644 index 8aeb48f..0000000 --- a/lzwcai_mcpskills_template/pyproject.toml +++ /dev/null @@ -1,31 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai_mcpskills_template" -version = "0.1.3" -description = "MCP Server 模板项目 - 用于快速创建新的 MCP 服务" -readme = "README.md" -requires-python = ">=3.10" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "template", "server"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", -] - -[project.scripts] -lzwcai_mcpskills_template = "lzwcai_mcpskills_template.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcpskills_template"] diff --git a/lzwcai_mcpskills_visual2url/README.md b/lzwcai_mcpskills_visual2url/README.md deleted file mode 100644 index d491df8..0000000 --- a/lzwcai_mcpskills_visual2url/README.md +++ /dev/null @@ -1,37 +0,0 @@ -# lzwcai_mcpskills_visual2url - -基于 FastMCP 的 MCP Server 项目。主要提供 HTML 转换为浏览器可访问 URL 的工具。 - -## 功能 - -提供以下两个工具（Tools）： -- `html_file_to_url`: 传入本地 HTML 文件路径，转换成 `file://` 协议的 URL，方便浏览器直接打开预览。 -- `html_code_to_url`: 传入 HTML 代码片段，将其保存为临时文件并返回可访问的 `file://` URL。 - -## 安装 - -```bash -pip install lzwcai_mcpskills_visual2url -``` - -## 使用 - -```bash -# 直接运行服务端 -lzwcai_mcpskills_visual2url -``` - -## 项目结构 - -``` -lzwcai_mcpskills_visual2url/ -├── main.py # 入口文件 -├── pyproject.toml # 项目配置 -├── README.md # 说明文档 -└── lzwcai_mcpskills_visual2url/ # 核心代码 - └── main.py # FastMCP Server 主逻辑 -``` - -## License - -MIT diff --git a/lzwcai_mcpskills_visual2url/lzwcai_mcpskills_visual2url/.python-version b/lzwcai_mcpskills_visual2url/lzwcai_mcpskills_visual2url/.python-version deleted file mode 100644 index 24ee5b1..0000000 --- a/lzwcai_mcpskills_visual2url/lzwcai_mcpskills_visual2url/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.13 diff --git a/lzwcai_mcpskills_visual2url/lzwcai_mcpskills_visual2url/__pycache__/main.cpython-312.pyc 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zJ!93$;`(UojD7vMCux5YZUWqml2n;1RaTXA0+9$-aa1}@OpVW z9vW~B=)<{n7%MgRS?tz(*lF*FtesjTQFRjKk*Io59A9`@aa_*I9tp?8FdoW98I)s< z&LO=VMjD+hw{c{5t0XfU5W+m`^$A@ZCuz9=!v@4K4r}Spj;GIGY;?NqlFsX8f?Xij zMwpkhkch*dQanC{{1bRaL+T;L+2PWHI5Pfm48Y%p7!<3AqlMQCpPCg!Ij@z)eiDPQ z`-H!r;14gT3z%?UNO%|VJMap2V)yi$`+`i^&u-zLg(1FI0{%}9t~W*9Q5h)ut^-l# zzoF8M_Inid$XIg*;rIA~i4E82Ys|#f#KyL}WGZu5RY#SN3FG_U9m*i+PB3@zEAu>6 zLTND}yvFz6#ji{QqRk^iQ|b!YuUVJk&*vzY?5pmWv^H(D^^O%S-!jpgT(&zwS4Nvh zYGSLuq75T$aXQvNX str: - """ - 将本地 HTML 文件上传至服务器并转换成可在浏览器中打开的 URL。 - - Args: - file_path: 本地 HTML 文件的绝对路径 - """ - if not os.path.exists(file_path): - return f"Error: 文件不存在: {file_path}" - - headers = { - 'Authorization': AUTHORIZATION, - 'User-Agent': 'Apifox/1.0.0 (https://apifox.com)' - } - - try: - with open(file_path, 'rb') as f: - files = { - 'file': (os.path.basename(file_path), f, 'text/html') - } - response = requests.post(UPLOAD_URL, headers=headers, files=files) - response.raise_for_status() - data = response.json() - if data.get('code') == 200 and 'data' in data and 'fileUrl' in data['data']: - return data['data']['fileUrl'] - else: - return f"上传失败: {data.get('msg', '未知错误')}" - except Exception as e: - return f"请求发生异常: {str(e)}" - -@mcp.tool() -def html_code_to_url(html_code: str) -> str: - """ - 将 HTML 代码片段上传至服务器并转换成可在浏览器中打开的 URL。 - - Args: - html_code: HTML 代码字符串 - """ - headers = { - 'Authorization': AUTHORIZATION, - 'User-Agent': 'Apifox/1.0.0 (https://apifox.com)', - 'Content-Type': 'text/html' - } - - try: - response = requests.post(UPLOAD_URL, headers=headers, data=html_code.encode('utf-8')) - response.raise_for_status() - data = response.json() - if data.get('code') == 200 and 'data' in data and 'fileUrl' in data['data']: - return data['data']['fileUrl'] - else: - return f"上传失败: {data.get('msg', '未知错误')}" - except Exception as e: - return f"请求发生异常: {str(e)}" - -def main(): - # 运行 MCP Server - mcp.run() - -if __name__ == "__main__": - main() diff --git a/lzwcai_mcpskills_visual2url/main.py b/lzwcai_mcpskills_visual2url/main.py deleted file mode 100644 index 23982d7..0000000 --- a/lzwcai_mcpskills_visual2url/main.py +++ /dev/null @@ -1,9 +0,0 @@ -""" -Entry point for lzwcai_mcpskills_visual2url -MCP Server 项目入口 -""" - -if __name__ == "__main__": - # Import and run the actual MCP server - from lzwcai_mcpskills_visual2url.main import main - main() diff --git a/lzwcai_mcpskills_visual2url/pyproject.toml b/lzwcai_mcpskills_visual2url/pyproject.toml deleted file mode 100644 index 2902e11..0000000 --- a/lzwcai_mcpskills_visual2url/pyproject.toml +++ /dev/null @@ -1,31 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai_mcpskills_visual2url" -version = "0.1.1" -description = "MCP Server 模板项目 - 用于快速创建新的 MCP 服务" -readme = "README.md" -requires-python = ">=3.10" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "template", "server"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", -] -dependencies = [ - "mcp>=1.0.0", - "requests>=2.31.0", -] - -[project.scripts] -lzwcai_mcpskills_visual2url = "lzwcai_mcpskills_visual2url.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcpskills_visual2url"] diff --git a/lzwcai_workflow_to_mcp/README.md b/lzwcai_workflow_to_mcp/README.md deleted file mode 100644 index 4259a9f..0000000 --- a/lzwcai_workflow_to_mcp/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# lzwcai-workflow-to-mcp - -MCP Server for executing workflows with dynamic tool generation. - -## 功能 - -- 从 API 动态加载工作流配置 -- 自动生成 MCP 工具 -- 支持工作流执行 - -## 安装 - -```bash -pip install lzwcai-workflow-to-mcp -``` - -## 使用 - -```bash -# 设置环境变量 -export workflowId="your-workflow-id" -export workflowExecuteKey="your-api-key" -export backendBaseUrl="http://your-api-server" - -# 运行 -lzwcai-workflow-to-mcp -``` - -## 环境变量 - -| 变量名 | 说明 | 默认值 | -|--------|------|--------| -| workflowId | 工作流ID | - | -| workflowExecuteKey | API密钥 | - | -| backendBaseUrl | 后端API地址 | http://lzwcai-demp-corp-manager:8086 | - -## License - -MIT diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/.python-version b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/.python-version deleted file mode 100644 index 24ee5b1..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.13 diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/README.md b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/README.md deleted file mode 100644 index e69de29..0000000 diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/__pycache__/main.cpython-312.pyc b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/__pycache__/main.cpython-312.pyc deleted file mode 100644 index 2aad1c179ab0cea9701006721cbd69c1409c6898..0000000000000000000000000000000000000000 GIT binary patch literal 0 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unnest(string_to_array(nullif((SELECT months_param FROM query_params LIMIT 1), ''), ',')) AS month\n ) t\n WHERE month IS NOT NULL AND TRIM(month) <> ''\n)\n\nSELECT \n c.company_name AS 公司,\n v.dbill_date AS 日期,\n v.csign || LPAD(v.ino_id::text, 4, '0') AS 凭证号,\n v.cdigest AS 摘要,\n a.ccode AS 科目编码,\n a.ccode_name AS 科目名称,\n v.md AS 借方,\n v.mc AS 贷方,\n v.cbill AS 制单人\nFROM uf_voucher v\nJOIN uf_company c ON v.company_id = c.id\nJOIN uf_account_code a ON v.account_code_id = a.id\nCROSS JOIN query_params qp\nWHERE v.bdelete = false\n AND v.iyear = qp.target_year\n AND v.cdigest LIKE '%' || qp.digest_keyword || '%'\n AND (\n NOT EXISTS (SELECT 1 FROM company_filters)\n OR EXISTS (\n SELECT 1 FROM company_filters cf \n WHERE c.company_name LIKE '%' || cf.company_keyword || '%'\n )\n )\n AND (\n NOT EXISTS (SELECT 1 FROM month_filters)\n OR v.iperiod IN (SELECT target_month FROM month_filters)\n )\nORDER BY c.company_name, v.dbill_date DESC, v.ino_id;", - "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"digest\",\"displayName\":\"摘要关键字\",\"maxLength\":500,\"defaultValue\":\"\",\"required\":true},{\"type\":\"string\",\"name\":\"company_names\",\"displayName\":\"公司名称\",\"maxLength\":200,\"defaultValue\":\"\",\"required\":false},{\"type\":\"number\",\"name\":\"year\",\"displayName\":\"会计年度\",\"maxLength\":4,\"defaultValue\":\"2025\",\"required\":true},{\"type\":\"string\",\"name\":\"months\",\"displayName\":\"月份\",\"maxLength\":50,\"defaultValue\":\"\",\"required\":false}]", - "resultType": "list", - "sourceType": "ai", - "trainingTaskId": null, - "tableMetadataIds": "", - "executionCount": 0, - "visualizationConfigs": [], - "inputJsonSchema": "{}", - "outputJsonSchema": "{\"type\":\"object\",\"properties\":{\"data\":{\"type\":\"array\"}}}", - "lastExecutionTime": null - }, - { - "id": "2000852304343240706", - "createBy": "bjt", - "createTime": "2025-12-16 16:55:38", - "updateBy": null, - "updateTime": null, - "serviceId": "2000821852159709186", - "uniqueName": "按科目查凭证", - "name": "ankemuchapingzheng_6a268ea1", - "description": "按照科目名称查询凭证信息", - "visualizable": 1, - "toolPrompt": "按照科目名称查询凭证信息", - "toolType": "sql", - "datasourceId": "158", - "sqlTemplate": "WITH query_params AS (\n SELECT \n {year}::INT AS target_year,\n '{company_names}' AS company_names_param,\n '{subject_name}' AS subject_keyword,\n '{months}' AS months_param\n),\ncompany_filters AS (\n SELECT \n TRIM(keyword) AS company_keyword\n FROM (\n SELECT unnest(string_to_array(nullif((SELECT company_names_param FROM query_params LIMIT 1), ''), ',')) AS keyword\n ) t\n WHERE keyword IS NOT NULL AND TRIM(keyword) <> ''\n),\nmonth_filters AS (\n SELECT \n month::INT AS target_month\n FROM (\n SELECT unnest(string_to_array(nullif((SELECT months_param FROM query_params LIMIT 1), ''), ',')) AS month\n ) t\n WHERE month IS NOT NULL AND TRIM(month) <> ''\n),\n目标科目 AS (\n SELECT DISTINCT a.id, a.ccode\n FROM uf_account_code a\n JOIN uf_company c ON a.company_id = c.id\n CROSS JOIN query_params qp\n WHERE a.ccode_name LIKE '%' || qp.subject_keyword || '%'\n AND a.iyear = qp.target_year\n AND (\n NOT EXISTS (SELECT 1 FROM company_filters)\n OR EXISTS (\n SELECT 1 FROM company_filters cf \n WHERE c.company_name LIKE '%' || cf.company_keyword || '%'\n )\n )\n),\n相关科目 AS (\n SELECT a.id\n FROM uf_account_code a\n JOIN uf_company c ON a.company_id = c.id\n CROSS JOIN query_params qp\n WHERE a.iyear = qp.target_year\n AND EXISTS (\n SELECT 1 FROM 目标科目 t \n WHERE a.ccode LIKE t.ccode || '%'\n )\n AND (\n NOT EXISTS (SELECT 1 FROM company_filters)\n OR EXISTS (\n SELECT 1 FROM company_filters cf \n WHERE c.company_name LIKE '%' || cf.company_keyword || '%'\n )\n )\n)\nSELECT \n c.company_name AS 公司,\n v.dbill_date AS 日期,\n v.csign || LPAD(v.ino_id::text, 4, '0') AS 凭证号,\n v.cdigest AS 摘要,\n a.ccode AS 科目编码,\n a.ccode_name AS 科目名称,\n a.igrade AS 级次,\n v.md AS 借方,\n v.mc AS 贷方\nFROM uf_voucher v\nJOIN uf_company c ON v.company_id = c.id\nJOIN uf_account_code a ON v.account_code_id = a.id\nCROSS JOIN query_params qp\nWHERE v.bdelete = false\n AND v.account_code_id IN (SELECT id FROM 相关科目)\n AND v.iyear = qp.target_year\n AND (\n NOT EXISTS (SELECT 1 FROM company_filters)\n OR EXISTS (\n SELECT 1 FROM company_filters cf \n WHERE c.company_name LIKE '%' || cf.company_keyword || '%'\n )\n )\n AND (\n NOT EXISTS (SELECT 1 FROM month_filters)\n OR v.iperiod IN (SELECT target_month FROM month_filters)\n )\nORDER BY c.company_name, a.ccode, v.dbill_date, v.ino_id;", - "sqlParams": "[{\"type\":\"select\",\"name\":\"subject_name\",\"displayName\":\"科目名称\",\"maxLength\":100,\"defaultValue\":\"\",\"required\":true,\"options\":[\"管理费用\",\"销售费用\",\"财务费用\",\"应收账款\",\"应付账款\"]},{\"type\":\"paragraph\",\"name\":\"company_names\",\"displayName\":\"公司名称列表\",\"maxLength\":300,\"defaultValue\":\"\",\"required\":false},{\"type\":\"number\",\"name\":\"year\",\"displayName\":\"年度\",\"maxLength\":4,\"defaultValue\":\"2025\",\"required\":true},{\"type\":\"select\",\"name\":\"months\",\"displayName\":\"期间月份\",\"maxLength\":30,\"defaultValue\":\"\",\"required\":false,\"options\":[\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\"]}]", - "resultType": "list", - "sourceType": "ai", - "trainingTaskId": null, - "tableMetadataIds": "", - "executionCount": 0, - "visualizationConfigs": [], - "inputJsonSchema": "{}", - "outputJsonSchema": "{\"type\":\"object\",\"properties\":{\"data\":{\"type\":\"array\"}}}", - "lastExecutionTime": null - }, - { - "id": "2000852610904920066", - "createBy": "bjt", - "createTime": "2025-12-16 16:56:51", - "updateBy": null, - "updateTime": null, - "serviceId": "2000821852159709186", - "uniqueName": "按科目查余额", - "name": "ankemuchayue_68b302c2", - "description": "按照科目名称查询余额", - "visualizable": 1, - "toolPrompt": "按照科目名称查询余额", - "toolType": "sql", - "datasourceId": "158", - "sqlTemplate": "WITH query_params AS (\n SELECT \n {year}::INT AS target_year,\n '{company_names}' AS company_names_param,\n '{subject_name}' AS subject_keyword,\n '{months}' AS months_param\n),\ncompany_filters AS (\n SELECT \n TRIM(keyword) AS company_keyword\n FROM (\n SELECT unnest(string_to_array(nullif((SELECT company_names_param FROM query_params LIMIT 1), ''), ',')) AS keyword\n ) t\n WHERE keyword IS NOT NULL AND TRIM(keyword) <> ''\n),\nmonth_filters AS (\n SELECT \n month::INT AS target_month\n FROM (\n SELECT unnest(string_to_array(nullif((SELECT months_param FROM query_params LIMIT 1), ''), ',')) AS month\n ) t\n WHERE month IS NOT NULL AND TRIM(month) <> ''\n),\n目标科目 AS (\n SELECT DISTINCT a.id, a.ccode\n FROM uf_account_code a\n JOIN uf_company c ON a.company_id = c.id\n CROSS JOIN query_params qp\n WHERE a.ccode_name LIKE '%' || qp.subject_keyword || '%'\n AND a.iyear = qp.target_year\n AND (\n NOT EXISTS (SELECT 1 FROM company_filters)\n OR EXISTS (\n SELECT 1 FROM company_filters cf \n WHERE c.company_name LIKE '%' || cf.company_keyword || '%'\n )\n )\n),\n相关科目 AS (\n SELECT a.id\n FROM uf_account_code a\n JOIN uf_company c ON a.company_id = c.id\n CROSS JOIN query_params qp\n WHERE a.iyear = qp.target_year\n AND EXISTS (\n SELECT 1 FROM 目标科目 t \n WHERE a.ccode LIKE t.ccode || '%'\n )\n AND (\n NOT EXISTS (SELECT 1 FROM company_filters)\n OR EXISTS (\n SELECT 1 FROM company_filters cf \n WHERE c.company_name LIKE '%' || cf.company_keyword || '%'\n )\n )\n)\nSELECT \n c.company_name AS 公司,\n a.ccode AS 科目编码,\n a.ccode_name AS 科目名称,\n a.igrade AS 级次,\n a.cclass AS 类别,\n b.iyear AS 年,\n b.iperiod AS 月,\n b.cbegind_c AS 期初方向,\n b.mb AS 期初余额,\n b.md AS 借方发生,\n b.mc AS 贷方发生,\n b.cendd_c AS 期末方向,\n b.me AS 期末余额\nFROM uf_account_balance b\nJOIN uf_company c ON b.company_id = c.id\nJOIN uf_account_code a ON b.account_code_id = a.id\nCROSS JOIN query_params qp\nWHERE b.iyear = qp.target_year\n AND b.account_code_id IN (SELECT id FROM 相关科目)\n AND (\n NOT EXISTS (SELECT 1 FROM company_filters)\n OR EXISTS (\n SELECT 1 FROM company_filters cf \n WHERE c.company_name LIKE '%' || cf.company_keyword || '%'\n )\n )\n AND (\n NOT EXISTS (SELECT 1 FROM month_filters)\n OR b.iperiod IN (SELECT target_month FROM month_filters)\n )\nORDER BY c.company_name, a.ccode, b.iperiod;", - "sqlParams": "[{\"type\":\"string\",\"name\":\"subject_name\",\"displayName\":\"科目名称关键字\",\"maxLength\":100,\"defaultValue\":\"银行存款\",\"required\":true},{\"type\":\"string\",\"name\":\"company_names\",\"displayName\":\"公司筛选\",\"maxLength\":150,\"defaultValue\":\"\",\"required\":false},{\"type\":\"number\",\"name\":\"year\",\"displayName\":\"查询年度\",\"maxLength\":4,\"defaultValue\":\"2025\",\"required\":true},{\"type\":\"number\",\"name\":\"months\",\"displayName\":\"查询月份\",\"maxLength\":2,\"defaultValue\":\"\",\"required\":false}]", - "resultType": "list", - "sourceType": "ai", - "trainingTaskId": null, - "tableMetadataIds": "", - "executionCount": 0, - "visualizationConfigs": [], - "inputJsonSchema": "{}", - "outputJsonSchema": "{\"type\":\"object\",\"properties\":{\"data\":{\"type\":\"array\"}}}", - "lastExecutionTime": null - } -] \ No newline at end of file diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp.log b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp.log deleted file mode 100644 index cb9382c..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp.log +++ /dev/null @@ -1,137 +0,0 @@ -2026-03-28 12:30:33 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs -2026-03-28 12:30:33 - mcp.server.lowlevel.server - DEBUG - [server.py:162] - Initializing server 'workflow_mcp_server' -2026-03-28 12:30:33 - mcp.server.lowlevel.server - DEBUG - [server.py:439] - Registering handler for ListToolsRequest -2026-03-28 12:30:33 - mcp.server.lowlevel.server - DEBUG - [server.py:519] - Registering handler for CallToolRequest -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:334] - ================================================== -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:335] - Workflow MCP Server 启动 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:336] - ================================================== -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:341] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None} -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:344] - 使用模式: api -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2037527155235225601 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2037527155235225601 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:115] - [客户端初始化] base_url=http://192.168.2.236:8088 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:116] - [客户端初始化] token=wf_bd39a583670c42ceab48b3353bf2ba43 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:117] - [客户端初始化] execute_timeout=600.0s -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:148] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2037527155235225601 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:149] - [API请求] Headers: {'X-API-Key': 'wf_bd39a583670c42ceab48b3353bf2ba43'} -2026-03-28 12:30:34 - httpcore.connection - DEBUG - [_trace.py:47] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None -2026-03-28 12:30:34 - httpcore.connection - DEBUG - [_trace.py:47] - connect_tcp.complete return_value= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - send_request_headers.started request= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - send_request_headers.complete -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - send_request_body.started request= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - send_request_body.complete -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - receive_response_headers.started request= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Sat, 28 Mar 2026 04:30:30 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')]) -2026-03-28 12:30:34 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2037527155235225601 "HTTP/1.1 200 " -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - receive_response_body.started request= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - receive_response_body.complete -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - response_closed.started -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - response_closed.complete -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:156] - [API响应] HTTP 200 -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:157] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Sat, 28 Mar 2026 04:30:30 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'} -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:162] - [API响应] 获取工作流配置成功: workflow_id=2037527155235225601 -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:163] - [API响应] Body: { - "msg": "查询成功", - "code": 200, - "data": [ - { - "id": "2037535460665982977", - "createBy": "duchangyuan", - "createTime": "2026-03-27 22:21:24", - "updateBy": "admin", - "updateTime": "2026-03-28 12:21:49", - "serviceId": "2037535460632428546", - "uniqueName": "银行流水状态检测", - "name": "yinxingliushuizhuangtaijiance_7c4d9a63", - "description": "用于检测流水导入状态，检查是否全部导入完成", - "visualizable": 0, - "toolPrompt": null, - "toolType": "api", - "datasourceId": null, - "sqlTemplate": null, - "sqlParams": "[]", - "resultType": null, - "sourceType": null, - "trainingTaskId": null, - "tableMetadataIds": null, - "executionCount": 0, - "visualizationConfigs": null, - "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"workflow_extraContext\":{\"description\":\"工作流额外的上下文参数（如环境变量等），可以是任何类型，非必填\"}},\"required\":[]}", - "outputJsonSchema": "{\"type\":\"object\",\"properties\":{\"text\":{\"type\":\"string\"}},\"additionalProperties\":false}", - "lastExecutionTime": null - } - ] -} -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:116] - API 响应原始数据: { - "msg": "查询成功", - "code": 200, - "data": [ - { - "id": "2037535460665982977", - "createBy": "duchangyuan", - "createTime": "2026-03-27 22:21:24", - "updateBy": "admin", - "updateTime": "2026-03-28 12:21:49", - "serviceId": "2037535460632428546", - "uniqueName": "银行流水状态检测", - "name": "yinxingliushuizhuangtaijiance_7c4d9a63", - "description": "用于检测流水导入状态，检查是否全部导入完成", - "visualizable": 0, - "toolPrompt": null, - "toolType": "api", - "datasourceId": null, - "sqlTemplate": null, - "sqlParams": "[]", - "resultType": null, - "sourceType": null, - "trainingTaskId": null, - "tableMetadataIds": null, - "executionCount": 0, - "visualizationConfigs": null, - "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"workflow_extraContext\":{\"description\":\"工作流额外的上下文参数（如环境变量等），可以是任何类型，非必填\"}},\"required\":[]}", - "outputJsonSchema": "{\"type\":\"object\",\"properties\":{\"text\":{\"type\":\"string\"}},\"additionalProperties\":false}", - "lastExecutionTime": null - } - ] -} -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [ - { - "id": "2037535460665982977", - "createBy": "duchangyuan", - "createTime": "2026-03-27 22:21:24", - "updateBy": "admin", - "updateTime": "2026-03-28 12:21:49", - "serviceId": "2037535460632428546", - "uniqueName": "银行流水状态检测", - "name": "yinxingliushuizhuangtaijiance_7c4d9a63", - "description": "用于检测流水导入状态，检查是否全部导入完成", - "visualizable": 0, - "toolPrompt": null, - "toolType": "api", - "datasourceId": null, - "sqlTemplate": null, - "sqlParams": "[]", - "resultType": null, - "sourceType": null, - "trainingTaskId": null, - "tableMetadataIds": null, - "executionCount": 0, - "visualizationConfigs": null, - "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"workflow_extraContext\":{\"description\":\"工作流额外的上下文参数（如环境变量等），可以是任何类型，非必填\"}},\"required\":[]}", - "outputJsonSchema": "{\"type\":\"object\",\"properties\":{\"text\":{\"type\":\"string\"}},\"additionalProperties\":false}", - "lastExecutionTime": null - } -] -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2037527155235225601, 配置数量: 1 -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置 -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - INFO - [main.py:352] - 开始运行 MCP Server (stdio 模式) -2026-03-28 12:30:34 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor -2026-03-28 12:30:34 - mcp.server.lowlevel.server - DEBUG - [server.py:675] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0') -2026-03-28 12:30:40 - mcp.server.lowlevel.server - DEBUG - [server.py:675] - Received message: -2026-03-28 12:30:40 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-03-28 12:30:40 - mcp.server.lowlevel.server - DEBUG - [server.py:723] - Dispatching request of type ListToolsRequest -2026-03-28 12:30:40 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1 -2026-03-28 12:30:40 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:178] - 处理工具配置: name=yinxingliushuizhuangtaijiance_7c4d9a63, description=用于检测流水导入状态，检查是否全部导入完成... -2026-03-28 12:30:40 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:185] - 从 sqlParams 转换的 inputSchema: {"type": "object", "properties": {}, "required": []} -2026-03-28 12:30:40 - lzwcai_workflow_to_mcp.main - INFO - [main.py:237] - ListTools 响应: 返回 1 个工具 -2026-03-28 12:30:40 - mcp.server.lowlevel.server - DEBUG - [server.py:790] - Response sent diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_daily.log b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_daily.log deleted file mode 100644 index cb9382c..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_daily.log +++ /dev/null @@ -1,137 +0,0 @@ -2026-03-28 12:30:33 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs -2026-03-28 12:30:33 - mcp.server.lowlevel.server - DEBUG - [server.py:162] - Initializing server 'workflow_mcp_server' -2026-03-28 12:30:33 - mcp.server.lowlevel.server - DEBUG - [server.py:439] - Registering handler for ListToolsRequest -2026-03-28 12:30:33 - mcp.server.lowlevel.server - DEBUG - [server.py:519] - Registering handler for CallToolRequest -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:334] - ================================================== -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:335] - Workflow MCP Server 启动 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:336] - ================================================== -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:341] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None} -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:344] - 使用模式: api -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2037527155235225601 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2037527155235225601 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:115] - [客户端初始化] base_url=http://192.168.2.236:8088 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:116] - [客户端初始化] token=wf_bd39a583670c42ceab48b3353bf2ba43 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:117] - [客户端初始化] execute_timeout=600.0s -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:148] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2037527155235225601 -2026-03-28 12:30:33 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:149] - [API请求] Headers: {'X-API-Key': 'wf_bd39a583670c42ceab48b3353bf2ba43'} -2026-03-28 12:30:34 - httpcore.connection - DEBUG - [_trace.py:47] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None -2026-03-28 12:30:34 - httpcore.connection - DEBUG - [_trace.py:47] - connect_tcp.complete return_value= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - send_request_headers.started request= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - send_request_headers.complete -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - send_request_body.started request= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - send_request_body.complete -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - receive_response_headers.started request= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Sat, 28 Mar 2026 04:30:30 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')]) -2026-03-28 12:30:34 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2037527155235225601 "HTTP/1.1 200 " -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - receive_response_body.started request= -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - receive_response_body.complete -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - response_closed.started -2026-03-28 12:30:34 - httpcore.http11 - DEBUG - [_trace.py:47] - response_closed.complete -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:156] - [API响应] HTTP 200 -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:157] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Sat, 28 Mar 2026 04:30:30 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'} -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:162] - [API响应] 获取工作流配置成功: workflow_id=2037527155235225601 -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:163] - [API响应] Body: { - "msg": "查询成功", - "code": 200, - "data": [ - { - "id": "2037535460665982977", - "createBy": "duchangyuan", - "createTime": "2026-03-27 22:21:24", - "updateBy": "admin", - "updateTime": "2026-03-28 12:21:49", - "serviceId": "2037535460632428546", - "uniqueName": "银行流水状态检测", - "name": "yinxingliushuizhuangtaijiance_7c4d9a63", - "description": "用于检测流水导入状态，检查是否全部导入完成", - "visualizable": 0, - "toolPrompt": null, - "toolType": "api", - "datasourceId": null, - "sqlTemplate": null, - "sqlParams": "[]", - "resultType": null, - "sourceType": null, - "trainingTaskId": null, - "tableMetadataIds": null, - "executionCount": 0, - "visualizationConfigs": null, - "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"workflow_extraContext\":{\"description\":\"工作流额外的上下文参数（如环境变量等），可以是任何类型，非必填\"}},\"required\":[]}", - "outputJsonSchema": "{\"type\":\"object\",\"properties\":{\"text\":{\"type\":\"string\"}},\"additionalProperties\":false}", - "lastExecutionTime": null - } - ] -} -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:116] - API 响应原始数据: { - "msg": "查询成功", - "code": 200, - "data": [ - { - "id": "2037535460665982977", - "createBy": "duchangyuan", - "createTime": "2026-03-27 22:21:24", - "updateBy": "admin", - "updateTime": "2026-03-28 12:21:49", - "serviceId": "2037535460632428546", - "uniqueName": "银行流水状态检测", - "name": "yinxingliushuizhuangtaijiance_7c4d9a63", - "description": "用于检测流水导入状态，检查是否全部导入完成", - "visualizable": 0, - "toolPrompt": null, - "toolType": "api", - "datasourceId": null, - "sqlTemplate": null, - "sqlParams": "[]", - "resultType": null, - "sourceType": null, - "trainingTaskId": null, - "tableMetadataIds": null, - "executionCount": 0, - "visualizationConfigs": null, - "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"workflow_extraContext\":{\"description\":\"工作流额外的上下文参数（如环境变量等），可以是任何类型，非必填\"}},\"required\":[]}", - "outputJsonSchema": "{\"type\":\"object\",\"properties\":{\"text\":{\"type\":\"string\"}},\"additionalProperties\":false}", - "lastExecutionTime": null - } - ] -} -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [ - { - "id": "2037535460665982977", - "createBy": "duchangyuan", - "createTime": "2026-03-27 22:21:24", - "updateBy": "admin", - "updateTime": "2026-03-28 12:21:49", - "serviceId": "2037535460632428546", - "uniqueName": "银行流水状态检测", - "name": "yinxingliushuizhuangtaijiance_7c4d9a63", - "description": "用于检测流水导入状态，检查是否全部导入完成", - "visualizable": 0, - "toolPrompt": null, - "toolType": "api", - "datasourceId": null, - "sqlTemplate": null, - "sqlParams": "[]", - "resultType": null, - "sourceType": null, - "trainingTaskId": null, - "tableMetadataIds": null, - "executionCount": 0, - "visualizationConfigs": null, - "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"workflow_extraContext\":{\"description\":\"工作流额外的上下文参数（如环境变量等），可以是任何类型，非必填\"}},\"required\":[]}", - "outputJsonSchema": "{\"type\":\"object\",\"properties\":{\"text\":{\"type\":\"string\"}},\"additionalProperties\":false}", - "lastExecutionTime": null - } -] -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2037527155235225601, 配置数量: 1 -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置 -2026-03-28 12:30:34 - lzwcai_workflow_to_mcp.main - INFO - [main.py:352] - 开始运行 MCP Server (stdio 模式) -2026-03-28 12:30:34 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor -2026-03-28 12:30:34 - mcp.server.lowlevel.server - DEBUG - [server.py:675] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0') -2026-03-28 12:30:40 - mcp.server.lowlevel.server - DEBUG - [server.py:675] - Received message: -2026-03-28 12:30:40 - mcp.server.lowlevel.server - INFO - [server.py:720] - Processing request of type ListToolsRequest -2026-03-28 12:30:40 - mcp.server.lowlevel.server - DEBUG - [server.py:723] - Dispatching request of type ListToolsRequest -2026-03-28 12:30:40 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1 -2026-03-28 12:30:40 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:178] - 处理工具配置: name=yinxingliushuizhuangtaijiance_7c4d9a63, description=用于检测流水导入状态，检查是否全部导入完成... -2026-03-28 12:30:40 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:185] - 从 sqlParams 转换的 inputSchema: {"type": "object", "properties": {}, "required": []} -2026-03-28 12:30:40 - lzwcai_workflow_to_mcp.main - INFO - [main.py:237] - ListTools 响应: 返回 1 个工具 -2026-03-28 12:30:40 - mcp.server.lowlevel.server - DEBUG - [server.py:790] - Response sent diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_error.log b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_error.log deleted file mode 100644 index e69de29..0000000 diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/main.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/main.py deleted file mode 100644 index 9f6a2e9..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/main.py +++ /dev/null @@ -1,358 +0,0 @@ -#!/usr/bin/env python3 -""" -Workflow to MCP Server -从 businessQueries.json 或 API 动态生成 MCP 工具 - -支持两种模式: -- local: 从本地 JSON 文件加载 -- api: 从远程 API 加载 -""" -import json -import os -import logging -import argparse -import anyio - -import mcp.types as types -from mcp.server import NotificationOptions, Server -from mcp.server.models import InitializationOptions -from mcp.server.stdio import stdio_server - -try: - from .schema_converter import convert_sql_params_to_input_schema, sanitize_json_schema - from .utils.api_client import execute_workflow, get_workflow_by_id, extract_final_output - from .utils.env_config import get_workflow_id - from .utils.logger_config import setup_system_logging, get_logger -except ImportError: - from schema_converter import convert_sql_params_to_input_schema, sanitize_json_schema - from utils.api_client import execute_workflow, get_workflow_by_id, extract_final_output - from utils.env_config import get_workflow_id - from utils.logger_config import setup_system_logging, get_logger - -# 初始化日志系统 -setup_system_logging(app_name="lzwcai_workflow_to_mcp", log_level=logging.DEBUG) -logger = get_logger(__name__) - -# 初始化 MCP Server -server = Server("workflow_mcp_server") - -# 全局配置 -_tools_config: list[dict] = [] -_config: dict = {} - - -def parse_arguments(): - """解析命令行参数""" - parser = argparse.ArgumentParser(description="Workflow MCP Server") - parser.add_argument( - "--mode", - type=str, - choices=["local", "api"], - default="api", - help="数据加载模式: local(本地JSON) 或 api(远程API，默认)" - ) - parser.add_argument( - "--json-path", - type=str, - default=None, - help="本地 JSON 文件路径 (local 模式)" - ) - parser.add_argument( - "--workflow-id", - type=str, - default=None, - help="工作流ID (api 模式，可选，默认从环境变量 workflowId 获取)" - ) - return parser.parse_args() - - -class DataLoader: - """数据加载器基类""" - - def load(self) -> list[dict]: - raise NotImplementedError - - -class LocalLoader(DataLoader): - """本地 JSON 文件加载器""" - - def __init__(self, json_path: str = None): - if json_path is None: - json_path = os.path.join(os.path.dirname(__file__), "businessQueries.json") - self.json_path = json_path - - def load(self) -> list[dict]: - try: - with open(self.json_path, "r", encoding="utf-8") as f: - data = json.load(f) - logger.info(f"从本地加载 {len(data)} 条配置: {self.json_path}") - return data - except FileNotFoundError: - logger.error(f"配置文件不存在: {self.json_path}") - return [] - except json.JSONDecodeError as e: - logger.error(f"JSON 解析错误: {e}") - return [] - - -class ApiLoader(DataLoader): - """API 远程加载器 - 使用 get_workflow_by_id 获取工作流配置""" - - def __init__(self, workflow_id: str = None): - self.workflow_id = workflow_id or get_workflow_id() - logger.debug(f"ApiLoader 初始化，工作流ID: {self.workflow_id}") - - def load(self) -> list[dict]: - try: - if not self.workflow_id: - logger.error("未提供工作流ID，请设置环境变量 workflowId") - return [] - - logger.info(f"开始从 API 加载工作流配置，工作流ID: {self.workflow_id}") - - # 调用 get_workflow_by_id 获取工作流配置 - response = get_workflow_by_id(self.workflow_id) - - logger.debug(f"API 响应原始数据: {json.dumps(response, ensure_ascii=False, indent=2)}") - - # 返回格式: {code, data, msg} - if response.get("code") != 200: - logger.error(f"获取工作流配置失败: code={response.get('code')}, msg={response.get('msg')}") - return [] - - data = response.get("data") - logger.debug(f"API 响应 data 字段: {json.dumps(data, ensure_ascii=False, indent=2) if data else 'None'}") - - # data 可能是单个对象或列表 - if isinstance(data, dict): - result = [data] - elif isinstance(data, list): - result = data - else: - logger.warning(f"API 响应 data 字段类型异常: {type(data)}") - result = [] - - logger.info(f"从 API 加载工作流配置成功，工作流ID: {self.workflow_id}, 配置数量: {len(result)}") - return result - - except Exception as e: - logger.error(f"API 请求失败: {e}", exc_info=True) - return [] - - -def create_loader(mode: str, **kwargs) -> DataLoader: - """ - 创建数据加载器 - - Args: - mode: "local" 或 "api" - kwargs: - - json_path: 本地 JSON 路径 (local 模式) - - workflow_id: 工作流ID (api 模式，可选，默认从环境变量获取) - """ - if mode == "local": - return LocalLoader(json_path=kwargs.get("json_path")) - elif mode == "api": - return ApiLoader(workflow_id=kwargs.get("workflow_id")) - else: - raise ValueError(f"不支持的模式: {mode}") - - -def init_tools(loader: DataLoader): - """初始化工具配置""" - global _tools_config - _tools_config = loader.load() - logger.info(f"已加载 {len(_tools_config)} 个工具配置") - - -@server.list_tools() -async def handle_list_tools() -> list[types.Tool]: - """列出所有可用工具""" - logger.info(f"收到 ListTools 请求，当前配置数量: {len(_tools_config)}") - tools = [] - - for query in _tools_config: - name = query.get("name", "") - description = query.get("description") or query.get("toolPrompt") or query.get("uniqueName", "") - - logger.debug(f"处理工具配置: name={name}, description={description[:50] if description else 'None'}...") - - # 优先从 sqlParams 转换，因为它包含更详细的文件配置信息 - sql_params = query.get("sqlParams") - if sql_params: - try: - input_schema = convert_sql_params_to_input_schema(sql_params) - logger.debug(f"从 sqlParams 转换的 inputSchema: {json.dumps(input_schema, ensure_ascii=False)}") - except Exception as e: - logger.warning(f"从 sqlParams 转换失败: {e}，尝试使用 inputJsonSchema") - # 回退到 inputJsonSchema - input_json_schema = query.get("inputJsonSchema") - if input_json_schema: - try: - if isinstance(input_json_schema, str): - input_schema = json.loads(input_json_schema) - else: - input_schema = input_json_schema - input_schema = sanitize_json_schema(input_schema) - logger.debug(f"使用 inputJsonSchema (已清理): {json.dumps(input_schema, ensure_ascii=False)}") - except (json.JSONDecodeError, TypeError) as e2: - logger.error(f"解析 inputJsonSchema 也失败: {e2}，使用空 schema") - input_schema = {"type": "object", "properties": {}, "required": []} - else: - logger.warning("sqlParams 和 inputJsonSchema 都不可用，使用空 schema") - input_schema = {"type": "object", "properties": {}, "required": []} - else: - # 如果没有 sqlParams，尝试使用 inputJsonSchema - input_json_schema = query.get("inputJsonSchema") - if input_json_schema: - try: - if isinstance(input_json_schema, str): - input_schema = json.loads(input_json_schema) - else: - input_schema = input_json_schema - input_schema = sanitize_json_schema(input_schema) - logger.debug(f"使用 inputJsonSchema (已清理): {json.dumps(input_schema, ensure_ascii=False)}") - except (json.JSONDecodeError, TypeError) as e: - logger.error(f"解析 inputJsonSchema 失败: {e}，使用空 schema") - input_schema = {"type": "object", "properties": {}, "required": []} - else: - logger.warning("sqlParams 和 inputJsonSchema 都不存在，使用空 schema") - input_schema = {"type": "object", "properties": {}, "required": []} - - # 添加 workflow_extraContext 字段到 schema，可以接收任何类型（非必填） - if "properties" not in input_schema: - input_schema["properties"] = {} - input_schema["properties"]["workflow_extraContext"] = { - "description": "工作流额外的上下文参数（如环境变量等），可以是任何类型，非必填" - } - - tools.append( - types.Tool( - name=name, - description=description, - inputSchema=input_schema - ) - ) - - logger.info(f"ListTools 响应: 返回 {len(tools)} 个工具") - return tools - - -@server.call_tool() -async def handle_call_tool( - name: str, - arguments: dict | None, -) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: - """调用工具""" - logger.info(f"收到 CallTool 请求: name={name}, arguments={json.dumps(arguments, ensure_ascii=False) if arguments else 'None'}") - - # 查找对应的工具配置 - tool_config = None - for query in _tools_config: - if query.get("name") == name: - tool_config = query - break - - if tool_config is None: - logger.error(f"未找到工具配置: {name}") - raise ValueError(f"未知工具: {name}") - - logger.debug(f"找到工具配置: {json.dumps(tool_config, ensure_ascii=False, indent=2)}") - - # 获取工作流ID - workflow_id = tool_config.get("workflowId") or get_workflow_id() - logger.info(f"使用工作流ID: {workflow_id}") - - # 提取 workflow_extraContext 字段并合并到 inputs - inputs = arguments or {} - workflow_extra_context = inputs.pop("workflow_extraContext", None) - - # 如果 workflow_extraContext 存在，将其内容合并到 inputs - if workflow_extra_context is not None: - if isinstance(workflow_extra_context, dict): - # 如果 workflow_extraContext 是字典，合并到 inputs - inputs.update(workflow_extra_context) - logger.info(f"workflow_extraContext 是字典类型，已合并到 inputs: {json.dumps(workflow_extra_context, ensure_ascii=False)}") - else: - # 如果 workflow_extraContext 不是字典，作为 workflow_extraContext 字段保留 - inputs["workflow_extraContext"] = workflow_extra_context - logger.info(f"workflow_extraContext 是 {type(workflow_extra_context).__name__} 类型，保留为 workflow_extraContext 字段") - - # 构建请求数据 - request_data = { - "workflowId": workflow_id, - "inputs": inputs - } - - logger.info(f"执行工作流请求数据: {json.dumps(request_data, ensure_ascii=False, indent=2)}") - - try: - # 调用工作流执行API - result = execute_workflow(request_data) - logger.info(f"工作流执行成功: {workflow_id}") - logger.debug(f"工作流执行结果: {json.dumps(result, ensure_ascii=False, indent=2)}") - - # 提取 final_output 最后一个节点的值 - result = extract_final_output(result) - except Exception as e: - logger.error(f"工作流执行失败: {e}", exc_info=True) - result = { - "error": str(e), - "tool_name": name, - "workflow_id": workflow_id - } - - return [ - types.TextContent( - type="text", - text=json.dumps(result, ensure_ascii=False, indent=2) - ) - ] - - -async def run_server(): - """运行 MCP Server (stdio 模式)""" - async with stdio_server() as streams: - await server.run( - streams[0], - streams[1], - InitializationOptions( - server_name="workflow_mcp_server", - server_version="0.1.0", - capabilities=server.get_capabilities( - notification_options=NotificationOptions(), - experimental_capabilities={}, - ), - ), - ) - - -def main(): - """主入口""" - global _config - - logger.info("=" * 50) - logger.info("Workflow MCP Server 启动") - logger.info("=" * 50) - - # 解析命令行参数 - args = parse_arguments() - _config = vars(args) - logger.info(f"命令行参数: {_config}") - - # 创建加载器并初始化工具 - logger.info(f"使用模式: {args.mode}") - loader = create_loader( - mode=args.mode, - json_path=args.json_path, - workflow_id=args.workflow_id - ) - init_tools(loader) - - logger.info("开始运行 MCP Server (stdio 模式)") - # 运行服务器 - anyio.run(run_server) - - -if __name__ == "__main__": - main() diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/pyproject.toml b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/pyproject.toml deleted file mode 100644 index 1a485da..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/pyproject.toml +++ /dev/null @@ -1,18 +0,0 @@ -[project] -name = "lzwcai-workflow-to-mcp" -version = "0.1.0" -description = "从 businessQueries 动态生成 MCP 工具" -readme = "README.md" -requires-python = ">=3.10" -dependencies = [ - "mcp>=1.0.0", - "anyio>=4.0.0", - "requests>=2.28.0", -] - -[project.scripts] -workflow-mcp = "main:main" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/schema_converter.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/schema_converter.py deleted file mode 100644 index 640a4a5..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/schema_converter.py +++ /dev/null @@ -1,304 +0,0 @@ -""" -Schema 转换器 -将 sqlParams 数组格式转换为 MCP 工具需要的 JSON Schema 格式 - -支持的类型: -- string: 文本输入 -- paragraph: 段落/多行文本 -- select: 下拉选项 -- number: 数字输入 -- file: 单文件上传 -- fileList: 多文件上传 -""" -import json -from typing import Any - - -def sanitize_json_schema(schema: dict) -> dict: - """ - 清理 JSON Schema，确保 enum 字段只包含字符串值 - - Args: - schema: 原始 JSON Schema - - Returns: - dict: 清理后的 JSON Schema - """ - if not isinstance(schema, dict): - return schema - - # 递归处理 properties - if "properties" in schema and isinstance(schema["properties"], dict): - for prop_name, prop_schema in schema["properties"].items(): - if isinstance(prop_schema, dict) and "enum" in prop_schema: - enum_values = prop_schema["enum"] - if isinstance(enum_values, list) and len(enum_values) > 0: - # 检查是否是对象数组 - if isinstance(enum_values[0], dict): - # 提取 value 字段 - cleaned_enum = [ - item.get("value", item.get("label", "")) - for item in enum_values - if isinstance(item, dict) - ] - # 过滤空值 - prop_schema["enum"] = [v for v in cleaned_enum if v] - # 保留原始数据 - prop_schema["x-options"] = enum_values - - return schema - - -def convert_param_to_schema_property(param: dict) -> tuple[str, dict, bool]: - """ - 将单个参数转换为 JSON Schema property - - Args: - param: 参数配置，格式如: - { - "type": "string", - "name": "company_names", - "displayName": "公司名称", - "maxLength": 200, - "defaultValue": "", - "required": true, - "options": ["选项1", "选项2"], # 仅 select 类型 - "fileConfig": { # 仅 file/fileList 类型 - "uploadMode": "both", - "typeCategories": ["image"], - "customAccept": "", - "accept": ".jpg,.jpeg,.png" - } - } - - Returns: - tuple: (property_name, property_schema, is_required) - """ - param_type = param.get("type", "string") - param_name = param.get("name", "") - display_name = param.get("displayName", param_name) - default_value = param.get("defaultValue", "") - max_length = param.get("maxLength") - is_required = param.get("required", False) - options = param.get("options", []) - file_config = param.get("fileConfig", {}) - - property_schema = { - "description": display_name - } - - if param_type == "string": - property_schema["type"] = "string" - if max_length: - property_schema["maxLength"] = max_length - - elif param_type == "paragraph": - property_schema["type"] = "string" - property_schema["format"] = "paragraph" - if max_length: - property_schema["maxLength"] = max_length - - elif param_type == "select": - property_schema["type"] = "string" - if options: - # 处理 options 可能是对象数组 [{label, value}] 或字符串数组的情况 - if isinstance(options, list) and len(options) > 0: - if isinstance(options[0], dict): - # 对象数组，提取 value 字段作为 enum，过滤空值 - enum_values = [ - opt.get("value", opt.get("label", "")) - for opt in options - if isinstance(opt, dict) - ] - # 过滤掉空字符串 - property_schema["enum"] = [v for v in enum_values if v] - # 保留原始 options 供前端使用（如果前端支持） - property_schema["x-options"] = options - else: - # 字符串数组，直接使用，过滤空值 - property_schema["enum"] = [v for v in options if v] - - elif param_type == "number": - property_schema["type"] = "number" - - elif param_type == "file": - # 单文件上传 - property_schema["type"] = "string" - property_schema["format"] = "url" - property_schema["x-file-type"] = "single" - - # 添加文件配置信息 - if file_config: - if file_config.get("accept"): - property_schema["x-accept"] = file_config["accept"] - if file_config.get("typeCategories"): - property_schema["x-type-categories"] = file_config["typeCategories"] - if file_config.get("uploadMode"): - property_schema["x-upload-mode"] = file_config["uploadMode"] - # 保留完整配置 - property_schema["x-file-config"] = file_config - - elif param_type == "fileList": - # 多文件上传 - property_schema["type"] = "array" - property_schema["items"] = { - "type": "string", - "format": "url" - } - property_schema["x-file-type"] = "multiple" - - # 添加文件配置信息 - if file_config: - if file_config.get("accept"): - property_schema["x-accept"] = file_config["accept"] - if file_config.get("typeCategories"): - property_schema["x-type-categories"] = file_config["typeCategories"] - if file_config.get("uploadMode"): - property_schema["x-upload-mode"] = file_config["uploadMode"] - # 保留完整配置 - property_schema["x-file-config"] = file_config - - else: - # 默认当作 string 处理 - property_schema["type"] = "string" - - # 添加默认值（文件类型不需要默认值） - if default_value not in (None, "") and param_type not in ("file", "fileList"): - if param_type == "number": - try: - property_schema["default"] = int(default_value) if str(default_value).isdigit() else float(default_value) - except (ValueError, TypeError): - property_schema["default"] = default_value - else: - property_schema["default"] = default_value - - return param_name, property_schema, is_required - - -def convert_sql_params_to_input_schema(sql_params: str | list) -> dict: - """ - 将 sqlParams 转换为 MCP 工具的 inputSchema - - Args: - sql_params: sqlParams 字段值，可以是 JSON 字符串或已解析的列表 - 格式: [{"type": "string", "name": "xxx", ...}, ...] - - Returns: - dict: MCP 工具的 inputSchema，格式如: - { - "type": "object", - "properties": {...}, - "required": [...] - } - """ - # 解析 JSON 字符串 - if isinstance(sql_params, str): - try: - params_list = json.loads(sql_params) - except json.JSONDecodeError: - return {"type": "object", "properties": {}, "required": []} - else: - params_list = sql_params - - if not isinstance(params_list, list): - return {"type": "object", "properties": {}, "required": []} - - input_schema = { - "type": "object", - "properties": {}, - "required": [] - } - - for param in params_list: - if not isinstance(param, dict): - continue - - name, schema, is_required = convert_param_to_schema_property(param) - - if name: - input_schema["properties"][name] = schema - if is_required: - input_schema["required"].append(name) - - return input_schema - - -def convert_business_query_to_tool(query: dict) -> dict: - """ - 将单个 businessQuery 转换为 MCP Tool 配置 - - Args: - query: businessQuery 对象 - - Returns: - dict: MCP Tool 配置 - { - "name": "工具名称", - "description": "工具描述", - "inputSchema": {...} - } - """ - name = query.get("name", "") - description = query.get("description") or query.get("toolPrompt") or query.get("uniqueName", "") - sql_params = query.get("sqlParams", "[]") - - input_schema = convert_sql_params_to_input_schema(sql_params) - - return { - "name": name, - "description": description, - "inputSchema": input_schema, - # 保留原始数据供后续使用 - "_raw": { - "id": query.get("id"), - "uniqueName": query.get("uniqueName"), - "sqlTemplate": query.get("sqlTemplate"), - "datasourceId": query.get("datasourceId"), - "toolType": query.get("toolType"), - } - } - - -def convert_all_queries_to_tools(queries: list[dict]) -> list[dict]: - """ - 将所有 businessQueries 转换为 MCP Tools 列表 - - Args: - queries: businessQueries 列表 - - Returns: - list: MCP Tools 配置列表 - """ - tools = [] - for query in queries: - tool = convert_business_query_to_tool(query) - if tool["name"]: - tools.append(tool) - return tools - - -# 测试用 -if __name__ == "__main__": - # 测试单个参数转换 - test_param = { - "type": "select", - "name": "subject_name", - "displayName": "科目名称", - "maxLength": 100, - "defaultValue": "", - "required": True, - "options": ["管理费用", "销售费用", "财务费用"] - } - - name, schema, required = convert_param_to_schema_property(test_param) - print(f"参数名: {name}") - print(f"Schema: {json.dumps(schema, ensure_ascii=False, indent=2)}") - print(f"必填: {required}") - print() - - # 测试完整 sqlParams 转换 - test_sql_params = '[{"type":"paragraph","name":"digest","displayName":"摘要关键字","maxLength":500,"defaultValue":"","required":true},{"type":"string","name":"company_names","displayName":"公司名称","maxLength":200,"defaultValue":"","required":false},{"type":"number","name":"year","displayName":"会计年度","maxLength":4,"defaultValue":"2025","required":true}]' - - input_schema = convert_sql_params_to_input_schema(test_sql_params) - print("InputSchema:") - print(json.dumps(input_schema, ensure_ascii=False, indent=2)) diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__init__.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__init__.py deleted file mode 100644 index 892d416..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__init__.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Utils package for lzwcai_workflow_to_mcp""" - -from .json_helper import load_json -from .name_helper import generate_tool_name -from .schema_helper import generate_input_schema, validate_input_schema -from .api_client import ( - WorkflowAPIClient, - get_workflow_by_id, - execute_workflow, - process_workflow_response, - get_default_client, - DEFAULT_TIMEOUT, - WORKFLOW_EXECUTE_TIMEOUT, - DEFAULT_POLLING_INTERVAL, - DEFAULT_MAX_POLL_COUNT, - DEFAULT_MAX_ERROR_COUNT, -) -from .env_config import get_workflow_id, get_backend_base_url, get_env_config, set_env_variable - -__all__ = [ - 'load_json', - 'generate_tool_name', - 'generate_input_schema', - 'validate_input_schema', - 'WorkflowAPIClient', - 'get_workflow_by_id', - 'execute_workflow', - 'process_workflow_response', - 'get_default_client', - 'DEFAULT_TIMEOUT', - 'WORKFLOW_EXECUTE_TIMEOUT', - 'DEFAULT_POLLING_INTERVAL', - 'DEFAULT_MAX_POLL_COUNT', - 'DEFAULT_MAX_ERROR_COUNT', - 'get_workflow_id', - 'get_backend_base_url', - 'get_env_config', - 'set_env_variable' -] diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__pycache__/__init__.cpython-312.pyc 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z&b%vLmw2t#Omp&Qsb8m#WnXzW+tQ!4_b=D?uUNMxo0qLG;0LN_g;L0x21mKWuhzfy_7ijM(TsveI*3jJMU&*{o0;6m^*OfDc) Tuple[float, int, int]: - """ - 获取轮询配置（允许用环境变量覆盖） - - Env: - - workflowPollingIntervalSeconds: float, 默认 1 - - workflowMaxPollCount: int, 默认 120 - - workflowMaxErrorCount: int, 默认 3 - """ - try: - interval = float(os.environ.get("workflowPollingIntervalSeconds", str(DEFAULT_POLLING_INTERVAL))) - except ValueError: - interval = DEFAULT_POLLING_INTERVAL - - try: - max_polls = int(os.environ.get("workflowMaxPollCount", str(DEFAULT_MAX_POLL_COUNT))) - except ValueError: - max_polls = DEFAULT_MAX_POLL_COUNT - - try: - max_errors = int(os.environ.get("workflowMaxErrorCount", str(DEFAULT_MAX_ERROR_COUNT))) - except ValueError: - max_errors = DEFAULT_MAX_ERROR_COUNT - - # 避免被错误配置成 0 或负数 - interval = interval if interval > 0 else DEFAULT_POLLING_INTERVAL - max_polls = max_polls if max_polls > 0 else DEFAULT_MAX_POLL_COUNT - max_errors = max_errors if max_errors > 0 else DEFAULT_MAX_ERROR_COUNT - - return interval, max_polls, max_errors - - -def _extract_async_ids(execute_response: Dict[str, Any]) -> Optional[Tuple[str, str, str]]: - """ - 从执行接口响应中提取 (status, execution_id, workflow_id) - 兼容 snake_case / camelCase。 - """ - if not isinstance(execute_response, dict): - return None - data = execute_response.get("data") - if not isinstance(data, dict): - return None - - status = data.get("status") - execution_id = data.get("execution_id") or data.get("executionId") - workflow_id = data.get("workflow_id") or data.get("workflowId") - - if status and execution_id and workflow_id: - return str(status), str(execution_id), str(workflow_id) - return None - - -class WorkflowAPIClient: - """工作流API客户端""" - - def __init__( - self, - base_url: Optional[str] = None, - token: Optional[str] = None, - default_timeout: float = DEFAULT_TIMEOUT, - execute_timeout: float = WORKFLOW_EXECUTE_TIMEOUT - ): - """ - 初始化API客户端 - - Args: - base_url: API基础URL（默认从环境变量 backendBaseUrl 读取） - token: 认证令牌（默认从环境变量 workflowExecuteKey 读取） - default_timeout: 普通请求超时时间（秒），默认30秒 - execute_timeout: 工作流执行超时时间（秒），默认300秒（5分钟） - """ - if base_url is None: - base_url = get_backend_base_url() - - if token is None: - token = get_workflow_execute_key() - - self.base_url = base_url.rstrip('/') - self.token = token - self.default_timeout = default_timeout - self.execute_timeout = execute_timeout - self._client: Optional[httpx.Client] = None - - # 打印完整的配置信息用于调试 - logger.info(f"[客户端初始化] base_url={self.base_url}") - logger.info(f"[客户端初始化] token={self.token}") - logger.info(f"[客户端初始化] execute_timeout={self.execute_timeout}s") - - @property - def client(self) -> httpx.Client: - """懒加载 HTTP 客户端""" - if self._client is None: - self._client = httpx.Client(timeout=self.default_timeout) - return self._client - - def _get_headers(self) -> Dict[str, str]: - """获取请求头""" - return { - 'X-API-Key': f'{self.token}', - } - - def get_workflow_by_id(self, workflow_id: str) -> Dict[str, Any]: - """ - 根据工作流ID获取工作流信息 - - Args: - workflow_id: 工作流ID - - Returns: - API响应数据 - - Raises: - Exception: 请求失败时抛出 - """ - url = f"{self.base_url}/system/workflowManage/getByWorkflowId/{workflow_id}" - - try: - logger.info(f"[API请求] GET {url}") - logger.debug(f"[API请求] Headers: {self._get_headers()}") - - response = self.client.get( - url, - headers=self._get_headers() - ) - - logger.info(f"[API响应] HTTP {response.status_code}") - logger.debug(f"[API响应] Headers: {dict(response.headers)}") - - response.raise_for_status() - data = response.json() - - logger.info(f"[API响应] 获取工作流配置成功: workflow_id={workflow_id}") - logger.debug(f"[API响应] Body: {json.dumps(data, ensure_ascii=False, indent=2)}") - - return data - - except httpx.TimeoutException: - error_msg = f"API请求超时: {url}" - logger.error(f"[API错误] {error_msg}") - raise Exception(error_msg) - - except httpx.HTTPStatusError as e: - error_msg = f"API请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(f"[API错误] {error_msg}") - logger.error(f"[API错误] 响应内容: {e.response.text}") - raise Exception(error_msg) - - except httpx.RequestError as e: - error_msg = f"API请求异常: {url}, 错误: {str(e)}" - logger.error(f"[API错误] {error_msg}") - raise Exception(error_msg) - - except Exception as e: - error_msg = f"处理API响应时出错: {str(e)}" - logger.error(f"[API错误] {error_msg}", exc_info=True) - raise Exception(error_msg) - - def execute_workflow(self, request_data: Dict[str, Any]) -> Dict[str, Any]: - """ - 执行工作流 - - Args: - request_data: 请求数据，包含工作流执行所需的参数 - - Returns: - API响应数据 - - Raises: - Exception: 请求失败时抛出 - """ - url = f"{self.base_url}/open/workflow/execute" - - try: - headers = self._get_headers() - headers['Content-Type'] = 'application/json' - headers['Accept'] = '*/*' - - # 打印完整的请求信息 - logger.info(f"[执行工作流] URL: {url}") - logger.info(f"[执行工作流] Token: {self.token}") - logger.info(f"[执行工作流] Headers: {json.dumps(headers, ensure_ascii=False)}") - logger.info(f"[执行工作流] 请求参数: {json.dumps(request_data, ensure_ascii=False, indent=2)}") - logger.info(f"[执行工作流] 超时时间: {self.execute_timeout}s") - - # 使用更长的超时时间执行工作流 - response = self.client.post( - url, - headers=headers, - json=request_data, - timeout=self.execute_timeout # 使用工作流执行专用超时时间 - ) - - logger.info(f"[API响应] HTTP {response.status_code}") - logger.debug(f"[API响应] Headers: {dict(response.headers)}") - - response.raise_for_status() - data = response.json() - - logger.info(f"[API响应] 执行工作流成功") - logger.debug(f"[API响应] Body: {json.dumps(data, ensure_ascii=False, indent=2)}") - - # 兼容：如果服务端返回 async 的 execution_id/workflow_id，则按文档轮询状态接口直到结束 - async_info = _extract_async_ids(data) - if async_info is None: - return data - - status, execution_id, workflow_id = async_info - if status not in ("pending", "running"): - # 有些服务端可能直接返回 success/failed 但仍带 execution_id - return data - - logger.info( - f"[执行工作流] 检测到异步执行: status={status}, execution_id={execution_id}, workflow_id={workflow_id}" - ) - return self.poll_workflow_result(execution_id=execution_id, workflow_id=workflow_id) - - except httpx.TimeoutException: - error_msg = f"执行工作流API请求超时: {url}" - logger.error(f"[API错误] {error_msg}") - raise Exception(error_msg) - - except httpx.HTTPStatusError as e: - error_msg = f"执行工作流API请求失败 (HTTP {e.response.status_code}): {url}" - logger.error(f"[API错误] {error_msg}") - logger.error(f"[API错误] 响应内容: {e.response.text}") - raise Exception(error_msg) - - except httpx.RequestError as e: - error_msg = f"执行工作流API请求异常: {url}, 错误: {str(e)}" - logger.error(f"[API错误] {error_msg}") - raise Exception(error_msg) - - except Exception as e: - error_msg = f"处理执行工作流API响应时出错: {str(e)}" - logger.error(f"[API错误] {error_msg}", exc_info=True) - raise Exception(error_msg) - - def get_workflow_execute_status(self, execution_id: str, workflow_id: str) -> Dict[str, Any]: - """ - 查询异步执行状态 - - GET /open/workflow/execute/status/{executionId}?workflowId={workflowId} - """ - url = f"{self.base_url}/open/workflow/status/{execution_id}" - try: - logger.info(f"[API请求] GET {url}") - response = self.client.get( - url, - headers=self._get_headers(), - params={"workflowId": workflow_id}, - ) - logger.info(f"[API响应] HTTP {response.status_code}") - response.raise_for_status() - data = response.json() - logger.debug(f"[API响应] Body: {json.dumps(data, ensure_ascii=False, indent=2)}") - return data - except httpx.TimeoutException: - error_msg = f"查询工作流执行状态超时: {url}" - logger.error(f"[API错误] {error_msg}") - raise Exception(error_msg) - except httpx.HTTPStatusError as e: - error_msg = f"查询工作流执行状态失败 (HTTP {e.response.status_code}): {url}" - logger.error(f"[API错误] {error_msg}") - logger.error(f"[API错误] 响应内容: {e.response.text}") - raise Exception(error_msg) - except httpx.RequestError as e: - error_msg = f"查询工作流执行状态请求异常: {url}, 错误: {str(e)}" - logger.error(f"[API错误] {error_msg}") - raise Exception(error_msg) - except Exception as e: - error_msg = f"处理查询执行状态响应时出错: {str(e)}" - logger.error(f"[API错误] {error_msg}", exc_info=True) - raise Exception(error_msg) - - def poll_workflow_result(self, execution_id: str, workflow_id: str) -> Dict[str, Any]: - """轮询直到工作流执行完成（success/failed）或超时""" - interval, max_polls, max_errors = _get_polling_config() - error_count = 0 - - logger.info( - f"[轮询] 开始轮询执行状态: execution_id={execution_id}, workflow_id={workflow_id}, " - f"interval={interval}s, max_polls={max_polls}, max_errors={max_errors}" - ) - - for i in range(max_polls): - try: - resp = self.get_workflow_execute_status(execution_id=execution_id, workflow_id=workflow_id) - if resp.get("code") != 200: - error_count += 1 - logger.warning(f"[轮询] 状态接口返回非200(code={resp.get('code')}), error_count={error_count}") - if error_count >= max_errors: - raise Exception(f"轮询连续错误次数过多: code={resp.get('code')}, msg={resp.get('msg')}") - time.sleep(interval) - continue - - error_count = 0 - data = resp.get("data") or {} - status = data.get("status") - - logger.info(f"[轮询] 第{i+1}/{max_polls}次: status={status}") - - if status in ("pending", "running"): - time.sleep(interval) - continue - if status in ("success", "failed"): - return resp - - # 兜底：未知状态 - raise Exception(f"未知执行状态: {status}") - - except Exception as e: - error_count += 1 - logger.warning(f"[轮询] 轮询异常: {e}, error_count={error_count}") - if error_count >= max_errors: - raise - time.sleep(interval) - - raise Exception("轮询超时：超过最大轮询次数仍未完成") - - def close(self): - """关闭HTTP客户端""" - if self._client is not None: - self._client.close() - self._client = None - - def __enter__(self): - """支持 context manager""" - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - """退出时关闭客户端""" - self.close() - return False - - -# 懒加载的默认客户端 -_default_client: Optional[WorkflowAPIClient] = None - - -def get_default_client() -> WorkflowAPIClient: - """获取默认客户端（懒加载）""" - global _default_client - if _default_client is None: - _default_client = WorkflowAPIClient() - return _default_client - - -def get_workflow_by_id(workflow_id: str, base_url: Optional[str] = None, token: Optional[str] = None) -> Dict[str, Any]: - """ - 便捷函数：根据工作流ID获取工作流信息 - - Args: - workflow_id: 工作流ID - base_url: API基础URL（可选） - token: 认证令牌（可选） - - Returns: - API响应数据 - """ - if base_url or token: - with WorkflowAPIClient(base_url=base_url, token=token) as client: - return client.get_workflow_by_id(workflow_id) - else: - return get_default_client().get_workflow_by_id(workflow_id) - - -def execute_workflow( - request_data: Dict[str, Any], - base_url: Optional[str] = None, - token: Optional[str] = None, - timeout: float = WORKFLOW_EXECUTE_TIMEOUT -) -> Dict[str, Any]: - """ - 便捷函数：执行工作流 - - Args: - request_data: 请求数据 - base_url: API基础URL（可选） - token: 认证令牌（可选） - timeout: 超时时间（秒），默认300秒（5分钟） - - Returns: - API响应数据 - """ - if base_url or token: - with WorkflowAPIClient(base_url=base_url, token=token, execute_timeout=timeout) as client: - return client.execute_workflow(request_data) - else: - # 更新默认客户端的超时时间 - default_client = get_default_client() - default_client.execute_timeout = timeout - return default_client.execute_workflow(request_data) - - -def extract_final_output(response: Dict[str, Any]) -> Dict[str, Any]: - """ - 提取工作流执行结果中 final_output 的最后一个属性的所有值 - - Args: - response: execute_workflow 返回的响应数据 - - Returns: - final_output 中最后一个节点的输出值 - """ - try: - # 获取 data.final_output - data = response.get("data", {}) - final_output = data.get("final_output", {}) - - if not final_output: - logger.warning("final_output 为空") - return {} - - # 获取最后一个 key 的值 - last_key = list(final_output.keys())[-1] - result = final_output[last_key] - - logger.info(f"提取 final_output 最后节点成功: key={last_key}") - logger.debug(f"提取结果: {json.dumps(result, ensure_ascii=False)}") - - return result - - except Exception as e: - logger.error(f"提取 final_output 失败: {e}", exc_info=True) - return {} - - -def process_workflow_response(response: Dict[str, Any]) -> List[Dict[str, Any]]: - """ - 处理API响应数据，映射为工作流配置格式 - - Args: - response: API原始响应数据 - - Returns: - 处理后的工作流配置列表 - """ - try: - data_list = response.get("data", []) - - # 如果 data 是单个对象而不是列表，转换为列表 - if isinstance(data_list, dict): - data_list = [data_list] - - workflows = [] - for workflow in data_list: - # 解析 inputParams 字符串为 JSON 对象 - input_params = workflow.get("inputParams", {}) - if isinstance(input_params, str): - try: - input_params = json.loads(input_params) - except json.JSONDecodeError: - input_params = {} - - # 映射字段 - config = { - "id": workflow.get("id"), - "workflowId": workflow.get("workflowId"), - "workflowName": workflow.get("workflowName") or workflow.get("name"), - "workflowDescription": workflow.get("workflowDescription") or workflow.get("description"), - "inputParams": input_params - } - workflows.append(config) - - logger.info(f"成功处理 {len(workflows)} 条工作流数据") - return workflows - - except Exception as e: - logger.error(f"处理API响应数据失败: {e}", exc_info=True) - raise diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/env_config.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/env_config.py deleted file mode 100644 index eac8f35..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/env_config.py +++ /dev/null @@ -1,85 +0,0 @@ -"""环境变量配置模块""" - -import os -from typing import Optional - - -def get_workflow_id(default: str = "") -> str: - """ - 获取工作流ID环境变量 - - Args: - default: 默认值（默认为 ""） - - Returns: - str: 工作流ID - - Environment Variables: - workflowId: 工作流ID - """ - return os.environ.get("workflowId", default) - - -def get_backend_base_url(default: str = "http://lzwcai-demp-corp-manager:8086") -> str: - """ - 获取后端API基础URL环境变量 - - Args: - default: 默认值（默认为 "http://lzwcai-demp-corp-manager:8086"） - - Returns: - str: 后端API基础URL - - Environment Variables: - backendBaseUrl: 后端API基础URL - """ - return os.environ.get("backendBaseUrl", default) - - -def get_workflow_execute_key(default: str = "") -> str: - """ - 获取工作流执行密钥（Token）环境变量 - - Args: - default: 默认值（默认为 ""） - - Returns: - str: 工作流执行密钥 - - Environment Variables: - workflowExecuteKey: 工作流执行密钥/Token - """ - return os.environ.get("workflowExecuteKey", default) - - -def get_env_config() -> dict: - """ - 获取所有环境配置 - - Returns: - dict: 包含所有配置的字典 - - Example: - config = get_env_config() - print(config['workflow_id']) # 输出: "" - print(config['backend_base_url']) # 输出: "http://lzwcai-demp-corp-manager:8086" - """ - return { - "workflow_id": get_workflow_id(), - "backend_base_url": get_backend_base_url(), - "workflow_execute_key": get_workflow_execute_key() - } - - -def set_env_variable(key: str, value: str) -> None: - """ - 设置环境变量（仅在当前进程中有效） - - Args: - key: 环境变量名 - value: 环境变量值 - - Example: - set_env_variable("workflowId", "1234567890") - """ - os.environ[key] = value diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/json_helper.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/json_helper.py deleted file mode 100644 index cd6894e..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/json_helper.py +++ /dev/null @@ -1,59 +0,0 @@ -"""JSON 文件读取工具""" - -import json -from pathlib import Path -from typing import Any, Union - - -def load_json(json_path: Union[str, Path]) -> Any: - """ - 读取 JSON 文件并返回其内容 - - Args: - json_path: JSON 文件的路径（支持字符串或 Path 对象） - - Returns: - JSON 文件中解析后的数据（可以是字典、列表或其他 JSON 类型） - - Raises: - FileNotFoundError: 当文件不存在时 - json.JSONDecodeError: 当 JSON 格式无效时 - Exception: 其他读取错误 - - Example: - >>> data = load_json('config.json') - >>> print(data) - {'key': 'value'} - - >>> data = load_json(Path('data/users.json')) - >>> print(data) - [{'id': 1, 'name': 'Alice'}] - """ - try: - # 转换为 Path 对象 - path = Path(json_path) - - # 检查文件是否存在 - if not path.exists(): - raise FileNotFoundError(f"JSON 文件不存在: {json_path}") - - # 检查是否为文件 - if not path.is_file(): - raise ValueError(f"路径不是一个文件: {json_path}") - - # 读取并解析 JSON 文件 - with open(path, 'r', encoding='utf-8') as f: - data = json.load(f) - - return data - - except json.JSONDecodeError as e: - raise json.JSONDecodeError( - f"JSON 格式错误: {e.msg}", - e.doc, - e.pos - ) - except FileNotFoundError: - raise - except Exception as e: - raise Exception(f"读取 JSON 文件时发生错误: {str(e)}") diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/logger_config.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/logger_config.py deleted file mode 100644 index 38efa16..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/logger_config.py +++ /dev/null @@ -1,174 +0,0 @@ -# -*- coding: utf-8 -*- -""" -统一日志配置模块 -提供系统级别的日志配置和管理 -""" - -import os -import sys -import logging -from logging.handlers import RotatingFileHandler, TimedRotatingFileHandler -from datetime import datetime -from pathlib import Path - - -class LoggerConfig: - """日志配置管理类""" - - def __init__(self, logs_dir: str = None): - """初始化日志配置 - - Args: - logs_dir: 日志目录路径，默认为项目根目录下的logs文件夹 - """ - if logs_dir: - self.logs_dir = Path(logs_dir) - else: - project_root = Path(__file__).parent.parent - self.logs_dir = project_root / "logs" - - self.logs_dir.mkdir(exist_ok=True) - - self.log_format = '%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s' - self.date_format = '%Y-%m-%d %H:%M:%S' - self.log_level = self._get_log_level_from_env() - self._initialized = False - - def _get_log_level_from_env(self) -> int: - log_level_str = os.getenv('LOG_LEVEL', 'INFO').upper() - level_mapping = { - 'DEBUG': logging.DEBUG, - 'INFO': logging.INFO, - 'WARNING': logging.WARNING, - 'WARN': logging.WARNING, - 'ERROR': logging.ERROR, - 'CRITICAL': logging.CRITICAL, - 'FATAL': logging.CRITICAL - } - return level_mapping.get(log_level_str, logging.INFO) - - def setup_logging(self, - app_name: str = "lzwcai_workflow_to_mcp", - log_level: int = logging.INFO, - max_file_size: int = 10 * 1024 * 1024, - backup_count: int = 5, - console_output: bool = True) -> logging.Logger: - if self._initialized: - return logging.getLogger() - - root_logger = logging.getLogger() - root_logger.setLevel(log_level) - - for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) - - formatter = logging.Formatter(self.log_format, self.date_format) - - # 1. 主日志文件 - 按大小滚动 - main_log_file = self.logs_dir / f"{app_name}.log" - file_handler = RotatingFileHandler( - main_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - file_handler.setLevel(log_level) - file_handler.setFormatter(formatter) - root_logger.addHandler(file_handler) - - # 2. 错误日志文件 - error_log_file = self.logs_dir / f"{app_name}_error.log" - error_handler = RotatingFileHandler( - error_log_file, - maxBytes=max_file_size, - backupCount=backup_count, - encoding='utf-8' - ) - error_handler.setLevel(logging.ERROR) - error_handler.setFormatter(formatter) - root_logger.addHandler(error_handler) - - # 3. 按日期滚动的日志文件 - daily_log_file = self.logs_dir / f"{app_name}_daily.log" - daily_handler = TimedRotatingFileHandler( - daily_log_file, - when='midnight', - interval=1, - backupCount=30, - encoding='utf-8' - ) - daily_handler.setLevel(log_level) - daily_handler.setFormatter(formatter) - daily_handler.suffix = "%Y-%m-%d" - root_logger.addHandler(daily_handler) - - # 4. 控制台输出 (MCP协议使用stdio时，必须将日志输出到stderr) - if console_output: - console_handler = logging.StreamHandler(sys.stderr) - console_handler.setLevel(log_level) - console_formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s', - self.date_format - ) - console_handler.setFormatter(console_formatter) - root_logger.addHandler(console_handler) - - self._initialized = True - root_logger.info(f"日志系统初始化完成 - 日志目录: {self.logs_dir}") - - return root_logger - - def get_module_logger(self, module_name: str) -> logging.Logger: - return logging.getLogger(module_name) - - def create_component_logger(self, - component_name: str, - log_file: str = None, - log_level: int = None) -> logging.Logger: - logger = logging.getLogger(component_name) - - if log_file: - component_log_file = self.logs_dir / log_file - handler = RotatingFileHandler( - component_log_file, - maxBytes=5 * 1024 * 1024, - backupCount=3, - encoding='utf-8' - ) - - formatter = logging.Formatter(self.log_format, self.date_format) - handler.setFormatter(formatter) - - if log_level: - handler.setLevel(log_level) - - logger.addHandler(handler) - - return logger - - def setup_mcp_logging(self) -> logging.Logger: - return self.create_component_logger( - "mcp_services", - "mcp_services.log", - logging.DEBUG - ) - - def setup_api_logging(self) -> logging.Logger: - return self.create_component_logger( - "api_requests", - "api_requests.log", - logging.INFO - ) - - -# 全局日志配置实例 -logger_config = LoggerConfig() - - -def setup_system_logging(app_name: str = "lzwcai_workflow_to_mcp", - log_level: int = logging.INFO) -> logging.Logger: - return logger_config.setup_logging(app_name, log_level) - - -def get_logger(name: str) -> logging.Logger: - return logger_config.get_module_logger(name) diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/name_helper.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/name_helper.py deleted file mode 100644 index 7a5499d..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/name_helper.py +++ /dev/null @@ -1,40 +0,0 @@ -# -*- coding: utf-8 -*- -""" -名称生成工具模块 -""" - -from pypinyin import lazy_pinyin, Style -import logging - -logger = logging.getLogger(__name__) - - -def generate_tool_name(business_name: str, tool_id: str) -> str: - """ - 根据业务名称和ID生成工具名称 - 格式: tool_拼音_id - - Args: - business_name: 业务名称（中文） - tool_id: 工具ID - - Returns: - str: 格式化的工具名称 - """ - try: - # 将中文转换为拼音（无音调，小写） - pinyin_list = lazy_pinyin(business_name, style=Style.NORMAL) - # 拼接拼音 - pinyin_str = ''.join(pinyin_list) - - # 将 ID 中的 '-' 替换为 '_' - formatted_id = tool_id.replace('-', '_') - - # 组合成最终的工具名称 - tool_name = f"workflow_{pinyin_str}_{formatted_id}" - - return tool_name - except Exception as e: - logger.error(f"生成工具名称失败: {business_name}, {tool_id}, 错误: {e}", exc_info=True) - # 降级处理：如果拼音转换失败，使用 ID - return f"workflow_{tool_id.replace('-', '_')}" diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/schema_helper.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/schema_helper.py deleted file mode 100644 index ee92867..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/schema_helper.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Schema 生成工具模块 -""" - -from typing import Any, Dict, List - - -def generate_input_schema(parameters: Dict[str, Any]) -> Dict[str, Any]: - """ - 从查询配置的参数定义生成 MCP 工具的 inputSchema - - 此函数会保留完整的 JSON Schema 信息，包括： - - type: Schema 类型（通常是 "object"） - - required: 必填字段列表 - - properties: 属性定义（包括每个属性的 type, description, format, examples 等） - - description: Schema 的整体描述（如果有） - - 以及其他任何 JSON Schema 标准字段 - - Args: - parameters: 查询配置中的参数定义字典，应该是一个完整的 JSON Schema 对象 - - Returns: - Dict[str, Any]: 符合 JSON Schema 规范的 inputSchema 对象 - """ - # 如果 parameters 本身就是一个完整的 JSON Schema 对象，直接使用 - # 但确保至少包含 type 和 properties - if not parameters: - # 如果 parameters 为空，返回一个空的 object schema - return { - "type": "object", - "properties": {}, - "required": [] - } - - # 深拷贝 parameters 以避免修改原始数据 - input_schema = dict(parameters) - - # 确保必需的字段存在 - if "type" not in input_schema: - input_schema["type"] = "object" - - if "properties" not in input_schema: - input_schema["properties"] = {} - - if "required" not in input_schema: - input_schema["required"] = [] - - return input_schema - - -def validate_input_schema(schema: Dict[str, Any]) -> tuple[bool, str]: - """ - 验证 inputSchema 是否符合基本的 JSON Schema 规范 - - Args: - schema: 要验证的 schema 对象 - - Returns: - tuple[bool, str]: (是否有效, 错误消息或成功消息) - """ - if not isinstance(schema, dict): - return False, "Schema 必须是一个字典对象" - - if schema.get("type") != "object": - return False, "Schema 的 type 字段必须是 'object'" - - if "properties" not in schema: - return False, "Schema 必须包含 properties 字段" - - if not isinstance(schema.get("properties"), dict): - return False, "Schema 的 properties 字段必须是一个字典对象" - - # 验证 required 字段（如果存在） - if "required" in schema: - required = schema["required"] - if not isinstance(required, list): - return False, "Schema 的 required 字段必须是一个列表" - - # 验证所有 required 的字段都在 properties 中定义 - properties = schema["properties"] - for field in required: - if field not in properties: - return False, f"必填字段 '{field}' 未在 properties 中定义" - - # 验证 properties 中每个字段的定义 - for prop_name, prop_def in schema["properties"].items(): - if not isinstance(prop_def, dict): - return False, f"属性 '{prop_name}' 的定义必须是一个字典对象" - - if "type" not in prop_def: - return False, f"属性 '{prop_name}' 必须包含 type 字段" - - return True, "Schema 验证通过" diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/workflow-execution-api.md b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/workflow-execution-api.md deleted file mode 100644 index c042932..0000000 --- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/workflow-execution-api.md +++ /dev/null @@ -1,321 +0,0 @@ -# 工作流执行接口文档 - -## 概述 -工作流执行分为**同步执行**和**异步执行**两种模式，通过 `async` 参数控制。 - ---- - -## 1. 调试执行接口 - -### 接口信息 -- **URL**: `POST /open/workflow/execute` -- **超时**: 600000ms (10分钟) - -### 请求参数 -```json -{ - "workflow": { - "nodes": [...], - "edges": [...] - }, - "inputs": { - "param1": "value1", - "param2": "value2" - }, - "saveLog": false, - "async": true -} -``` - -| 参数 | 类型 | 必填 | 说明 | -|------|------|------|------| -| workflow | Object | 是 | 工作流配置（节点和边） | -| inputs | Object | 否 | 开始节点的输入参数 | -| saveLog | Boolean | 否 | 是否保存执行日志 | -| async | Boolean | 否 | 是否异步执行（默认 false） | - ---- - -## 2. 响应处理 - -### 2.1 同步执行响应 (async=false) -直接返回完整执行结果： - -```json -{ - "code": 200, - "data": { - "success": true, - "status": "success", - "duration": 2.5, - "nodes_executed": 5, - "errors_count": 0, - "final_output": {...}, - "execution_history": [ - { - "node_id": "node_1", - "node_type": "start", - "status": "success", - "inputs": {...}, - "outputs": {...}, - "error": null, - "duration": 0.1, - "timestamp": "2024-01-20T10:00:00" - } - ] - } -} -``` - -### 2.2 异步执行响应 (async=true) -返回执行 ID，需要轮询查询状态： - -```json -{ - "code": 200, - "data": { - "status": "pending", - "execution_id": "exec_123456", - "workflow_id": "wf_789" - } -} -``` - ---- - -## 3. 异步执行状态查询 - -### 接口信息 -- **URL**: `GET /open/workflow/status/{executionId}?workflowId={workflowId}` - -### 请求参数 -| 参数 | 位置 | 类型 | 必填 | 说明 | -|------|------|------|------|------| -| executionId | Path | String | 是 | 执行ID | -| workflowId | Query | String | 是 | 工作流ID | - -### 响应数据 -```json -{ - "code": 200, - "data": { - "status": "success", - "success": true, - "execution_id": "exec_123456", - "workflow_id": "wf_789", - "duration": 2.5, - "nodes_executed": 5, - "errors_count": 0, - "final_output": {...}, - "execution_history": [...] - } -} -``` - -### 状态值说明 -| 状态 | 说明 | 处理方式 | -|------|------|----------| -| pending | 等待执行 | 继续轮询 | -| running | 执行中 | 继续轮询 | -| success | 执行成功 | 停止轮询，显示结果 | -| failed | 执行失败 | 停止轮询，显示错误 | - ---- - -## 4. 轮询策略 - -### 前端实现参数 -```javascript -const POLLING_INTERVAL = 1000; // 轮询间隔：1秒 -const MAX_POLL_COUNT = 120; // 最大轮询次数：2分钟 -const MAX_ERROR_COUNT = 3; // 最大连续错误次数 -``` - -### 轮询逻辑 -1. 提交异步执行请求，获取 `execution_id` 和 `workflow_id` -2. 每隔 1 秒调用状态查询接口 -3. 根据返回的 `status` 判断： - - `pending/running`: 继续轮询 - - `success/failed`: 停止轮询，处理结果 -4. 超过 120 次轮询或连续 3 次错误，停止轮询 - ---- - -## 5. Python 实现示例 - -### 5.1 同步执行 -```python -import requests - -def execute_workflow_sync(workflow_config, inputs=None): - """同步执行工作流""" - url = "http://your-api/api/system/workflowManage/debug" - - payload = { - "workflow": workflow_config, - "inputs": inputs or {}, - "saveLog": False, - "async": False - } - - response = requests.post(url, json=payload, timeout=600) - result = response.json() - - if result["code"] == 200: - return result["data"] - else: - raise Exception(f"执行失败: {result.get('msg')}") -``` - -### 5.2 异步执行 + 轮询 -```python -import requests -import time - -def execute_workflow_async(workflow_config, inputs=None): - """异步执行工作流并轮询结果""" - # 1. 提交异步执行 - url = "http://your-api/api/system/workflowManage/debug" - payload = { - "workflow": workflow_config, - "inputs": inputs or {}, - "saveLog": False, - "async": True - } - - response = requests.post(url, json=payload, timeout=600) - result = response.json() - - if result["code"] != 200: - raise Exception(f"提交失败: {result.get('msg')}") - - execution_id = result["data"]["execution_id"] - workflow_id = result["data"]["workflow_id"] - - # 2. 轮询执行状态 - status_url = f"http://your-api/api/system/workflowManage/status/{execution_id}" - params = {"workflowId": workflow_id} - - max_polls = 120 - poll_interval = 1 - error_count = 0 - - for i in range(max_polls): - try: - response = requests.get(status_url, params=params) - result = response.json() - - if result["code"] == 200: - error_count = 0 # 重置错误计数 - data = result["data"] - status = data["status"] - - if status == "success": - return data - elif status == "failed": - raise Exception(f"执行失败: {data.get('error')}") - elif status in ["pending", "running"]: - time.sleep(poll_interval) - continue - else: - raise Exception(f"未知状态: {status}") - else: - error_count += 1 - if error_count >= 3: - raise Exception("连续错误次数过多") - time.sleep(poll_interval) - - except requests.RequestException as e: - error_count += 1 - if error_count >= 3: - raise Exception(f"网络异常: {str(e)}") - time.sleep(poll_interval) - - raise Exception("执行超时") -``` - -### 5.3 完整使用示例 -```python -# 工作流配置 -workflow_config = { - "nodes": [ - { - "id": "start_1", - "type": "start", - "data": { - "config": { - "inputs": [ - {"name": "text", "type": "string", "required": True} - ] - } - } - }, - # ... 其他节点 - ], - "edges": [ - {"source": "start_1", "target": "node_2"} - ] -} - -# 输入参数 -inputs = { - "text": "Hello World" -} - -# 同步执行 -try: - result = execute_workflow_sync(workflow_config, inputs) - print("执行成功:", result) -except Exception as e: - print("执行失败:", e) - -# 异步执行 -try: - result = execute_workflow_async(workflow_config, inputs) - print("执行成功:", result) -except Exception as e: - print("执行失败:", e) -``` - ---- - -## 6. 响应数据结构 - -### execution_history 节点执行记录 -```python -{ - "node_id": "node_1", # 节点ID - "node_type": "llm", # 节点类型 - "status": "success", # 执行状态 - "inputs": {...}, # 输入参数 - "outputs": {...}, # 输出结果 - "error": None, # 错误信息 - "duration": 1.2, # 执行耗时(秒) - "timestamp": "2024-01-20..." # 时间戳 -} -``` - -### 节点类型枚举 -- `start`: 开始节点 -- `end`: 结束节点 -- `llm`: 大模型节点 -- `code`: 代码节点 -- `condition`: 条件分支 -- `loop`: 循环节点 -- `http`: HTTP请求 -- `knowledge`: 知识库 -- `agent`: 智能体 -- `tool`: 工具节点 -- `variable`: 变量节点 -- `template`: 模板节点 -- `subflow`: 子流程 - ---- - -## 7. 注意事项 - -1. **超时设置**: 调试接口超时时间为 10 分钟，适合长时间运行的工作流 -2. **异步推荐**: 对于复杂工作流，建议使用异步模式避免超时 -3. **轮询频率**: 建议 1 秒轮询一次，避免过于频繁 -4. **错误处理**: 需要处理网络异常、超时、连续错误等情况 -5. **日志保存**: `saveLog` 参数控制是否保存执行日志到数据库 diff --git a/lzwcai_workflow_to_mcp/main.py b/lzwcai_workflow_to_mcp/main.py deleted file mode 100644 index 18c43bd..0000000 --- a/lzwcai_workflow_to_mcp/main.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -Entry point for lzwcai-workflow-to-mcp -Runs the MCP server for workflow execution -""" - -import os - -if __name__ == "__main__": - # 设置环境变量 - os.environ["workflowId"] = "2037527155235225601" - os.environ["workflowExecuteKey"] = "wf_bd39a583670c42ceab48b3353bf2ba43" - os.environ["backendBaseUrl"] = "http://192.168.2.236:8088" - - # Import and run the actual MCP server - from lzwcai_workflow_to_mcp.main import main - main() diff --git a/lzwcai_workflow_to_mcp/pyproject.toml b/lzwcai_workflow_to_mcp/pyproject.toml deleted file mode 100644 index 8389973..0000000 --- a/lzwcai_workflow_to_mcp/pyproject.toml +++ /dev/null @@ -1,38 +0,0 @@ -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "lzwcai-workflow-to-mcp" -version = "0.1.9" -description = "MCP server for executing business SQL queries with dynamic tool generation" -readme = "README.md" -requires-python = ">=3.10" -license = {text = "MIT"} -authors = [ - {name = "lzwcai", email = "your-email@example.com"}, -] -keywords = ["mcp", "sql", "executor", "server"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", -] -dependencies = [ - "httpx>=0.28.1", - "mcp[cli]>=1.10.1", - "pypinyin>=0.53.0", -] - -[project.scripts] -lzwcai-workflow-to-mcp = "lzwcai_workflow_to_mcp.main:main" - -[tool.hatch.build.targets.wheel] -packages = ["lzwcai_workflow_to_mcp"] - -[tool.hatch.build.targets.wheel.force-include] -"lzwcai_workflow_to_mcp/businessQueries.json" = "lzwcai_workflow_to_mcp/businessQueries.json"