From 2d2db5e8665ed775367d5520b84465cb0cd17236 Mon Sep 17 00:00:00 2001 From: yuanzhipeng <2501363769@qq.com> Date: Thu, 8 Jan 2026 00:31:22 +0800 Subject: [PATCH] =?UTF-8?q?feat(lzwcai=5Fmcpskills=5FanalyzeOrder):=20?= =?UTF-8?q?=E6=B7=BB=E5=8A=A0MCP=E6=8A=80=E8=83=BD=E5=88=86=E6=9E=90?= =?UTF-8?q?=E8=AE=A2=E5=8D=95=E6=A8=A1=E5=9D=97?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 创建基于MCP协议的SQL查询执行服务器 - 实现从businessQueries.json动态生成查询工具功能 - 支持中文工具名称自动转换为拼音 - 配置详细操作日志记录（仅输出到文件，不干扰MCP通信） - 添加订单延迟预警分析、工单进度跟踪、供应链风险预警等6个业务查询模板 - 提供完整的安装、使用和开发文档 --- lzwcai_mcpskills_analyzeOrder/README.md | 138 + .../lzwcai_mcpskills_analyzeOrder/.gitignore | 10 + .../.python-version | 1 + .../lzwcai_mcpskills_analyzeOrder/README.md | 154 + .../lzwcai_mcpskills_analyzeOrder/__init__.py | 9 + .../businessQueries.json | 50 + .../logs/lzwcai_mcp_sqlexecutor.log | 233 + .../logs/lzwcai_mcp_sqlexecutor_daily.log | 109 + .../logs/lzwcai_mcp_sqlexecutor_error.log | 0 .../logs/mcp_services.log | 71 + .../lzwcai_mcpskills_analyzeOrder/main.py | 373 ++ .../pyproject.toml | 35 + .../lzwcai_mcpskills_analyzeOrder/sql11 | 169 + .../utils/__init__.py | 25 + .../utils/api_client.py | 318 ++ .../utils/env_config.py | 106 + .../utils/json_helper.py | 60 + .../utils/logger_config.py | 489 ++ .../utils/name_helper.py | 41 + .../utils/schema_helper.py | 166 + .../lzwcai_mcpskills_analyzeOrder/uv.lock | 497 ++ lzwcai_mcpskills_analyzeOrder/main.py | 13 + .../manufacturing_data_model_v1.0.0.md | 1094 +++++ lzwcai_mcpskills_analyzeOrder/pyproject.toml | 35 + .../一页式决策简报.sql | 317 ++ .../人效产值损耗三维模型仪表盘.sql | 193 + .../供应链风险预警.sql | 416 ++ .../工单执行进度与异常节点.sql | 409 ++ .../指标趋势分析与拐点预警.sql | 426 ++ .../订单延迟预警分析.sql | 154 + lzwcai_mcpskills_analyzeWorkOrder/README.md | 10 +- .../__init__.py | 2 +- .../businessQueries.json | 41 - .../lzwcai_mcpskills_analyzeWorkOrder/main.py | 6 +- lzwcai_mcpskills_analyzeWorkOrder/main.py | 4 +- .../pyproject.toml | 8 +- server.log | 6 + sql/dim_bom.json | 342 ++ sql/dim_contract.json | 542 +++ sql/dim_customer.json | 382 ++ sql/dim_equipment.json | 102 + sql/dim_operation.json | 122 + sql/dim_person.json | 1 + sql/dim_product.json | 962 ++++ sql/dim_qc_reason.json | 52 + sql/dim_routing.json | 112 + sql/dim_supplier.json | 322 ++ sql/dim_warehouse.json | 132 + sql/fact_ap_payment.json | 422 ++ sql/fact_ar_receipt.json | 422 ++ sql/fact_cash_flow.json | 322 ++ sql/fact_commission.json | 192 + sql/fact_inventory_count.json | 102 + sql/fact_inventory_movement.json | 4002 +++++++++++++++++ sql/fact_inventory_transfer.json | 122 + sql/fact_invoice.json | 322 ++ sql/fact_labor_report.json | 902 ++++ sql/fact_lead.json | 401 ++ sql/fact_operation_task.json | 852 ++++ sql/fact_opportunity.json | 212 + sql/fact_production_plan.json | 142 + sql/fact_purchase_order.json | 322 ++ sql/fact_purchase_receipt.json | 692 +++ sql/fact_purchase_request.json | 322 ++ sql/fact_purchase_return.json | 72 + sql/fact_quality_inspection.json | 722 +++ sql/fact_sales_order.json | 2102 +++++++++ sql/fact_sales_return.json | 152 + sql/fact_sales_shipment.json | 482 ++ sql/fact_scrap.json | 92 + sql/fact_work_order.json | 1082 +++++ sql/fact_write_off.json | 162 + sql/现有表.json | 107 + 73 files changed, 23927 insertions(+), 56 deletions(-) create mode 100644 lzwcai_mcpskills_analyzeOrder/README.md create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/.gitignore create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/.python-version create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/README.md create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/__init__.py create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/businessQueries.json create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor.log create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor_daily.log create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor_error.log create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/mcp_services.log create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/main.py create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/pyproject.toml create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/sql11 create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/__init__.py create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/api_client.py create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/env_config.py create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/json_helper.py create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/logger_config.py create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/name_helper.py create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/schema_helper.py create mode 100644 lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/uv.lock create mode 100644 lzwcai_mcpskills_analyzeOrder/main.py create mode 100644 lzwcai_mcpskills_analyzeOrder/manufacturing_data_model_v1.0.0.md create mode 100644 lzwcai_mcpskills_analyzeOrder/pyproject.toml create mode 100644 lzwcai_mcpskills_analyzeOrder/一页式决策简报.sql create mode 100644 lzwcai_mcpskills_analyzeOrder/人效产值损耗三维模型仪表盘.sql create mode 100644 lzwcai_mcpskills_analyzeOrder/供应链风险预警.sql create mode 100644 lzwcai_mcpskills_analyzeOrder/工单执行进度与异常节点.sql create mode 100644 lzwcai_mcpskills_analyzeOrder/指标趋势分析与拐点预警.sql create mode 100644 lzwcai_mcpskills_analyzeOrder/订单延迟预警分析.sql create mode 100644 server.log create mode 100644 sql/dim_bom.json create mode 100644 sql/dim_contract.json create mode 100644 sql/dim_customer.json create mode 100644 sql/dim_equipment.json create mode 100644 sql/dim_operation.json create mode 100644 sql/dim_person.json create mode 100644 sql/dim_product.json create mode 100644 sql/dim_qc_reason.json create mode 100644 sql/dim_routing.json create mode 100644 sql/dim_supplier.json create mode 100644 sql/dim_warehouse.json create mode 100644 sql/fact_ap_payment.json create mode 100644 sql/fact_ar_receipt.json create mode 100644 sql/fact_cash_flow.json create mode 100644 sql/fact_commission.json create mode 100644 sql/fact_inventory_count.json create mode 100644 sql/fact_inventory_movement.json create mode 100644 sql/fact_inventory_transfer.json create mode 100644 sql/fact_invoice.json create mode 100644 sql/fact_labor_report.json create mode 100644 sql/fact_lead.json create mode 100644 sql/fact_operation_task.json create mode 100644 sql/fact_opportunity.json create mode 100644 sql/fact_production_plan.json create mode 100644 sql/fact_purchase_order.json create mode 100644 sql/fact_purchase_receipt.json create mode 100644 sql/fact_purchase_request.json create mode 100644 sql/fact_purchase_return.json create mode 100644 sql/fact_quality_inspection.json create mode 100644 sql/fact_sales_order.json create mode 100644 sql/fact_sales_return.json create mode 100644 sql/fact_sales_shipment.json create mode 100644 sql/fact_scrap.json create mode 100644 sql/fact_work_order.json create mode 100644 sql/fact_write_off.json create mode 100644 sql/现有表.json diff --git a/lzwcai_mcpskills_analyzeOrder/README.md b/lzwcai_mcpskills_analyzeOrder/README.md new file mode 100644 index 0000000..e4bc0ac --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/README.md @@ -0,0 +1,138 @@ +# lzwcai-mcpskills-analyzeOrder + +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-mcpskills-analyzeOrder +``` + +### 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-analyzeOrder +``` + +## 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-analyzeOrder +``` + +### 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-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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/.gitignore b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/.gitignore new file mode 100644 index 0000000..505a3b1 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/.gitignore @@ -0,0 +1,10 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv diff --git a/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/.python-version b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/README.md b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/README.md new file mode 100644 index 0000000..0ec9671 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/README.md @@ -0,0 +1,154 @@ +# 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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/__init__.py b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/__init__.py new file mode 100644 index 0000000..3e52bf0 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/__init__.py @@ -0,0 +1,9 @@ +""" +lzwcai-mcpskills-analyzeOrder - MCP server for executing business SQL queries +""" + +__version__ = "0.1.2" +__author__ = "lzwcai" + +__all__ = [] + diff --git a/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/businessQueries.json b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/businessQueries.json new file mode 100644 index 0000000..18f9008 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/businessQueries.json @@ -0,0 +1,50 @@ +[ + { + "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' 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": {} + }, + { + "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 > 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": {} + }, + { + "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) > 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": {} + }, + { + "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) >= 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": {} + }, + { + "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)) >= 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": {} + }, + { + "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) > 0.3 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": {} + } +] \ No newline at end of file diff --git a/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor.log b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor.log new file mode 100644 index 0000000..7327bf8 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor.log @@ -0,0 +1,233 @@ +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 diff --git a/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor_daily.log b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor_daily.log new file mode 100644 index 0000000..1346525 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor_daily.log @@ -0,0 +1,109 @@ +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 diff --git a/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor_error.log b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/lzwcai_mcp_sqlexecutor_error.log new file mode 100644 index 0000000..e69de29 diff --git a/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/mcp_services.log b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/mcp_services.log new file mode 100644 index 0000000..7cca1e2 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/logs/mcp_services.log @@ -0,0 +1,71 @@ +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 diff --git a/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/main.py b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/main.py new file mode 100644 index 0000000..07f4694 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/main.py @@ -0,0 +1,373 @@ +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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/pyproject.toml b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/pyproject.toml new file mode 100644 index 0000000..c12ec43 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/pyproject.toml @@ -0,0 +1,35 @@ +[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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/sql11 b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/sql11 new file mode 100644 index 0000000..39b7319 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/sql11 @@ -0,0 +1,169 @@ +-- ===================================================== +-- 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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/__init__.py b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/__init__.py new file mode 100644 index 0000000..3df2b44 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/__init__.py @@ -0,0 +1,25 @@ +"""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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/api_client.py b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/api_client.py new file mode 100644 index 0000000..cf49a0a --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/api_client.py @@ -0,0 +1,318 @@ +""" +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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/env_config.py b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/env_config.py new file mode 100644 index 0000000..585b0a6 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/env_config.py @@ -0,0 +1,106 @@ +"""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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/json_helper.py b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/json_helper.py new file mode 100644 index 0000000..1a6fc95 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/json_helper.py @@ -0,0 +1,60 @@ +"""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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/logger_config.py b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/logger_config.py new file mode 100644 index 0000000..290964f --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/logger_config.py @@ -0,0 +1,489 @@ +# -*- 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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/name_helper.py b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/name_helper.py new file mode 100644 index 0000000..d66cf32 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/name_helper.py @@ -0,0 +1,41 @@ +# -*- 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_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/schema_helper.py b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/schema_helper.py new file mode 100644 index 0000000..626559f --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/lzwcai_mcpskills_analyzeOrder/utils/schema_helper.py @@ -0,0 +1,166 @@ +# -*- 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: + 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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_analyzeOrder/manufacturing_data_model_v1.0.0.md b/lzwcai_mcpskills_analyzeOrder/manufacturing_data_model_v1.0.0.md new file mode 100644 index 0000000..02b9ec4 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/manufacturing_data_model_v1.0.0.md @@ -0,0 +1,1094 @@ +# manufacturing_data_model_v1.0.0 + +## 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_analyzeOrder/pyproject.toml b/lzwcai_mcpskills_analyzeOrder/pyproject.toml new file mode 100644 index 0000000..0f9a2b3 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/pyproject.toml @@ -0,0 +1,35 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "lzwcai-mcpskills-analyzeOrder" +version = "0.1.12" +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-analyzeOrder = "lzwcai_mcpskills_analyzeOrder.main:main" + +[tool.hatch.build.targets.wheel] +packages = ["lzwcai_mcpskills_analyzeOrder"] + +[tool.hatch.build.targets.wheel.force-include] +"lzwcai_mcpskills_analyzeOrder/businessQueries.json" = "lzwcai_mcpskills_analyzeOrder/businessQueries.json" diff --git a/lzwcai_mcpskills_analyzeOrder/一页式决策简报.sql b/lzwcai_mcpskills_analyzeOrder/一页式决策简报.sql new file mode 100644 index 0000000..156d3f6 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/一页式决策简报.sql @@ -0,0 +1,317 @@ +-- ===================================================== +-- 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_analyzeOrder/人效产值损耗三维模型仪表盘.sql b/lzwcai_mcpskills_analyzeOrder/人效产值损耗三维模型仪表盘.sql new file mode 100644 index 0000000..b6f98b9 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/人效产值损耗三维模型仪表盘.sql @@ -0,0 +1,193 @@ +-- ===================================================== +-- 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_analyzeOrder/供应链风险预警.sql b/lzwcai_mcpskills_analyzeOrder/供应链风险预警.sql new file mode 100644 index 0000000..857c7ee --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/供应链风险预警.sql @@ -0,0 +1,416 @@ +-- ===================================================== +-- 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_analyzeOrder/工单执行进度与异常节点.sql b/lzwcai_mcpskills_analyzeOrder/工单执行进度与异常节点.sql new file mode 100644 index 0000000..f7d3770 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/工单执行进度与异常节点.sql @@ -0,0 +1,409 @@ +-- ===================================================== +-- 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_analyzeOrder/指标趋势分析与拐点预警.sql b/lzwcai_mcpskills_analyzeOrder/指标趋势分析与拐点预警.sql new file mode 100644 index 0000000..45f978d --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/指标趋势分析与拐点预警.sql @@ -0,0 +1,426 @@ +-- ===================================================== +-- 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_analyzeOrder/订单延迟预警分析.sql b/lzwcai_mcpskills_analyzeOrder/订单延迟预警分析.sql new file mode 100644 index 0000000..77fc5b5 --- /dev/null +++ b/lzwcai_mcpskills_analyzeOrder/订单延迟预警分析.sql @@ -0,0 +1,154 @@ +-- 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_analyzeWorkOrder/README.md b/lzwcai_mcpskills_analyzeWorkOrder/README.md index dd1d1dc..e4bc0ac 100644 --- a/lzwcai_mcpskills_analyzeWorkOrder/README.md +++ b/lzwcai_mcpskills_analyzeWorkOrder/README.md @@ -1,4 +1,4 @@ -# lzwcai-mcpskills-analyzeWorkOrder +# lzwcai-mcpskills-analyzeOrder 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。 @@ -14,7 +14,7 @@ ### 使用 pip 安装 ```bash -pip install lzwcai-mcpskills-analyzeWorkOrder +pip install lzwcai-mcpskills-analyzeOrder ``` ### 从源码安装 @@ -28,7 +28,7 @@ pip install -e . ### 使用 uv 安装（推荐） ```bash -uv pip install lzwcai-mcpskills-analyzeWorkOrder +uv pip install lzwcai-mcpskills-analyzeOrder ``` ## 使用方法 @@ -38,7 +38,7 @@ uv pip install lzwcai-mcpskills-analyzeWorkOrder 安装后，可以直接通过命令启动： ```bash -lzwcai-mcpskills-analyzeWorkOrder +lzwcai-mcpskills-analyzeOrder ``` ### 作为 Python 模块运行 @@ -55,7 +55,7 @@ python -m lzwcai_mcp_sqlexecutor.main { "mcpServers": { "lzwcai-sqlexecutor": { - "command": "lzwcai-mcpskills-analyzeWorkOrder" + "command": "lzwcai-mcpskills-analyzeOrder" } } } diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/__init__.py b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/__init__.py index 4798f49..3e52bf0 100644 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/__init__.py +++ b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/__init__.py @@ -1,5 +1,5 @@ """ -lzwcai-mcpskills-analyzeWorkOrder - MCP server for executing business SQL queries +lzwcai-mcpskills-analyzeOrder - MCP server for executing business SQL queries """ __version__ = "0.1.2" diff --git a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json index 2b30e05..a180e7e 100644 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json +++ b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json @@ -1,37 +1,4 @@ [ - { - "id": "2006300000000000000", - "businessName": "QueryList", - "businessDescription": "查询所有工单列表，返回fact_work_order表的全部数据", - "datasourceId": "19", - "sqlTemplate": "SELECT * FROM fact_work_order", - "parameters": {} - }, - { - "id": "2006300000000000004", - "businessName": "WorkOrderDetail", - "businessDescription": "根据工单编号查询工单详情，包括工单基本信息、产品信息、委外供应商、工艺路线等", - "datasourceId": "19", - "sqlTemplate": "SELECT wo.work_order_id, wo.work_order_number, wo.status, wo.planned_qty, wo.completed_qty, wo.operation_good_qty, wo.operation_bad_qty, wo.qc_good_qty, wo.qc_bad_qty, wo.source_doc_number, wo.event_time_utc, p.product_code, p.product_name, p.product_spec, s.supplier_name, r.routing_name FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = true LEFT JOIN dim_supplier s ON wo.supplier_id = s.supplier_id AND s.is_current = true LEFT JOIN dim_routing r ON wo.routing_id = r.routing_id AND r.is_current = true WHERE wo.work_order_number = '{workOrderNumber}'", - "parameters": { - "workOrderNumber": { - "type": "string", - "description": "工单编号", - "required": true, - "examples": [ - "WO1311343859" - ] - } - } - }, - { - "id": "2006300000000000005", - "businessName": "WorkOrderProgressDashboard", - "businessDescription": "工单执行进度与异常节点看板，展示工单完工进度、工序良率、质检良率、异常标识等关键监控指标", - "datasourceId": "19", - "sqlTemplate": "WITH work_order_progress AS (SELECT wo.work_order_number, wo.status AS work_order_status, wo.planned_qty, wo.completed_qty, wo.operation_good_qty, wo.operation_bad_qty, wo.qc_good_qty, wo.qc_bad_qty, wo.source_doc_number AS sales_order_number, wo.event_time_utc, p.product_code, p.product_name, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 2) ELSE 0 END AS completion_pct, CASE WHEN (wo.operation_good_qty + wo.operation_bad_qty) > 0 THEN ROUND(wo.operation_good_qty * 100.0 / (wo.operation_good_qty + wo.operation_bad_qty), 2) ELSE NULL END AS operation_yield_pct, CASE WHEN (wo.qc_good_qty + wo.qc_bad_qty) > 0 THEN ROUND(wo.qc_good_qty * 100.0 / (wo.qc_good_qty + wo.qc_bad_qty), 2) ELSE NULL END AS qc_yield_pct FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = true), task_summary AS (SELECT work_order_number, COUNT(*) AS total_tasks, SUM(CASE WHEN actual_end_time_utc IS NOT NULL THEN 1 ELSE 0 END) AS completed_tasks, SUM(CASE WHEN actual_start_time_utc IS NOT NULL AND actual_end_time_utc IS NULL THEN 1 ELSE 0 END) AS in_progress_tasks, SUM(CASE WHEN actual_start_time_utc IS NULL THEN 1 ELSE 0 END) AS pending_tasks, SUM(bad_qty) AS total_bad_qty FROM fact_operation_task GROUP BY work_order_number), labor_summary AS (SELECT work_order_number, SUM(report_qty) AS total_report_qty, SUM(good_qty) AS total_good_qty, SUM(bad_qty) AS total_bad_qty, SUM(duration_minutes) AS total_work_minutes FROM fact_labor_report GROUP BY work_order_number), qc_summary AS (SELECT work_order_number, COUNT(*) AS qc_record_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection WHERE work_order_number IS NOT NULL GROUP BY work_order_number) SELECT wp.work_order_number, wp.product_code, wp.product_name, wp.sales_order_number, wp.work_order_status, wp.planned_qty, wp.completed_qty, wp.completion_pct, COALESCE(ts.total_tasks, 0) AS total_tasks, COALESCE(ts.completed_tasks, 0) AS completed_tasks, COALESCE(ts.in_progress_tasks, 0) AS in_progress_tasks, COALESCE(ts.pending_tasks, 0) AS pending_tasks, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_work_minutes, 0) AS total_work_minutes, wp.operation_yield_pct, wp.qc_yield_pct, COALESCE(qc.total_pass_qty, 0) AS qc_pass_qty, COALESCE(qc.total_fail_qty, 0) AS qc_fail_qty, CASE WHEN wp.completion_pct < 50 AND wp.work_order_status = 'STARTED' THEN 'PROGRESS_DELAY' WHEN wp.operation_yield_pct < 90 THEN 'OPERATION_YIELD_ABNORMAL' WHEN wp.qc_yield_pct < 95 THEN 'QC_YIELD_ABNORMAL' WHEN ts.total_bad_qty > 0 THEN 'HAS_DEFECTS' ELSE 'NORMAL' END AS abnormal_flag, wp.event_time_utc AS last_update_time FROM work_order_progress wp LEFT JOIN task_summary ts ON wp.work_order_number = ts.work_order_number LEFT JOIN labor_summary ls ON wp.work_order_number = ls.work_order_number LEFT JOIN qc_summary qc ON wp.work_order_number = qc.work_order_number ORDER BY CASE wp.work_order_status WHEN 'STARTED' THEN 1 WHEN 'RELEASED' THEN 2 WHEN 'PLANNED' THEN 3 ELSE 4 END, wp.event_time_utc DESC", - "parameters": {} - }, { "id": "2006300000000000006", "businessName": "SalesOrderList", @@ -64,13 +31,5 @@ "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": {} - }, - { - "id": "2006300000000000009", - "businessName": "DepartmentEfficiencyDashboard", - "businessDescription": "部门人效产值损耗三维仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门汇总展示", - "datasourceId": "19", - "sqlTemplate": "WITH order_summary AS (SELECT COUNT(DISTINCT so.sales_order_id) AS total_order_count, SUM(COALESCE(so.deal_amount, 0)) AS total_order_amount FROM fact_sales_order so), labor_by_dept AS (SELECT p.department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(COALESCE(lr.duration_minutes, 0)) AS total_work_minutes, SUM(COALESCE(lr.report_qty, 0)) AS total_report_qty, SUM(COALESCE(lr.good_qty, 0)) AS total_good_qty, SUM(COALESCE(lr.bad_qty, 0)) AS total_bad_qty FROM fact_labor_report lr LEFT JOIN dim_person p ON lr.worker_name = p.person_name AND p.is_current = true GROUP BY p.department), work_order_output AS (SELECT p.department, SUM(COALESCE(wo.completed_qty, 0)) AS completed_qty, SUM(COALESCE(wo.operation_bad_qty, 0)) AS operation_bad_qty, SUM(COALESCE(wo.qc_bad_qty, 0)) AS qc_bad_qty, COUNT(DISTINCT wo.work_order_id) AS work_order_count FROM fact_work_order wo LEFT JOIN dim_person p ON wo.production_manager_id = p.person_id AND p.is_current = true GROUP BY p.department), scrap_by_dept AS (SELECT p.department, COUNT(*) AS scrap_count FROM fact_scrap s LEFT JOIN dim_person p ON s.operator_id = p.person_id AND p.is_current = true GROUP BY p.department), qc_by_dept AS (SELECT p.department, SUM(COALESCE(qi.pass_qty, 0)) AS qc_pass_qty, SUM(COALESCE(qi.fail_qty, 0)) AS qc_fail_qty FROM fact_quality_inspection qi LEFT JOIN dim_person p ON qi.inspector_id = p.person_id AND p.is_current = true GROUP BY p.department) SELECT COALESCE(ld.department, '未分配') AS department, os.total_order_count, os.total_order_amount, COALESCE(ld.worker_count, 0) AS worker_count, COALESCE(ld.total_work_minutes, 0) AS total_work_minutes, ROUND(COALESCE(ld.total_work_minutes, 0) / 60.0, 2) AS total_work_hours, COALESCE(ld.total_report_qty, 0) AS total_report_qty, COALESCE(ld.total_good_qty, 0) AS total_good_qty, COALESCE(ld.total_bad_qty, 0) AS labor_bad_qty, COALESCE(wo.completed_qty, 0) AS completed_qty, COALESCE(wo.work_order_count, 0) AS work_order_count, COALESCE(wo.operation_bad_qty, 0) AS operation_bad_qty, COALESCE(wo.qc_bad_qty, 0) AS qc_bad_qty, COALESCE(sc.scrap_count, 0) AS scrap_count, COALESCE(qc.qc_pass_qty, 0) AS qc_pass_qty, COALESCE(qc.qc_fail_qty, 0) AS qc_fail_qty, CASE WHEN COALESCE(ld.worker_count, 0) > 0 THEN ROUND(COALESCE(ld.total_report_qty, 0)::NUMERIC / ld.worker_count, 2) ELSE 0 END AS output_per_worker, CASE WHEN COALESCE(ld.total_work_minutes, 0) > 0 THEN ROUND(COALESCE(ld.total_report_qty, 0)::NUMERIC * 60 / ld.total_work_minutes, 2) ELSE 0 END AS output_per_hour, CASE WHEN COALESCE(ld.worker_count, 0) > 0 THEN ROUND(os.total_order_amount::NUMERIC / ld.worker_count, 2) ELSE 0 END AS order_value_per_worker, CASE WHEN (COALESCE(ld.total_good_qty, 0) + COALESCE(ld.total_bad_qty, 0)) > 0 THEN ROUND(COALESCE(ld.total_good_qty, 0)::NUMERIC * 100 / (ld.total_good_qty + ld.total_bad_qty), 2) ELSE 100 END AS labor_yield_pct, CASE WHEN (COALESCE(qc.qc_pass_qty, 0) + COALESCE(qc.qc_fail_qty, 0)) > 0 THEN ROUND(COALESCE(qc.qc_pass_qty, 0)::NUMERIC * 100 / (qc.qc_pass_qty + qc.qc_fail_qty), 2) ELSE 100 END AS qc_yield_pct, COALESCE(ld.total_bad_qty, 0) + COALESCE(wo.operation_bad_qty, 0) + COALESCE(wo.qc_bad_qty, 0) + COALESCE(qc.qc_fail_qty, 0) AS total_loss_qty, CASE WHEN COALESCE(ld.worker_count, 0) > 0 AND COALESCE(ld.total_work_minutes, 0) > 0 AND (COALESCE(ld.total_good_qty, 0) + COALESCE(ld.total_bad_qty, 0)) > 0 THEN CASE WHEN (COALESCE(ld.total_report_qty, 0)::NUMERIC / ld.worker_count) >= 100 AND (COALESCE(ld.total_report_qty, 0)::NUMERIC * 60 / ld.total_work_minutes) >= 10 AND (COALESCE(ld.total_good_qty, 0)::NUMERIC * 100 / (ld.total_good_qty + ld.total_bad_qty)) >= 95 THEN 'EXCELLENT' WHEN (COALESCE(ld.total_report_qty, 0)::NUMERIC / ld.worker_count) >= 50 AND (COALESCE(ld.total_report_qty, 0)::NUMERIC * 60 / ld.total_work_minutes) >= 5 AND (COALESCE(ld.total_good_qty, 0)::NUMERIC * 100 / (ld.total_good_qty + ld.total_bad_qty)) >= 90 THEN 'GOOD' ELSE 'NEEDS_IMPROVEMENT' END ELSE 'NO_DATA' END AS efficiency_rating FROM labor_by_dept ld CROSS JOIN order_summary os LEFT JOIN work_order_output wo ON ld.department = wo.department LEFT JOIN scrap_by_dept sc ON ld.department = sc.department LEFT JOIN qc_by_dept qc ON ld.department = qc.department ORDER BY total_report_qty 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 index 0607ec1..07f4694 100644 --- a/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/main.py +++ b/lzwcai_mcpskills_analyzeWorkOrder/lzwcai_mcpskills_analyzeWorkOrder/main.py @@ -94,7 +94,7 @@ def generate_tool_schema_from_query(query: dict) -> types.Tool: # 创建 MCP 服务器实例 -server = Server("lzwcai-mcpskills-analyzeWorkOrder") +server = Server("lzwcai-mcpskills-analyzeOrder") # 缓存查询配置，避免重复加载 _queries_cache = None @@ -311,7 +311,7 @@ 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-analyzeWorkOrder") + 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) @@ -332,7 +332,7 @@ async def async_main(): read_stream, write_stream, InitializationOptions( - server_name="lzwcai-mcpskills-analyzeWorkOrder", + server_name="lzwcai-mcpskills-analyzeOrder", server_version="0.1.0", capabilities=server.get_capabilities( notification_options=NotificationOptions(), diff --git a/lzwcai_mcpskills_analyzeWorkOrder/main.py b/lzwcai_mcpskills_analyzeWorkOrder/main.py index 5b16dc8..015f7d0 100644 --- a/lzwcai_mcpskills_analyzeWorkOrder/main.py +++ b/lzwcai_mcpskills_analyzeWorkOrder/main.py @@ -1,5 +1,5 @@ """ -Entry point for lzwcai-mcpskills-analyzeWorkOrder +Entry point for lzwcai-mcpskills-analyzeOrder Runs the MCP server for SQL query execution """ import os @@ -9,5 +9,5 @@ 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_analyzeWorkOrder.main import main + from lzwcai_mcpskills_analyzeOrder.main import main main() diff --git a/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml b/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml index 06bad03..0f9a2b3 100644 --- a/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml +++ b/lzwcai_mcpskills_analyzeWorkOrder/pyproject.toml @@ -3,7 +3,7 @@ requires = ["hatchling"] build-backend = "hatchling.build" [project] -name = "lzwcai-mcpskills-analyzeWorkOrder" +name = "lzwcai-mcpskills-analyzeOrder" version = "0.1.12" description = "MCP server for executing business SQL queries with dynamic tool generation" readme = "README.md" @@ -26,10 +26,10 @@ dependencies = [ ] [project.scripts] -lzwcai-mcpskills-analyzeWorkOrder = "lzwcai_mcpskills_analyzeWorkOrder.main:main" +lzwcai-mcpskills-analyzeOrder = "lzwcai_mcpskills_analyzeOrder.main:main" [tool.hatch.build.targets.wheel] -packages = ["lzwcai_mcpskills_analyzeWorkOrder"] +packages = ["lzwcai_mcpskills_analyzeOrder"] [tool.hatch.build.targets.wheel.force-include] -"lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json" = "lzwcai_mcpskills_analyzeWorkOrder/businessQueries.json" +"lzwcai_mcpskills_analyzeOrder/businessQueries.json" = "lzwcai_mcpskills_analyzeOrder/businessQueries.json" diff --git a/server.log b/server.log new file mode 100644 index 0000000..a6cda93 --- /dev/null +++ b/server.log @@ -0,0 +1,6 @@ +{"level":30,"time":1767795873541,"pid":31816,"hostname":"MICROSO-UUDM3QK","msg":"config>{\"host\":\"192.168.11.24\",\"user\":\"postgres\",\"password\":\"******\",\"database\":\"manufacturing_data_model_v1_0_0\",\"port\":5432}"} +{"level":30,"time":1767795873541,"pid":31816,"hostname":"MICROSO-UUDM3QK","msg":"MySQL MCP server running on stdio"} +{"level":50,"time":1767796119331,"pid":31816,"hostname":"MICROSO-UUDM3QK","msg":"List tables failed","sql":"SHOW TABLES","error":"connect ETIMEDOUT"} +{"level":50,"time":1767797249666,"pid":31816,"hostname":"MICROSO-UUDM3QK","msg":"Describe table failed","sql":"DESCRIBE ??","params":["fact_work_order"],"error":"connect ETIMEDOUT"} +{"level":50,"time":1767797265187,"pid":31816,"hostname":"MICROSO-UUDM3QK","msg":"Describe table failed","sql":"DESCRIBE ??","params":["fact_sales_order"],"error":"connect ETIMEDOUT"} +{"level":50,"time":1767797277193,"pid":31816,"hostname":"MICROSO-UUDM3QK","msg":"Describe table failed","sql":"DESCRIBE ??","params":["fact_sales_shipment"],"error":"connect ETIMEDOUT"} diff --git a/sql/dim_bom.json b/sql/dim_bom.json new file mode 100644 index 0000000..fbcc20f --- /dev/null +++ b/sql/dim_bom.json @@ -0,0 +1,342 @@ +[ + { + "bom_id": 1, + "bom_number": "BOM67148266", + "bom_topic": "BOM Topic 0", + "is_primary": "f", + "level_number": 1, + "product_code": "4904724113571", + "unit_usage_qty": 6.77, + "uom_code": "pcs", + "supplier_name": null, + "remark": "谢谢有限运行大小希望什么女人.", + "source_system": null, + "row_valid_from": "3/12/2025 04:38:26.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 2, + "bom_number": "BOM41843358", + "bom_topic": "BOM Topic 1", + "is_primary": "f", + "level_number": 3, + "product_code": "7404817071704", + "unit_usage_qty": 3.15, + "uom_code": "pcs", + "supplier_name": null, + "remark": "联系时候首页功能到了人民包括方面.", + "source_system": null, + "row_valid_from": "18/12/2025 13:59:24.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 3, + "bom_number": "BOM98209951", + "bom_topic": "BOM Topic 2", + "is_primary": "f", + "level_number": 2, + "product_code": "2371469356556", + "unit_usage_qty": 5.85, + "uom_code": "pcs", + "supplier_name": null, + "remark": "一种感觉只是都是程序上海.", + "source_system": null, + "row_valid_from": "19/12/2025 21:41:57.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 4, + "bom_number": "BOM54567090", + "bom_topic": "BOM Topic 3", + "is_primary": "t", + "level_number": 2, + "product_code": "7246071817298", + "unit_usage_qty": 8.79, + "uom_code": "pcs", + "supplier_name": null, + "remark": "市场会员或者是一可是.", + "source_system": null, + "row_valid_from": "11/12/2025 15:22:25.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 5, + "bom_number": "BOM15469037", + "bom_topic": "BOM Topic 4", + "is_primary": "f", + "level_number": 1, + "product_code": "7756777017088", + "unit_usage_qty": 6.14, + "uom_code": "pcs", + "supplier_name": null, + "remark": "说明电脑工作但是语言组织.", + "source_system": null, + "row_valid_from": "8/12/2025 09:21:49.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 6, + "bom_number": "BOM38789955", + "bom_topic": "BOM Topic 5", + "is_primary": "f", + "level_number": 2, + "product_code": "2173642523324", + "unit_usage_qty": 4.84, + "uom_code": "pcs", + "supplier_name": null, + "remark": "次数安全产品的话空间其他.", + "source_system": null, + "row_valid_from": "18/12/2025 01:14:37.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 7, + "bom_number": "BOM44892173", + "bom_topic": "BOM Topic 6", + "is_primary": "f", + "level_number": 2, + "product_code": "9233722353722", + "unit_usage_qty": 1.57, + "uom_code": "pcs", + "supplier_name": null, + "remark": "功能学校产品应用大小原因然后.", + "source_system": null, + "row_valid_from": "21/12/2025 09:40:17.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + 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"supplier_name": null, + "remark": "中国方式电脑情况使用以及那些积分.", + "source_system": null, + "row_valid_from": "30/11/2025 05:27:42.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 15, + "bom_number": "BOM93108369", + "bom_topic": "BOM Topic 14", + "is_primary": "f", + "level_number": 3, + "product_code": "5567459110483", + "unit_usage_qty": 3.52, + "uom_code": "pcs", + "supplier_name": null, + "remark": "直接企业发展一点.", + "source_system": null, + "row_valid_from": "2/12/2025 08:37:40.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 16, + "bom_number": "BOM11354551", + "bom_topic": "BOM Topic 15", + "is_primary": "f", + "level_number": 1, + "product_code": "4219806010724", + "unit_usage_qty": 5.7, + "uom_code": "pcs", + "supplier_name": null, + "remark": "法律使用同时工作名称应该.", + "source_system": null, + "row_valid_from": "8/12/2025 15:00:03.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 17, + "bom_number": "BOM81835936", + "bom_topic": "BOM Topic 16", + "is_primary": "f", + "level_number": 1, + "product_code": "6664882480851", + "unit_usage_qty": 1.75, + "uom_code": "pcs", + "supplier_name": null, + "remark": "而且音乐大学是否本站国内.", + "source_system": null, + "row_valid_from": "1/12/2025 19:09:50.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 18, + "bom_number": "BOM50885655", + "bom_topic": "BOM Topic 17", + "is_primary": "t", + "level_number": 2, + "product_code": "8442282024091", + "unit_usage_qty": 5.72, + "uom_code": "pcs", + "supplier_name": null, + "remark": "设备设备政府到了时间.", + "source_system": null, + "row_valid_from": "7/12/2025 14:54:51.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 19, + "bom_number": "BOM33362097", + "bom_topic": "BOM Topic 18", + "is_primary": "f", + "level_number": 2, + "product_code": "9499708203973", + "unit_usage_qty": 9.05, + "uom_code": "pcs", + "supplier_name": null, + "remark": "不同然后当前或者其实城市.", + "source_system": null, + "row_valid_from": "13/12/2025 17:05:21.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "bom_id": 20, + "bom_number": "BOM26137367", + "bom_topic": "BOM Topic 19", + "is_primary": "t", + "level_number": 3, + "product_code": "6791611168077", + "unit_usage_qty": 5.75, + "uom_code": "pcs", + "supplier_name": null, + "remark": "方面数据那么免费.", + "source_system": null, + "row_valid_from": "25/12/2025 05:34:17.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + } +] \ No newline at 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19:16:27.-1f+00" + }, + { + "customer_id": 3, + "customer_name": "海创网络有限公司", + "customer_source": "线下", + "phone": "18957536746", + "telephone": null, + "email": "zzhang@example.com", + "customer_level": null, + "industry": null, + "address": "海南省莉市海陵彭路l座 290465", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "30/11/2025 18:29:01.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "customer_id": 4, + "customer_name": "菊风公司信息有限公司", + "customer_source": "线下", + "phone": "13504075265", + "telephone": null, + "email": "xia54@example.com", + "customer_level": null, + "industry": null, + "address": "辽宁省邯郸市淄川兴城路p座 565507", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "20/12/2025 06:40:06.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "customer_id": 5, + "customer_name": "盟新科技有限公司", + "customer_source": "线下", + "phone": "15814141145", + "telephone": null, + "email": "momin@example.com", + "customer_level": null, + "industry": null, + "address": "四川省太原市崇文乌鲁木齐街c座 701509", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "17/12/2025 08:45:29.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "customer_id": 6, + "customer_name": "四通传媒有限公司", + "customer_source": "线下", + "phone": "13715663646", + "telephone": null, + "email": "taosun@example.com", + "customer_level": null, + "industry": null, + "address": "贵州省桂荣市魏都尹路N座 263707", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "20/12/2025 17:27:20.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "customer_id": 7, + "customer_name": "飞海科技传媒有限公司", + "customer_source": "线下", + "phone": "13938217533", + "telephone": null, + "email": "jingzou@example.com", + "customer_level": null, + "industry": null, + "address": "河南省台北市大兴张街O座 373438", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "5/12/2025 01:40:19.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "customer_id": 8, + "customer_name": "易动力科技有限公司", + "customer_source": "线上", + "phone": "13872486621", + "telephone": null, + "email": "oqian@example.com", + "customer_level": null, + "industry": null, + "address": "河南省阜新市合川卢路o座 600090", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "11/12/2025 11:41:23.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "customer_id": 9, + "customer_name": "MBP软件网络有限公司", + "customer_source": "线上", + "phone": "15684801530", + "telephone": null, + "email": "qiufang@example.net", + "customer_level": null, + "industry": null, + "address": "福建省澳门市城东陈路K座 792594", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "29/11/2025 22:23:16.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "customer_id": 10, + "customer_name": "鑫博腾飞信息有限公司", + "customer_source": "线上", + "phone": "18503391590", + "telephone": null, + "email": "lixia@example.org", + "customer_level": null, + "industry": null, + "address": "广东省六安县长寿丁街f座 479607", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "11/12/2025 15:03:47.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "customer_id": 11, + "customer_name": "同兴万点传媒有限公司", + "customer_source": "推荐", + "phone": "15005909274", + "telephone": null, + "email": "ming77@example.net", + "customer_level": null, + "industry": null, + "address": "上海市畅县黄浦潮州路Z座 168305", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "27/11/2025 06:37:01.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "customer_id": 12, + "customer_name": "浦华众城信息有限公司", + "customer_source": "推荐", + "phone": "13436637029", + "telephone": null, + "email": "qiaofang@example.com", + "customer_level": null, + "industry": null, + "address": "澳门特别行政区梧州市大兴潮州街H座 920472", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "23/12/2025 17:30:53.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "customer_id": 13, + "customer_name": "华成育卓信息有限公司", + "customer_source": "线上", + "phone": "18668177887", + "telephone": null, + "email": "guotao@example.com", + "customer_level": null, + "industry": null, + "address": "山西省玉梅市怀柔胡路a座 437143", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "21/12/2025 00:30:52.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "customer_id": 14, + "customer_name": "双敏电子网络有限公司", + "customer_source": "线上", + "phone": "13556032093", + "telephone": null, + "email": "mingong@example.com", + "customer_level": null, + "industry": null, + "address": "山西省阜新县滨城何路o座 127755", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "8/12/2025 01:41:03.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "customer_id": 15, + "customer_name": "华成育卓网络有限公司", + "customer_source": "线上", + "phone": "18853534189", + "telephone": null, + "email": "qtan@example.org", + "customer_level": null, + "industry": null, + "address": "云南省林市淄川陈街m座 800035", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "30/11/2025 07:30:18.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "customer_id": 16, + "customer_name": "信诚致远网络有限公司", + "customer_source": "线上", + "phone": "18851352720", + "telephone": null, + "email": "dren@example.net", + "customer_level": null, + "industry": null, + "address": "山东省广州市璧山赵路U座 774845", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "18/12/2025 05:06:12.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "customer_id": 17, + "customer_name": "毕博诚传媒有限公司", + "customer_source": "线下", + "phone": "18865747212", + "telephone": null, + "email": "pinggao@example.com", + "customer_level": null, + "industry": null, + "address": "河北省北镇市魏都哈尔滨路N座 310652", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "11/12/2025 18:21:02.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "customer_id": 18, + "customer_name": "合联电子科技有限公司", + "customer_source": "推荐", + "phone": "13526318565", + "telephone": null, + "email": "guiyingwei@example.org", + "customer_level": null, + "industry": null, + "address": "内蒙古自治区静县普陀南京街W座 344461", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "23/12/2025 00:02:08.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "customer_id": 19, + "customer_name": "诺依曼软件科技有限公司", + "customer_source": "线下", + "phone": "13356911559", + "telephone": null, + "email": "minxue@example.net", + "customer_level": null, + "industry": null, + "address": "新疆维吾尔自治区阜新市东城邯郸街O座 159144", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "2/12/2025 05:55:01.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "customer_id": 20, + "customer_name": "襄樊地球村科技有限公司", + "customer_source": "推荐", + "phone": "18249985760", + "telephone": null, + "email": "jhan@example.net", + "customer_level": null, + "industry": null, + "address": "重庆市昆明市高港张路U座 962119", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "3/12/2025 01:41:25.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "customer_id": 21, + "customer_name": "新宇龙信息传媒有限公司", + "customer_source": "线下", + "phone": "15988906811", + "telephone": null, + "email": "mochao@example.com", + "customer_level": null, + "industry": null, + "address": "海南省玉珍市永川袁街r座 604856", + "is_locked": null, + "deal_status": null, + "last_follow_up_time_utc": null, + "source_system": null, + "row_valid_from": "22/12/2025 18:33:48.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + } +] \ No newline at end of file diff --git a/sql/dim_equipment.json b/sql/dim_equipment.json new file mode 100644 index 0000000..68e6d22 --- /dev/null +++ b/sql/dim_equipment.json @@ -0,0 +1,102 @@ +[ + { + "equipment_id": 1, + "equipment_number": "EQ4092", + "equipment_name": "设备 0", + "source_system": null, + "row_valid_from": "13/12/2025 11:51:05.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "equipment_id": 2, + "equipment_number": "EQ2399", + "equipment_name": "设备 1", + "source_system": null, + "row_valid_from": "26/11/2025 19:49:01.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "equipment_id": 3, + "equipment_number": "EQ4938", + "equipment_name": "设备 2", + "source_system": null, + "row_valid_from": "24/12/2025 01:48:18.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "equipment_id": 4, + "equipment_number": "EQ2339", + "equipment_name": "设备 3", + "source_system": null, + "row_valid_from": "12/12/2025 05:56:50.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "equipment_id": 5, + "equipment_number": "EQ9168", + "equipment_name": "设备 4", + "source_system": null, + "row_valid_from": "18/12/2025 03:40:39.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "equipment_id": 6, + "equipment_number": "EQ79", + "equipment_name": "设备 5", + "source_system": null, + "row_valid_from": "13/12/2025 11:39:15.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "equipment_id": 7, + "equipment_number": "EQ7709", + "equipment_name": "设备 6", + "source_system": null, + "row_valid_from": "25/12/2025 05:33:52.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "equipment_id": 8, + "equipment_number": "EQ267", + "equipment_name": "设备 7", + "source_system": null, + "row_valid_from": "19/12/2025 10:48:53.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "equipment_id": 9, + "equipment_number": "EQ8412", + "equipment_name": "设备 8", + "source_system": null, + "row_valid_from": "17/12/2025 23:27:19.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "equipment_id": 10, + "equipment_number": "EQ6494", + "equipment_name": "设备 9", + "source_system": null, + "row_valid_from": "16/12/2025 06:33:13.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + } +] \ No newline at end of file diff --git a/sql/dim_operation.json b/sql/dim_operation.json new file mode 100644 index 0000000..eed78a6 --- /dev/null +++ b/sql/dim_operation.json @@ -0,0 +1,122 @@ +[ + { + "operation_id": 1, + "operation_number": "OP0", + "operation_name": "组装", + "report_permission": "ALL", + "report_ratio": 1.0, + "source_system": null, + "row_valid_from": "12/12/2025 21:08:07.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "operation_id": 2, + "operation_number": "OP10", + "operation_name": "测试", + "report_permission": "ALL", + "report_ratio": 1.0, + "source_system": null, + "row_valid_from": "12/12/2025 18:31:51.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "operation_id": 3, + "operation_number": "OP20", + "operation_name": "测试", + "report_permission": "ALL", + "report_ratio": 1.0, + "source_system": null, + "row_valid_from": "6/12/2025 22:57:16.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "operation_id": 4, + "operation_number": "OP30", + "operation_name": "焊接", + "report_permission": "ALL", + "report_ratio": 1.0, + "source_system": null, + "row_valid_from": "28/11/2025 18:46:36.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "operation_id": 5, + "operation_number": "OP40", + "operation_name": "切割", + "report_permission": "ALL", + "report_ratio": 1.0, + "source_system": null, + "row_valid_from": "22/12/2025 05:31:31.-1f+00", + "row_valid_to": "31/12/9999 15:59:59+00", + "is_current": "t", + "last_updated_utc": "26/12/2025 05:36:09.-1f+00" + }, + { + "operation_id": 6, + "operation_number": "OP50", + "operation_name": "组装", + "report_permission": "ALL", + 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05:36:11.-1f+00" + }, + { + "inventory_count_id": 8, + "count_number": "CNT25970731", + "warehouse_name": null, + "count_name": null, + "count_time_utc": "20/12/2025 16:00:00+00", + "event_time_utc": "26/12/2025 05:36:11.-1f+00", + "source_system": null, + "last_updated_utc": "26/12/2025 05:36:11.-1f+00" + }, + { + "inventory_count_id": 9, + "count_number": "CNT96428082", + "warehouse_name": null, + "count_name": null, + "count_time_utc": "5/9/2025 16:00:00+00", + "event_time_utc": "26/12/2025 05:36:11.-1f+00", + "source_system": null, + "last_updated_utc": "26/12/2025 05:36:11.-1f+00" + }, + { + "inventory_count_id": 10, + "count_number": "CNT32858501", + "warehouse_name": null, + "count_name": null, + "count_time_utc": "1/6/2025 16:00:00+00", + "event_time_utc": "26/12/2025 05:36:11.-1f+00", + "source_system": null, + "last_updated_utc": "26/12/2025 05:36:11.-1f+00" + } +] \ No newline at end of file diff --git a/sql/fact_inventory_movement.json b/sql/fact_inventory_movement.json new file mode 100644 index 0000000..979d492 --- /dev/null +++ b/sql/fact_inventory_movement.json @@ -0,0 +1,4002 @@ +[ + { + "inventory_movement_id": 3, + "event_time_utc": "4/12/2025 00:19:46.-1f+00", + "product_id": 11, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV402995443", + "doc_type": "IN", + "movement_qty": 43.5, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 4, + "event_time_utc": "5/12/2025 12:47:27.-1f+00", + "product_id": 17, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV2121838375", + "doc_type": "IN", + "movement_qty": 88.89, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + 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"MOV739514373", + "doc_type": "OUT", + "movement_qty": 49.7, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 35, + "event_time_utc": "14/12/2025 01:39:08.-1f+00", + "product_id": 4, + "warehouse_id": 2, + "bom_id": null, + "doc_number": "MOV9205731440", + "doc_type": "IN", + "movement_qty": -16.07, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 36, + "event_time_utc": "12/12/2025 18:16:24.-1f+00", + "product_id": 20, + "warehouse_id": 2, + "bom_id": null, + 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null, + "doc_number": "MOV7138943656", + "doc_type": "IN", + "movement_qty": 35.65, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 39, + "event_time_utc": "8/12/2025 08:09:53.-1f+00", + "product_id": 1, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV8581258787", + "doc_type": "OUT", + "movement_qty": -66.52, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 40, + "event_time_utc": "7/12/2025 00:54:19.-1f+00", + "product_id": 14, + "warehouse_id": 2, + 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20:41:25.-1f+00", + "product_id": 17, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV8094258572", + "doc_type": "OUT", + "movement_qty": -10.8, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 47, + "event_time_utc": "18/12/2025 11:42:56.-1f+00", + "product_id": 5, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV3011817410", + "doc_type": "OUT", + "movement_qty": 73.93, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 48, + "event_time_utc": "19/12/2025 18:10:51.-1f+00", + "product_id": 18, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV9619364533", + "doc_type": "IN", + "movement_qty": -16.45, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 49, + "event_time_utc": "16/12/2025 14:24:35.-1f+00", + "product_id": 9, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV9405562690", + "doc_type": "IN", + "movement_qty": 36.85, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 50, + 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19:16:27.-1f+00" + }, + { + "inventory_movement_id": 56, + "event_time_utc": "12/12/2025 09:24:35.-1f+00", + "product_id": 15, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV6673862551", + "doc_type": "IN", + "movement_qty": 61.45, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 57, + "event_time_utc": "20/12/2025 19:48:20.-1f+00", + "product_id": 17, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV9923688251", + "doc_type": "IN", + "movement_qty": -41.25, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": 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"MOV2853128010", + "doc_type": "IN", + "movement_qty": 82.86, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 98, + "event_time_utc": "20/12/2025 19:49:08.-1f+00", + "product_id": 19, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV2170334925", + "doc_type": "IN", + "movement_qty": -68.83, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 99, + "event_time_utc": "15/12/2025 10:29:32.-1f+00", + "product_id": 10, + "warehouse_id": 4, + "bom_id": null, + 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"bom_id": null, + "doc_number": "MOV976304440", + "doc_type": "OUT", + "movement_qty": 52.49, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 102, + "event_time_utc": "16/12/2025 05:33:02.-1f+00", + "product_id": 17, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV166126828", + "doc_type": "OUT", + "movement_qty": 72.27, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "25/12/2025 19:16:27.-1f+00" + }, + { + "inventory_movement_id": 103, + "event_time_utc": "12/12/2025 11:48:52.-1f+00", + "product_id": 6, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV1765553923", + "doc_type": "IN", + "movement_qty": -20.71, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 104, + "event_time_utc": "6/12/2025 14:33:30.-1f+00", + "product_id": 19, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV6804525846", + "doc_type": "OUT", + "movement_qty": -81.07, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 105, + "event_time_utc": "8/12/2025 22:12:42.-1f+00", + "product_id": 8, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV8789591467", + "doc_type": "IN", + "movement_qty": -5.1, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 106, + "event_time_utc": "11/12/2025 09:18:31.-1f+00", + "product_id": 12, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV4513544935", + "doc_type": "IN", + "movement_qty": 83.36, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 107, + "event_time_utc": "12/12/2025 07:04:30.-1f+00", + "product_id": 20, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV5116085025", + "doc_type": "OUT", + "movement_qty": -84.91, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 108, + "event_time_utc": "17/12/2025 14:59:13.-1f+00", + "product_id": 6, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV3611750545", + "doc_type": "IN", + "movement_qty": 25.49, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 109, + "event_time_utc": "9/12/2025 15:46:37.-1f+00", + "product_id": 6, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV4282405379", + "doc_type": "OUT", + "movement_qty": -98.59, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 110, + "event_time_utc": "8/12/2025 13:52:15.-1f+00", + "product_id": 5, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV8651209937", + "doc_type": "OUT", + "movement_qty": -22.25, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 111, + "event_time_utc": "22/12/2025 12:58:11.-1f+00", + "product_id": 4, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV9873350949", + "doc_type": "IN", + "movement_qty": -72.56, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 112, + "event_time_utc": "16/12/2025 08:20:34.-1f+00", + "product_id": 4, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV6660743118", + "doc_type": "IN", + "movement_qty": -92.83, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 113, + "event_time_utc": "6/12/2025 10:41:54.-1f+00", + "product_id": 3, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV41662701", + "doc_type": "OUT", + "movement_qty": 37.97, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 114, + "event_time_utc": "30/11/2025 16:26:20.-1f+00", + "product_id": 6, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV2212087021", + "doc_type": "OUT", + "movement_qty": -2.75, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, 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05:36:10.-1f+00" + }, + { + "inventory_movement_id": 117, + "event_time_utc": "26/12/2025 01:54:36.-1f+00", + "product_id": 8, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV2882308361", + "doc_type": "IN", + "movement_qty": -99.23, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 118, + "event_time_utc": "1/12/2025 13:16:40.-1f+00", + "product_id": 9, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV8881271984", + "doc_type": "IN", + "movement_qty": 49.96, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 119, + "event_time_utc": "22/12/2025 20:07:41.-1f+00", + "product_id": 1, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV5381972035", + "doc_type": "OUT", + "movement_qty": 84.22, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 120, + "event_time_utc": "3/12/2025 21:38:51.-1f+00", + "product_id": 20, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV4187995691", + "doc_type": "OUT", + "movement_qty": 15.63, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 121, + "event_time_utc": "24/12/2025 10:42:59.-1f+00", + "product_id": 18, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV1515567469", + "doc_type": "IN", + "movement_qty": 38.36, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 122, + "event_time_utc": "24/12/2025 05:13:01.-1f+00", + "product_id": 9, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV6055865684", + "doc_type": "OUT", + "movement_qty": -7.2, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 123, + "event_time_utc": "6/12/2025 14:01:08.-1f+00", + "product_id": 16, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV8765064646", + "doc_type": "IN", + "movement_qty": 58.16, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 124, + "event_time_utc": "17/12/2025 18:43:44.-1f+00", + "product_id": 10, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV7582771173", + "doc_type": "OUT", + "movement_qty": -41.3, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 125, + "event_time_utc": "12/12/2025 08:02:51.-1f+00", + "product_id": 10, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV9279184136", + "doc_type": "IN", + "movement_qty": 7.99, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 126, + "event_time_utc": "14/12/2025 06:10:08.-1f+00", + "product_id": 7, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV5946606508", + "doc_type": "OUT", + "movement_qty": 56.28, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 127, + "event_time_utc": "25/12/2025 08:01:46.-1f+00", + "product_id": 6, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV2692990993", + "doc_type": "OUT", + "movement_qty": -55.19, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 128, + "event_time_utc": "12/12/2025 10:42:30.-1f+00", + "product_id": 14, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV1407517132", + "doc_type": "IN", + "movement_qty": 61.76, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + 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"discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 135, + "event_time_utc": "23/12/2025 14:10:44.-1f+00", + "product_id": 10, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV7887614131", + "doc_type": "IN", + "movement_qty": -60.87, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 136, + "event_time_utc": "9/12/2025 04:18:59.-1f+00", + "product_id": 1, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV1711589555", + "doc_type": "OUT", + "movement_qty": -92.95, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 137, + "event_time_utc": "24/12/2025 02:13:24.-1f+00", + "product_id": 10, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV2104276735", + "doc_type": "IN", + "movement_qty": 23.23, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 138, + "event_time_utc": "9/12/2025 12:18:48.-1f+00", + "product_id": 19, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV5306036530", + "doc_type": "OUT", + "movement_qty": 24.92, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 139, + "event_time_utc": "10/12/2025 19:22:52.-1f+00", + "product_id": 3, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV8341928208", + "doc_type": "IN", + "movement_qty": 69.59, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 140, + "event_time_utc": "7/12/2025 03:27:54.-1f+00", + "product_id": 9, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV4518076818", + "doc_type": "IN", + "movement_qty": -57.24, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 141, + "event_time_utc": "21/12/2025 23:47:54.-1f+00", + "product_id": 10, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV8568128251", + "doc_type": "OUT", + "movement_qty": 48.64, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 142, + "event_time_utc": "6/12/2025 05:53:19.-1f+00", + "product_id": 13, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV8368495793", + "doc_type": "OUT", + "movement_qty": 77.06, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 143, + "event_time_utc": "11/12/2025 09:29:44.-1f+00", + "product_id": 14, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV9792362562", + "doc_type": "OUT", + "movement_qty": -97.33, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 144, + "event_time_utc": "3/12/2025 13:56:22.-1f+00", + "product_id": 7, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV9191728174", + "doc_type": "OUT", + "movement_qty": -37.8, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 145, + "event_time_utc": "30/11/2025 23:02:48.-1f+00", + "product_id": 12, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV1360200357", + "doc_type": "IN", + "movement_qty": 79.11, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 146, + "event_time_utc": "7/12/2025 02:28:07.-1f+00", + "product_id": 16, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV5360459804", + "doc_type": "IN", + "movement_qty": -51.46, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 147, + "event_time_utc": "7/12/2025 13:19:06.-1f+00", + "product_id": 16, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV8642178133", + "doc_type": "IN", + "movement_qty": -42.2, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 148, + "event_time_utc": "22/12/2025 06:31:24.-1f+00", + "product_id": 18, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV5145596323", + "doc_type": "OUT", + "movement_qty": -8.92, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 149, + "event_time_utc": "13/12/2025 09:35:35.-1f+00", + "product_id": 2, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV764845171", + "doc_type": "OUT", + "movement_qty": -1.2, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 150, + "event_time_utc": "9/12/2025 14:48:24.-1f+00", + "product_id": 19, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV7226436257", + "doc_type": "IN", + "movement_qty": 27.21, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 151, + "event_time_utc": "18/12/2025 11:20:31.-1f+00", + "product_id": 5, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV597995534", + "doc_type": "IN", + "movement_qty": 47.66, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 152, + "event_time_utc": "13/12/2025 23:38:01.-1f+00", + "product_id": 19, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV2652832681", + "doc_type": "OUT", + "movement_qty": -94.49, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 153, + "event_time_utc": "14/12/2025 00:52:57.-1f+00", + "product_id": 7, + "warehouse_id": 2, + "bom_id": null, + "doc_number": "MOV2352419595", + "doc_type": "OUT", + "movement_qty": 71.46, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 154, + "event_time_utc": "8/12/2025 15:40:03.-1f+00", + "product_id": 10, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV6724278170", + "doc_type": "OUT", + "movement_qty": 21.16, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 155, + "event_time_utc": "16/12/2025 18:33:41.-1f+00", + "product_id": 7, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV4663318509", + "doc_type": "IN", + "movement_qty": -61.42, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 156, + "event_time_utc": "16/12/2025 18:17:42.-1f+00", + "product_id": 18, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV3023794803", + "doc_type": "OUT", + "movement_qty": -22.89, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 157, + "event_time_utc": "14/12/2025 04:45:18.-1f+00", + "product_id": 3, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV8320984454", + "doc_type": "IN", + "movement_qty": 84.55, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 158, + "event_time_utc": "18/12/2025 05:01:13.-1f+00", + "product_id": 13, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV3034687286", + "doc_type": "IN", + "movement_qty": 39.28, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 159, + "event_time_utc": "1/12/2025 02:49:36.-1f+00", + "product_id": 13, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV5471222983", + "doc_type": "IN", + "movement_qty": 21.48, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 160, + "event_time_utc": "1/12/2025 09:45:59.-1f+00", + "product_id": 10, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV5542908794", + "doc_type": "IN", + "movement_qty": -5.91, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 161, + "event_time_utc": "11/12/2025 22:05:28.-1f+00", + "product_id": 3, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV2214868913", + "doc_type": "OUT", + "movement_qty": -92.49, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 162, + "event_time_utc": "22/12/2025 21:20:25.-1f+00", + "product_id": 5, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV2564635760", + "doc_type": "IN", + "movement_qty": -31.83, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 163, + "event_time_utc": "23/12/2025 23:35:15.-1f+00", + "product_id": 2, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV9231845919", + "doc_type": "IN", + "movement_qty": -23.32, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 164, + "event_time_utc": "8/12/2025 06:38:03.-1f+00", + "product_id": 14, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV8938309141", + "doc_type": "IN", + "movement_qty": 54.71, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 165, + "event_time_utc": "23/12/2025 19:43:21.-1f+00", + "product_id": 19, + "warehouse_id": 4, + "bom_id": null, + "doc_number": "MOV7096234894", + "doc_type": "OUT", + "movement_qty": -4.48, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 166, + "event_time_utc": "7/12/2025 19:00:12.-1f+00", + "product_id": 12, + "warehouse_id": 5, + "bom_id": null, + "doc_number": "MOV2929419176", + "doc_type": "IN", + "movement_qty": -48.22, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 167, + "event_time_utc": "10/12/2025 05:50:35.-1f+00", + "product_id": 15, + "warehouse_id": 2, + "bom_id": null, + "doc_number": "MOV4687619394", + "doc_type": "IN", + "movement_qty": 66.67, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 168, + 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05:36:10.-1f+00" + }, + { + "inventory_movement_id": 172, + "event_time_utc": "11/12/2025 13:50:59.-1f+00", + "product_id": 10, + "warehouse_id": 1, + "bom_id": null, + "doc_number": "MOV6782951618", + "doc_type": "IN", + "movement_qty": -1.77, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": "26/12/2025 05:36:10.-1f+00" + }, + { + "inventory_movement_id": 173, + "event_time_utc": "2/12/2025 10:43:43.-1f+00", + "product_id": 3, + "warehouse_id": 3, + "bom_id": null, + "doc_number": "MOV6952738372", + "doc_type": "IN", + "movement_qty": -36.38, + "uom_code": null, + "unit_price": null, + "discount_unit_price": null, + "amount": null, + "discount_amount": null, + "onhand_qty_after": null, + "responsible_person": null, + "audit_status": null, + "source_system": "WMS", + "last_updated_utc": 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"event_time_utc": "26/12/2025 05:36:11.-1f+00", + "source_system": null, + "last_updated_utc": "26/12/2025 05:36:11.-1f+00" + }, + { + "invoice_id": 18, + "invoice_request_number": "INV881944", + "contract_id": null, + "customer_id": 2, + "invoice_amount": 47111.67, + "contract_amount": null, + "invoice_date_utc": "29/10/2025 16:00:00+00", + "actual_invoice_date_utc": null, + "invoice_type": null, + "stage": null, + "result": null, + "event_time_utc": "26/12/2025 05:36:11.-1f+00", + "source_system": null, + "last_updated_utc": "26/12/2025 05:36:11.-1f+00" + }, + { + "invoice_id": 19, + "invoice_request_number": "INV63773037", + "contract_id": null, + "customer_id": 5, + "invoice_amount": 39694.31, + "contract_amount": null, + "invoice_date_utc": "4/12/2025 16:00:00+00", + "actual_invoice_date_utc": null, + "invoice_type": null, + "stage": null, + "result": null, + "event_time_utc": "26/12/2025 05:36:11.-1f+00", + "source_system": null, + "last_updated_utc": "26/12/2025 05:36:11.-1f+00" + }, + { + "invoice_id": 20, + "invoice_request_number": "INV82625925", + "contract_id": null, + "customer_id": 10, + "invoice_amount": 35210.5, + "contract_amount": null, + "invoice_date_utc": "30/10/2025 16:00:00+00", + "actual_invoice_date_utc": null, + "invoice_type": null, + "stage": null, + "result": null, + "event_time_utc": "26/12/2025 05:36:11.-1f+00", + "source_system": null, + "last_updated_utc": "26/12/2025 05:36:11.-1f+00" + } +] \ No newline at end of file diff --git a/sql/fact_labor_report.json b/sql/fact_labor_report.json new file mode 100644 index 0000000..9a268e5 --- /dev/null +++ b/sql/fact_labor_report.json @@ -0,0 +1,902 @@ +[ + { + "labor_report_id": 1, + "work_order_number": "WO6412", + "task_name": null, + "product_id": 19, + "operation_status": null, + "worker_name": "周峰", + "report_qty": 10.0, + "good_qty": null, + "bad_qty": null, + "duration_minutes": 60.0, + "uom_code": null, + "bad_reason_text": null, + "event_time_utc": "17/12/2025 05:41:28.-1f+00", + 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