diff --git a/lzwcai_demp_tool_server_dify_to_mcp_test/lzwcai_demp_tool_server_dify_to_mcp_test/logs/lzwcai_demp_tool_server_dify_to_mcp_test.log b/lzwcai_demp_tool_server_dify_to_mcp_test/lzwcai_demp_tool_server_dify_to_mcp_test/logs/lzwcai_demp_tool_server_dify_to_mcp_test.log
new file mode 100644
index 0000000..b9251e7
--- /dev/null
+++ b/lzwcai_demp_tool_server_dify_to_mcp_test/lzwcai_demp_tool_server_dify_to_mcp_test/logs/lzwcai_demp_tool_server_dify_to_mcp_test.log
@@ -0,0 +1,156 @@
+2026-01-19 16:05:23 - src.utils.logger_config - INFO - [logger_config.py:213] - ================================================================================
+2026-01-19 16:05:23 - src.utils.logger_config - INFO - [logger_config.py:214] - 日志系统初始化完成 - 2026-01-19 16:05:23
+2026-01-19 16:05:23 - src.utils.logger_config - INFO - [logger_config.py:215] - 日志级别: INFO
+2026-01-19 16:05:23 - src.utils.logger_config - INFO - [logger_config.py:216] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 16:05:23 - src.utils.logger_config - INFO - [logger_config.py:217] - 控制台输出: False
+2026-01-19 16:05:23 - src.utils.logger_config - INFO - [logger_config.py:218] - 文件输出: True
+2026-01-19 16:05:23 - src.utils.logger_config - INFO - [logger_config.py:219] - 文件轮转: 最大10MB, 保留5个备份
+2026-01-19 16:05:23 - src.utils.logger_config - INFO - [logger_config.py:220] - ================================================================================
+2026-01-19 16:05:23 - __main__ - INFO - [main.py:50] - ================================================================================
+2026-01-19 16:05:23 - __main__ - INFO - [main.py:51] - Dify MCP 服务器启动
+2026-01-19 16:05:23 - __main__ - INFO - [main.py:52] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 16:05:23 - __main__ - INFO - [main.py:53] - ================================================================================
+2026-01-19 16:05:25 - src.workflow.workflow_server - INFO - [workflow_server.py:322] - 调用 /parameters API: http://192.168.11.24:3001/v1/parameters
+2026-01-19 16:05:25 - src.workflow.workflow_server - INFO - [workflow_server.py:323] - 请求头: {'Authorization': 'Bearer app-RM7IGxvdq48ixu1JXFiUZh76'}
+2026-01-19 16:05:25 - src.workflow.workflow_server - INFO - [workflow_server.py:324] - 请求参数: {'user': 'pp666'}
+2026-01-19 16:05:25 - src.workflow.workflow_server - INFO - [workflow_server.py:328] - /parameters API 响应状态码: 200
+2026-01-19 16:05:25 - src.workflow.workflow_server - INFO - [workflow_server.py:333] - /parameters API 响应数据: {'opening_statement': '', 'suggested_questions': [], 'suggested_questions_after_answer': {'enabled': False}, 'speech_to_text': {'enabled': False}, 'text_to_speech': {'enabled': False, 'voice': '', 'language': ''}, 'retriever_resource': {'enabled': True}, 'annotation_reply': {'enabled': False}, 'more_like_this': {'enabled': False}, 'user_input_form': [{'file': {'variable': 'contact_file', 'label': 'contact_file', 'type': 'file', 'max_length': 4096, 'required': True, 'options': [], 'allowed_file_upload_methods': ['remote_url'], 'allowed_file_types': ['document'], 'allowed_file_extensions': []}}], 'sensitive_word_avoidance': {'enabled': False}, 'file_upload': {'image': {'enabled': False, 'number_limits': 3, 'transfer_methods': ['local_file', 'remote_url']}, 'enabled': False, 'allowed_file_types': ['image'], 'allowed_file_extensions': ['.JPG', '.JPEG', '.PNG', '.GIF', '.WEBP', '.SVG'], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'number_limits': 3, 'fileUploadConfig': {'file_size_limit': 15, 'batch_count_limit': 5, 'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'workflow_file_upload_limit': 10}}, 'system_parameters': {'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'file_size_limit': 15, 'workflow_file_upload_limit': 10}}
+2026-01-19 16:05:25 - __main__ - INFO - [main.py:68] - 传输模式: stdio
+2026-01-19 16:05:25 - __main__ - INFO - [main.py:69] - 配置参数: {'base_url': 'http://192.168.11.24:3001/v1', 'app_sks': ['app-RM7IGxvdq48ixu1JXFiUZh76'], 'mode_type': 'workflow', 'transport': 'stdio'}
+2026-01-19 16:05:26 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-19 16:05:26 - src.create_mcp - INFO - [create_mcp.py:104] - 工具 tool_HeTongShenChaGongZuoLiu 的 parameters 数据: {'opening_statement': '', 'suggested_questions': [], 'suggested_questions_after_answer': {'enabled': False}, 'speech_to_text': {'enabled': False}, 'text_to_speech': {'enabled': False, 'voice': '', 'language': ''}, 'retriever_resource': {'enabled': True}, 'annotation_reply': {'enabled': False}, 'more_like_this': {'enabled': False}, 'user_input_form': [{'file': {'variable': 'contact_file', 'label': 'contact_file', 'type': 'file', 'max_length': 4096, 'required': True, 'options': [], 'allowed_file_upload_methods': ['remote_url'], 'allowed_file_types': ['document'], 'allowed_file_extensions': []}}], 'sensitive_word_avoidance': {'enabled': False}, 'file_upload': {'image': {'enabled': False, 'number_limits': 3, 'transfer_methods': ['local_file', 'remote_url']}, 'enabled': False, 'allowed_file_types': ['image'], 'allowed_file_extensions': ['.JPG', '.JPEG', '.PNG', '.GIF', '.WEBP', '.SVG'], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'number_limits': 3, 'fileUploadConfig': {'file_size_limit': 15, 'batch_count_limit': 5, 'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'workflow_file_upload_limit': 10}}, 'system_parameters': {'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'file_size_limit': 15, 'workflow_file_upload_limit': 10}}
+2026-01-19 16:05:26 - src.create_mcp - INFO - [create_mcp.py:110] - 工具 tool_HeTongShenChaGongZuoLiu 提取的文件字段: [{'variable': 'contact_file', 'label': 'contact_file', 'required': True, 'max_length': 4096, 'allowed_file_types': ['document'], 'allowed_file_upload_methods': ['remote_url'], 'allowed_file_extensions': [], 'is_list': False}]
+2026-01-19 16:06:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-19 16:06:14 - src.create_mcp - INFO - [create_mcp.py:144] - 工具 tool_HeTongShenChaGongZuoLiu 的文件字段信息: [{'variable': 'contact_file', 'label': 'contact_file', 'required': True, 'max_length': 4096, 'allowed_file_types': ['document'], 'allowed_file_upload_methods': ['remote_url'], 'allowed_file_extensions': [], 'is_list': False}]
+2026-01-19 16:06:14 - src.create_mcp - INFO - [create_mcp.py:145] - 工具 tool_HeTongShenChaGongZuoLiu 调用前的 arguments: {'contact_file': [{'url': 'http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx'}]}
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:64] - 工具 tool_HeTongShenChaGongZuoLiu 的文件字段变量名: {'contact_file'}
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:73] - 工具 tool_HeTongShenChaGongZuoLiu: 发现文件字段 contact_file，值: [{'url': 'http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx'}]
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:168] - 工具 tool_HeTongShenChaGongZuoLiu: 处理文件列表: [{'url': 'http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx'}]
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:85] - 工具 tool_HeTongShenChaGongZuoLiu: 准备处理文件列表: [{'url': 'http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx'}]
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:322] - 从URL http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx 提取的文件扩展名: docx
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:333] - URL http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx 匹配文件类型: document
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:96] - 工具 tool_HeTongShenChaGongZuoLiu: 自动识别文件类型为 document
+2026-01-19 16:06:14 - src.workflow.workflow_server - INFO - [workflow_server.py:242] - 开始上传远程文件: http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx
+2026-01-19 16:06:14 - src.utils.upload_file - INFO - [upload_file.py:400] - 开始处理文件: http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx
+2026-01-19 16:06:14 - src.utils.upload_file - INFO - [upload_file.py:86] - 正在下载文件: http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx (尝试 1/3)
+2026-01-19 16:06:14 - src.utils.upload_file - INFO - [upload_file.py:100] - 文件下载成功: C:\Users\HiWin10\AppData\Local\Temp\eb25b2b233a8719f680d4b0b9c5a80e7.docx (大小: 20445 字节)
+2026-01-19 16:06:14 - src.utils.upload_file - INFO - [upload_file.py:189] - 准备上传文件: C:\Users\HiWin10\AppData\Local\Temp\eb25b2b233a8719f680d4b0b9c5a80e7.docx (大小: 0.02 MB)
+2026-01-19 16:06:14 - src.utils.upload_file - INFO - [upload_file.py:220] - 文件上传成功: eb25b2b233a8719f680d4b0b9c5a80e7.docx (ID: e2ec6720-9cf9-4a64-aa31-102e787a343b)
+2026-01-19 16:06:14 - src.utils.upload_file - INFO - [upload_file.py:417] - 已清理临时文件: C:\Users\HiWin10\AppData\Local\Temp\eb25b2b233a8719f680d4b0b9c5a80e7.docx
+2026-01-19 16:06:14 - src.workflow.workflow_server - INFO - [workflow_server.py:255] - 文件上传成功 - ID: e2ec6720-9cf9-4a64-aa31-102e787a343b, 名称: eb25b2b233a8719f680d4b0b9c5a80e7.docx, 大小: 20445 bytes
+2026-01-19 16:06:14 - src.workflow.workflow_server - INFO - [workflow_server.py:274] - 文件预处理完成，共处理 1 个文件
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:111] - 工具 tool_HeTongShenChaGongZuoLiu: 文件预处理完成，成功上传 1 个文件
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:219] - 工具 tool_HeTongShenChaGongZuoLiu: 字段 contact_file 是 file 类型，输出单个对象
+2026-01-19 16:06:14 - src.create_mcp_utils - INFO - [create_mcp_utils.py:221] - 工具 tool_HeTongShenChaGongZuoLiu: 字段 contact_file 最终值: {'transfer_method': 'local_file', 'type': 'document', 'upload_file_id': 'e2ec6720-9cf9-4a64-aa31-102e787a343b'}
+2026-01-19 16:06:14 - src.create_mcp - INFO - [create_mcp.py:149] - 工具 tool_HeTongShenChaGongZuoLiu 处理后的 arguments: {'contact_file': {'transfer_method': 'local_file', 'type': 'document', 'upload_file_id': 'e2ec6720-9cf9-4a64-aa31-102e787a343b'}}
+2026-01-19 16:06:14 - src.workflow.workflow_server - INFO - [workflow_server.py:131] - Sending data to Dify API: {'inputs': {'contact_file': {'transfer_method': 'local_file', 'type': 'document', 'upload_file_id': 'e2ec6720-9cf9-4a64-aa31-102e787a343b'}}, 'response_mode': 'streaming', 'user': 'pp666'}
+2026-01-19 16:06:14 - src.workflow.workflow_server - INFO - [workflow_server.py:132] - Sending headers to Dify API: {'Authorization': 'Bearer app-RM7IGxvdq48ixu1JXFiUZh76', 'Content-Type': 'application/json'}
+2026-01-19 16:06:14 - src.workflow.workflow_server - INFO - [workflow_server.py:133] - Sending url to Dify API: http://192.168.11.24:3001/v1/workflows/run
+2026-01-19 16:06:14 - src.workflow.workflow_server - INFO - [workflow_server.py:148] - Response1:{'inputs': {'contact_file': {'transfer_method': 'local_file', 'type': 'document', 'upload_file_id': 'e2ec6720-9cf9-4a64-aa31-102e787a343b'}}, 'response_mode': 'streaming', 'user': 'pp666'} 400 BAD REQUEST
+2026-01-19 16:06:14 - src.workflow.workflow_server - ERROR - [workflow_server.py:152] - API request failed with status 400
+2026-01-19 16:06:14 - src.workflow.workflow_server - ERROR - [workflow_server.py:153] - Response content: {"code": "invalid_param", "message": "File validation failed for file: eb25b2b233a8719f680d4b0b9c5a80e7.docx", "status": 400}
+
+2026-01-19 16:06:14 - src.workflow.workflow_server - ERROR - [workflow_server.py:154] - Request data: {'inputs': {'contact_file': {'transfer_method': 'local_file', 'type': 'document', 'upload_file_id': 'e2ec6720-9cf9-4a64-aa31-102e787a343b'}}, 'response_mode': 'streaming', 'user': 'pp666'}
+2026-01-19 16:06:14 - src.create_mcp - ERROR - [create_mcp.py:178] - 工具 tool_HeTongShenChaGongZuoLiu 调用 Dify API 失败: [400] invalid_param: File validation failed for file: eb25b2b233a8719f680d4b0b9c5a80e7.docx
+2026-01-19 16:21:31 - src.utils.logger_config - INFO - [logger_config.py:213] - ================================================================================
+2026-01-19 16:21:31 - src.utils.logger_config - INFO - [logger_config.py:214] - 日志系统初始化完成 - 2026-01-19 16:21:31
+2026-01-19 16:21:31 - src.utils.logger_config - INFO - [logger_config.py:215] - 日志级别: INFO
+2026-01-19 16:21:31 - src.utils.logger_config - INFO - [logger_config.py:216] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 16:21:31 - src.utils.logger_config - INFO - [logger_config.py:217] - 控制台输出: False
+2026-01-19 16:21:31 - src.utils.logger_config - INFO - [logger_config.py:218] - 文件输出: True
+2026-01-19 16:21:31 - src.utils.logger_config - INFO - [logger_config.py:219] - 文件轮转: 最大10MB, 保留5个备份
+2026-01-19 16:21:31 - src.utils.logger_config - INFO - [logger_config.py:220] - ================================================================================
+2026-01-19 16:21:31 - __main__ - INFO - [main.py:50] - ================================================================================
+2026-01-19 16:21:31 - __main__ - INFO - [main.py:51] - Dify MCP 服务器启动
+2026-01-19 16:21:31 - __main__ - INFO - [main.py:52] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 16:21:31 - __main__ - INFO - [main.py:53] - ================================================================================
+2026-01-19 16:21:33 - src.workflow.workflow_server - INFO - [workflow_server.py:322] - 调用 /parameters API: http://192.168.11.24:3001/v1/parameters
+2026-01-19 16:21:33 - src.workflow.workflow_server - INFO - [workflow_server.py:323] - 请求头: {'Authorization': 'Bearer app-RM7IGxvdq48ixu1JXFiUZh76'}
+2026-01-19 16:21:33 - src.workflow.workflow_server - INFO - [workflow_server.py:324] - 请求参数: {'user': 'pp666'}
+2026-01-19 16:21:33 - src.workflow.workflow_server - INFO - [workflow_server.py:328] - /parameters API 响应状态码: 200
+2026-01-19 16:21:33 - src.workflow.workflow_server - INFO - [workflow_server.py:333] - /parameters API 响应数据: {'opening_statement': '', 'suggested_questions': [], 'suggested_questions_after_answer': {'enabled': False}, 'speech_to_text': {'enabled': False}, 'text_to_speech': {'enabled': False, 'voice': '', 'language': ''}, 'retriever_resource': {'enabled': True}, 'annotation_reply': {'enabled': False}, 'more_like_this': {'enabled': False}, 'user_input_form': [{'file': {'variable': 'contact_file', 'label': 'contact_file', 'type': 'file', 'max_length': 4096, 'required': True, 'options': [], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'allowed_file_types': ['document'], 'allowed_file_extensions': []}}], 'sensitive_word_avoidance': {'enabled': False}, 'file_upload': {'image': {'enabled': False, 'number_limits': 3, 'transfer_methods': ['local_file', 'remote_url']}, 'enabled': False, 'allowed_file_types': ['image'], 'allowed_file_extensions': ['.JPG', '.JPEG', '.PNG', '.GIF', '.WEBP', '.SVG'], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'number_limits': 3, 'fileUploadConfig': {'file_size_limit': 15, 'batch_count_limit': 5, 'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'workflow_file_upload_limit': 10}}, 'system_parameters': {'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'file_size_limit': 15, 'workflow_file_upload_limit': 10}}
+2026-01-19 16:21:33 - __main__ - INFO - [main.py:68] - 传输模式: stdio
+2026-01-19 16:21:33 - __main__ - INFO - [main.py:69] - 配置参数: {'base_url': 'http://192.168.11.24:3001/v1', 'app_sks': ['app-RM7IGxvdq48ixu1JXFiUZh76'], 'mode_type': 'workflow', 'transport': 'stdio'}
+2026-01-19 16:21:37 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-19 16:21:37 - src.create_mcp - INFO - [create_mcp.py:104] - 工具 tool_HeTongShenChaGongZuoLiu 的 parameters 数据: {'opening_statement': '', 'suggested_questions': [], 'suggested_questions_after_answer': {'enabled': False}, 'speech_to_text': {'enabled': False}, 'text_to_speech': {'enabled': False, 'voice': '', 'language': ''}, 'retriever_resource': {'enabled': True}, 'annotation_reply': {'enabled': False}, 'more_like_this': {'enabled': False}, 'user_input_form': [{'file': {'variable': 'contact_file', 'label': 'contact_file', 'type': 'file', 'max_length': 4096, 'required': True, 'options': [], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'allowed_file_types': ['document'], 'allowed_file_extensions': []}}], 'sensitive_word_avoidance': {'enabled': False}, 'file_upload': {'image': {'enabled': False, 'number_limits': 3, 'transfer_methods': ['local_file', 'remote_url']}, 'enabled': False, 'allowed_file_types': ['image'], 'allowed_file_extensions': ['.JPG', '.JPEG', '.PNG', '.GIF', '.WEBP', '.SVG'], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'number_limits': 3, 'fileUploadConfig': {'file_size_limit': 15, 'batch_count_limit': 5, 'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'workflow_file_upload_limit': 10}}, 'system_parameters': {'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'file_size_limit': 15, 'workflow_file_upload_limit': 10}}
+2026-01-19 16:21:37 - src.create_mcp - INFO - [create_mcp.py:110] - 工具 tool_HeTongShenChaGongZuoLiu 提取的文件字段: [{'variable': 'contact_file', 'label': 'contact_file', 'required': True, 'max_length': 4096, 'allowed_file_types': ['document'], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'allowed_file_extensions': [], 'is_list': False}]
+2026-01-19 16:21:46 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-19 16:21:46 - src.create_mcp - INFO - [create_mcp.py:144] - 工具 tool_HeTongShenChaGongZuoLiu 的文件字段信息: [{'variable': 'contact_file', 'label': 'contact_file', 'required': True, 'max_length': 4096, 'allowed_file_types': ['document'], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'allowed_file_extensions': [], 'is_list': False}]
+2026-01-19 16:21:46 - src.create_mcp - INFO - [create_mcp.py:145] - 工具 tool_HeTongShenChaGongZuoLiu 调用前的 arguments: {'contact_file': [{'url': 'http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx'}]}
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:64] - 工具 tool_HeTongShenChaGongZuoLiu 的文件字段变量名: {'contact_file'}
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:73] - 工具 tool_HeTongShenChaGongZuoLiu: 发现文件字段 contact_file，值: [{'url': 'http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx'}]
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:168] - 工具 tool_HeTongShenChaGongZuoLiu: 处理文件列表: [{'url': 'http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx'}]
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:85] - 工具 tool_HeTongShenChaGongZuoLiu: 准备处理文件列表: [{'url': 'http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx'}]
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:322] - 从URL http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx 提取的文件扩展名: docx
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:333] - URL http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx 匹配文件类型: document
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:96] - 工具 tool_HeTongShenChaGongZuoLiu: 自动识别文件类型为 document
+2026-01-19 16:21:46 - src.workflow.workflow_server - INFO - [workflow_server.py:242] - 开始上传远程文件: http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx
+2026-01-19 16:21:46 - src.utils.upload_file - INFO - [upload_file.py:400] - 开始处理文件: http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx
+2026-01-19 16:21:46 - src.utils.upload_file - INFO - [upload_file.py:86] - 正在下载文件: http://192.168.11.24:9000/lzwcai/upload/2026-01-19/eb25b2b233a8719f680d4b0b9c5a80e7/eb25b2b233a8719f680d4b0b9c5a80e7.docx (尝试 1/3)
+2026-01-19 16:21:46 - src.utils.upload_file - INFO - [upload_file.py:100] - 文件下载成功: C:\Users\HiWin10\AppData\Local\Temp\eb25b2b233a8719f680d4b0b9c5a80e7.docx (大小: 20445 字节)
+2026-01-19 16:21:46 - src.utils.upload_file - INFO - [upload_file.py:189] - 准备上传文件: C:\Users\HiWin10\AppData\Local\Temp\eb25b2b233a8719f680d4b0b9c5a80e7.docx (大小: 0.02 MB)
+2026-01-19 16:21:46 - src.utils.upload_file - INFO - [upload_file.py:220] - 文件上传成功: eb25b2b233a8719f680d4b0b9c5a80e7.docx (ID: c09a25e0-9d92-4b8d-b098-a59d6be10921)
+2026-01-19 16:21:46 - src.utils.upload_file - INFO - [upload_file.py:417] - 已清理临时文件: C:\Users\HiWin10\AppData\Local\Temp\eb25b2b233a8719f680d4b0b9c5a80e7.docx
+2026-01-19 16:21:46 - src.workflow.workflow_server - INFO - [workflow_server.py:255] - 文件上传成功 - ID: c09a25e0-9d92-4b8d-b098-a59d6be10921, 名称: eb25b2b233a8719f680d4b0b9c5a80e7.docx, 大小: 20445 bytes
+2026-01-19 16:21:46 - src.workflow.workflow_server - INFO - [workflow_server.py:274] - 文件预处理完成，共处理 1 个文件
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:111] - 工具 tool_HeTongShenChaGongZuoLiu: 文件预处理完成，成功上传 1 个文件
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:219] - 工具 tool_HeTongShenChaGongZuoLiu: 字段 contact_file 是 file 类型，输出单个对象
+2026-01-19 16:21:46 - src.create_mcp_utils - INFO - [create_mcp_utils.py:221] - 工具 tool_HeTongShenChaGongZuoLiu: 字段 contact_file 最终值: {'transfer_method': 'local_file', 'type': 'document', 'upload_file_id': 'c09a25e0-9d92-4b8d-b098-a59d6be10921'}
+2026-01-19 16:21:46 - src.create_mcp - INFO - [create_mcp.py:149] - 工具 tool_HeTongShenChaGongZuoLiu 处理后的 arguments: {'contact_file': {'transfer_method': 'local_file', 'type': 'document', 'upload_file_id': 'c09a25e0-9d92-4b8d-b098-a59d6be10921'}}
+2026-01-19 16:21:46 - src.workflow.workflow_server - INFO - [workflow_server.py:131] - Sending data to Dify API: {'inputs': {'contact_file': {'transfer_method': 'local_file', 'type': 'document', 'upload_file_id': 'c09a25e0-9d92-4b8d-b098-a59d6be10921'}}, 'response_mode': 'streaming', 'user': 'pp666'}
+2026-01-19 16:21:46 - src.workflow.workflow_server - INFO - [workflow_server.py:132] - Sending headers to Dify API: {'Authorization': 'Bearer app-RM7IGxvdq48ixu1JXFiUZh76', 'Content-Type': 'application/json'}
+2026-01-19 16:21:46 - src.workflow.workflow_server - INFO - [workflow_server.py:133] - Sending url to Dify API: http://192.168.11.24:3001/v1/workflows/run
+2026-01-19 16:21:46 - src.workflow.workflow_server - INFO - [workflow_server.py:148] - Response1:{'inputs': {'contact_file': {'transfer_method': 'local_file', 'type': 'document', 'upload_file_id': 'c09a25e0-9d92-4b8d-b098-a59d6be10921'}}, 'response_mode': 'streaming', 'user': 'pp666'} 200 OK
+2026-01-19 22:09:39 - src.utils.logger_config - INFO - [logger_config.py:213] - ================================================================================
+2026-01-19 22:09:39 - src.utils.logger_config - INFO - [logger_config.py:214] - 日志系统初始化完成 - 2026-01-19 22:09:39
+2026-01-19 22:09:39 - src.utils.logger_config - INFO - [logger_config.py:215] - 日志级别: INFO
+2026-01-19 22:09:39 - src.utils.logger_config - INFO - [logger_config.py:216] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 22:09:39 - src.utils.logger_config - INFO - [logger_config.py:217] - 控制台输出: False
+2026-01-19 22:09:39 - src.utils.logger_config - INFO - [logger_config.py:218] - 文件输出: True
+2026-01-19 22:09:39 - src.utils.logger_config - INFO - [logger_config.py:219] - 文件轮转: 最大10MB, 保留5个备份
+2026-01-19 22:09:39 - src.utils.logger_config - INFO - [logger_config.py:220] - ================================================================================
+2026-01-19 22:09:39 - __main__ - INFO - [main.py:50] - ================================================================================
+2026-01-19 22:09:39 - __main__ - INFO - [main.py:51] - Dify MCP 服务器启动
+2026-01-19 22:09:39 - __main__ - INFO - [main.py:52] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 22:09:39 - __main__ - INFO - [main.py:53] - ================================================================================
+2026-01-19 22:09:54 - src.workflow.workflow_server - INFO - [workflow_server.py:322] - 调用 /parameters API: http://192.167.30.8:3001/v1/parameters
+2026-01-19 22:09:54 - src.workflow.workflow_server - INFO - [workflow_server.py:323] - 请求头: {'Authorization': 'Bearer app-0dlxPuFY8HBXo6PpoeStQCjA'}
+2026-01-19 22:09:54 - src.workflow.workflow_server - INFO - [workflow_server.py:324] - 请求参数: {'user': 'pp666'}
+2026-01-19 22:09:55 - src.utils.logger_config - INFO - [logger_config.py:213] - ================================================================================
+2026-01-19 22:09:55 - src.utils.logger_config - INFO - [logger_config.py:214] - 日志系统初始化完成 - 2026-01-19 22:09:55
+2026-01-19 22:09:55 - src.utils.logger_config - INFO - [logger_config.py:215] - 日志级别: INFO
+2026-01-19 22:09:55 - src.utils.logger_config - INFO - [logger_config.py:216] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 22:09:55 - src.utils.logger_config - INFO - [logger_config.py:217] - 控制台输出: False
+2026-01-19 22:09:55 - src.utils.logger_config - INFO - [logger_config.py:218] - 文件输出: True
+2026-01-19 22:09:55 - src.utils.logger_config - INFO - [logger_config.py:219] - 文件轮转: 最大10MB, 保留5个备份
+2026-01-19 22:09:55 - src.utils.logger_config - INFO - [logger_config.py:220] - ================================================================================
+2026-01-19 22:09:55 - __main__ - INFO - [main.py:50] - ================================================================================
+2026-01-19 22:09:55 - __main__ - INFO - [main.py:51] - Dify MCP 服务器启动
+2026-01-19 22:09:55 - __main__ - INFO - [main.py:52] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 22:09:55 - __main__ - INFO - [main.py:53] - ================================================================================
+2026-01-19 22:10:01 - src.utils.logger_config - INFO - [logger_config.py:213] - ================================================================================
+2026-01-19 22:10:01 - src.utils.logger_config - INFO - [logger_config.py:214] - 日志系统初始化完成 - 2026-01-19 22:10:01
+2026-01-19 22:10:01 - src.utils.logger_config - INFO - [logger_config.py:215] - 日志级别: INFO
+2026-01-19 22:10:01 - src.utils.logger_config - INFO - [logger_config.py:216] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 22:10:01 - src.utils.logger_config - INFO - [logger_config.py:217] - 控制台输出: False
+2026-01-19 22:10:01 - src.utils.logger_config - INFO - [logger_config.py:218] - 文件输出: True
+2026-01-19 22:10:01 - src.utils.logger_config - INFO - [logger_config.py:219] - 文件轮转: 最大10MB, 保留5个备份
+2026-01-19 22:10:01 - src.utils.logger_config - INFO - [logger_config.py:220] - ================================================================================
+2026-01-19 22:10:01 - __main__ - INFO - [main.py:50] - ================================================================================
+2026-01-19 22:10:01 - __main__ - INFO - [main.py:51] - Dify MCP 服务器启动
+2026-01-19 22:10:01 - __main__ - INFO - [main.py:52] - 日志文件: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_demp_tool_server_dify_to_mcp_test\lzwcai_demp_tool_server_dify_to_mcp_test\logs\lzwcai_demp_tool_server_dify_to_mcp_test.log
+2026-01-19 22:10:01 - __main__ - INFO - [main.py:53] - ================================================================================
+2026-01-19 22:10:06 - src.workflow.workflow_server - INFO - [workflow_server.py:322] - 调用 /parameters API: http://192.167.30.8:3001/v1/parameters
+2026-01-19 22:10:06 - src.workflow.workflow_server - INFO - [workflow_server.py:323] - 请求头: {'Authorization': 'Bearer app-0dlxPuFY8HBXo6PpoeStQCjA'}
+2026-01-19 22:10:06 - src.workflow.workflow_server - INFO - [workflow_server.py:324] - 请求参数: {'user': 'pp666'}
+2026-01-19 22:10:10 - src.workflow.workflow_server - INFO - [workflow_server.py:328] - /parameters API 响应状态码: 200
+2026-01-19 22:10:10 - src.workflow.workflow_server - INFO - [workflow_server.py:333] - /parameters API 响应数据: {'opening_statement': '', 'suggested_questions': [], 'suggested_questions_after_answer': {'enabled': False}, 'speech_to_text': {'enabled': False}, 'text_to_speech': {'enabled': False, 'voice': '', 'language': ''}, 'retriever_resource': {'enabled': True}, 'annotation_reply': {'enabled': False}, 'more_like_this': {'enabled': False}, 'user_input_form': [{'paragraph': {'variable': 'contentText', 'label': '订单内容', 'type': 'paragraph', 'max_length': 48000, 'required': False, 'options': [], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'allowed_file_types': ['image', 'document'], 'allowed_file_extensions': []}}, {'file': {'variable': 'contentFile', 'label': '文件内容', 'type': 'file', 'max_length': 5, 'required': False, 'options': [], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'allowed_file_types': ['document', 'image'], 'allowed_file_extensions': []}}], 'sensitive_word_avoidance': {'enabled': False}, 'file_upload': {'image': {'enabled': False, 'number_limits': 3, 'transfer_methods': ['local_file', 'remote_url']}, 'enabled': False, 'allowed_file_types': ['image'], 'allowed_file_extensions': ['.JPG', '.JPEG', '.PNG', '.GIF', '.WEBP', '.SVG'], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'number_limits': 3, 'fileUploadConfig': {'file_size_limit': 15, 'batch_count_limit': 5, 'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'workflow_file_upload_limit': 10}}, 'system_parameters': {'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'file_size_limit': 15, 'workflow_file_upload_limit': 10}}
+2026-01-19 22:10:10 - src.workflow.workflow_server - INFO - [workflow_server.py:322] - 调用 /parameters API: http://192.167.30.8:3001/v1/parameters
+2026-01-19 22:10:10 - src.workflow.workflow_server - INFO - [workflow_server.py:328] - /parameters API 响应状态码: 200
+2026-01-19 22:10:10 - src.workflow.workflow_server - INFO - [workflow_server.py:323] - 请求头: {'Authorization': 'Bearer app-0dlxPuFY8HBXo6PpoeStQCjA'}
+2026-01-19 22:10:10 - src.workflow.workflow_server - INFO - [workflow_server.py:333] - /parameters API 响应数据: {'opening_statement': '', 'suggested_questions': [], 'suggested_questions_after_answer': {'enabled': False}, 'speech_to_text': {'enabled': False}, 'text_to_speech': {'enabled': False, 'voice': '', 'language': ''}, 'retriever_resource': {'enabled': True}, 'annotation_reply': {'enabled': False}, 'more_like_this': {'enabled': False}, 'user_input_form': [{'paragraph': {'variable': 'contentText', 'label': '订单内容', 'type': 'paragraph', 'max_length': 48000, 'required': False, 'options': [], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'allowed_file_types': ['image', 'document'], 'allowed_file_extensions': []}}, {'file': {'variable': 'contentFile', 'label': '文件内容', 'type': 'file', 'max_length': 5, 'required': False, 'options': [], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'allowed_file_types': ['document', 'image'], 'allowed_file_extensions': []}}], 'sensitive_word_avoidance': {'enabled': False}, 'file_upload': {'image': {'enabled': False, 'number_limits': 3, 'transfer_methods': ['local_file', 'remote_url']}, 'enabled': False, 'allowed_file_types': ['image'], 'allowed_file_extensions': ['.JPG', '.JPEG', '.PNG', '.GIF', '.WEBP', '.SVG'], 'allowed_file_upload_methods': ['local_file', 'remote_url'], 'number_limits': 3, 'fileUploadConfig': {'file_size_limit': 15, 'batch_count_limit': 5, 'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'workflow_file_upload_limit': 10}}, 'system_parameters': {'image_file_size_limit': 10, 'video_file_size_limit': 100, 'audio_file_size_limit': 50, 'file_size_limit': 15, 'workflow_file_upload_limit': 10}}
+2026-01-19 22:10:10 - src.workflow.workflow_server - INFO - [workflow_server.py:324] - 请求参数: {'user': 'pp666'}
+2026-01-19 22:10:13 - __main__ - INFO - [main.py:68] - 传输模式: stdio
+2026-01-19 22:10:13 - __main__ - INFO - [main.py:69] - 配置参数: {'base_url': 'http://192.167.30.8:3001/v1', 'app_sks': ['app-0dlxPuFY8HBXo6PpoeStQCjA'], 'mode_type': 'workflow', 'transport': 'stdio'}
+2026-01-19 22:10:17 - __main__ - INFO - [main.py:68] - 传输模式: stdio
+2026-01-19 22:10:17 - __main__ - INFO - [main.py:69] - 配置参数: {'base_url': 'http://192.167.30.8:3001/v1', 'app_sks': ['app-0dlxPuFY8HBXo6PpoeStQCjA'], 'mode_type': 'workflow', 'transport': 'stdio'}
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/businessQueries.json b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/businessQueries.json
index 5bf8a91..d9aed5a 100644
--- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/businessQueries.json
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/businessQueries.json
@@ -4,7 +4,7 @@
     "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",
+    "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id WHERE EXTRACT(YEAR FROM so.order_date_utc) = 2025 ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 30",
     "parameters": {}
   },
   {
@@ -12,7 +12,7 @@
     "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 ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 50 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts",
+    "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 30 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 30 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts",
     "parameters": {}
   },
   {
@@ -20,7 +20,7 @@
     "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 ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20",
+    "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name LIMIT 30 ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC LIMIT 30 ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC LIMIT 30 ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC LIMIT 30 ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20",
     "parameters": {}
   },
   {
@@ -28,7 +28,7 @@
     "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 ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status",
+    "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC LIMIT 30 ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC LIMIT 30 ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category LIMIT 30 ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST LIMIT 30 ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status LIMIT 30",
     "parameters": {}
   },
   {
@@ -36,7 +36,7 @@
     "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 ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12",
+    "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC LIMIT 30 ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12",
     "parameters": {}
   },
   {
@@ -44,7 +44,7 @@
     "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 ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10",
+    "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC LIMIT 30 ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC LIMIT 30 ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10",
     "parameters": {}
   }
 ]
\ No newline at end of file
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log
index e9ed7b4..c653d64 100644
--- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor.log
@@ -668,3 +668,112 @@
 2026-01-10 00:47:46 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
 2026-01-10 00:47:46 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
 2026-01-10 00:47:46 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:13 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs
+2026-01-16 12:39:13 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-16 12:39:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-16 12:39:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis
+2026-01-16 12:39:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "OrderDelayWarningAnalysis",
+  "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级",
+  "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id WHERE EXTRACT(YEAR FROM so.order_date_utc) = 2025 ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 30",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:39:17 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes
+2026-01-16 12:39:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "WorkOrderProgressAndAnomalyNodes",
+  "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点",
+  "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 30 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 30 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:39:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:26 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:26 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning
+2026-01-16 12:39:26 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SupplyChainRiskWarning",
+  "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示",
+  "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name LIMIT 30 ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC LIMIT 30 ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC LIMIT 30 ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC LIMIT 30 ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:39:26 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:26 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:30 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-16 12:39:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "OnePageDecisionBrief",
+  "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报",
+  "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC LIMIT 30 ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:39:30 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard
+2026-01-16 12:39:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "EfficiencyOutputLossDashboard",
+  "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示",
+  "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC LIMIT 30 ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC LIMIT 30 ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category LIMIT 30 ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST LIMIT 30 ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status LIMIT 30",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:39:33 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:33 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:34 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:34 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning
+2026-01-16 12:39:34 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "MetricTrendAndTurningPointWarning",
+  "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警",
+  "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC LIMIT 30 ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC LIMIT 30 ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:39:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log
index 4466214..ae6a095 100644
--- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log
@@ -1,45 +1,109 @@
-2026-01-10 00:10:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-10 00:10:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard
-2026-01-10 00:10:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+2026-01-16 12:39:13 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agent\lzwcai_mcpskills_mfg_data_agent\logs
+2026-01-16 12:39:13 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-16 12:39:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-16 12:39:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis
+2026-01-16 12:39:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
   "datasourceId": "19",
-  "businessName": "EfficiencyOutputLossDashboard",
-  "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示",
-  "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status",
+  "businessName": "OrderDelayWarningAnalysis",
+  "businessDescription": "订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级",
+  "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc >= event_time_utc), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment WHERE doc_date_utc IS NOT NULL AND event_time_utc IS NOT NULL GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection WHERE pass_qty IS NOT NULL AND fail_qty IS NOT NULL), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.3 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 60 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) >= 30 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id WHERE EXTRACT(YEAR FROM so.order_date_utc) = 2025 ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 30",
   "parameters": {},
   "testParams": {}
 }
-2026-01-10 00:10:04 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-10 00:10:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-10 00:26:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-10 00:26:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
-2026-01-10 00:26:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+2026-01-16 12:39:17 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:17 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:21 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes
+2026-01-16 12:39:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:21 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "WorkOrderProgressAndAnomalyNodes",
+  "businessDescription": "工单执行进度与异常节点：实时拉取工单数据，动态映射订单各环节状态（OPEN→PENDING, STARTED→IN_PROGRESS, CLOSED→COMPLETED），呈现执行进度与异常节点",
+  "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 24 AND completion_rate < 50 OR (last_report_time IS NULL AND elapsed_hours > 24) OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 30 ; WITH timeline AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, CASE WHEN wo.status = 'CLOSED' THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1) ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) END AS duration_hours, wo.planned_qty, wo.completed_qty FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't') SELECT work_order_number, product_name, CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' END AS status_name, TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS start_time, TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS last_update, duration_hours, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, CASE WHEN planned_qty > 0 THEN ROUND(completed_qty * 100.0 / planned_qty, 1) ELSE 0 END AS completion_rate FROM timeline ORDER BY start_time DESC LIMIT 30 ; WITH current_stats AS (SELECT COUNT(*) AS total_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order), anomaly_stats AS (SELECT COUNT(*) AS anomaly_count FROM fact_work_order wo WHERE wo.status = 'STARTED' AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24 AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 5 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:39:22 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:22 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:26 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:26 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning
+2026-01-16 12:39:26 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SupplyChainRiskWarning",
+  "businessDescription": "供应链风险预警：整合采购系统中的供应商历史交期与质检合格率，结合外采物流数据，识别交期异常+物流停滞组合风险模式，自动推送高风险订单提示",
+  "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name LIMIT 30 ; WITH supplier_risk_info AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE((SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp)) FROM fact_purchase_order po2 LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id WHERE po2.supplier_id = s.supplier_id AND pr.doc_date_utc IS NOT NULL), 0) AS avg_delivery_days, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), order_risk AS (SELECT po.purchase_order_number, po.doc_date_utc::date AS order_date, sri.supplier_name, sri.supplier_category, ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days, sri.return_count, sri.receipt_count, CASE WHEN sri.receipt_count > 0 THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1) ELSE 100 END AS quality_rate, EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order, CASE WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH' WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC LIMIT 30 ; WITH supplier_stats AS (SELECT s.supplier_category, COUNT(DISTINCT s.supplier_id) AS supplier_count, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_category) SELECT supplier_category, supplier_count, order_count, receipt_count, return_count, CASE WHEN receipt_count > 0 THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1) ELSE 100 END AS quality_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC LIMIT 30 ; WITH monthly_delivery AS (SELECT DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS receipt_count, AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days FROM fact_purchase_receipt pr JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)), monthly_returns AS (SELECT DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start, COUNT(*) AS return_count FROM fact_purchase_return pret GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)), monthly_combined AS (SELECT md.month_start, md.receipt_count, ROUND(md.avg_delivery_days, 1) AS avg_delivery_days, COALESCE(mr.return_count, 0) AS return_count FROM monthly_delivery md LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start), with_trend AS (SELECT *, LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days, LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count FROM monthly_combined) SELECT month_start, receipt_count, avg_delivery_days, return_count, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, CASE WHEN prev_avg_days IS NULL THEN 'NONE' WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING' WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING' ELSE 'STABLE' END AS delivery_trend, CASE WHEN prev_return_count IS NULL THEN 'NONE' WHEN return_count > prev_return_count THEN 'INCREASING' WHEN return_count < prev_return_count THEN 'DECREASING' ELSE 'STABLE' END AS return_trend FROM with_trend ORDER BY month_start DESC LIMIT 30 ; WITH risk_summary AS (SELECT s.supplier_id, s.supplier_name, COALESCE((SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0) AS return_count, COALESCE((SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0) AS receipt_count FROM dim_supplier s WHERE s.is_current = 't'), risk_counts AS (SELECT SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 10 THEN 'HIGH_RISK' WHEN return_rate > 5 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 20",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:39:26 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:26 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:26 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:30 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-16 12:39:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
   "datasourceId": "19",
   "businessName": "OnePageDecisionBrief",
   "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报",
-  "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12",
+  "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC LIMIT 30 ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12",
   "parameters": {},
   "testParams": {}
 }
-2026-01-10 00:26:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-10 00:26:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-10 00:47:45 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-10 00:47:45 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning
-2026-01-10 00:47:45 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-10 00:47:45 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-10 00:47:45 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+2026-01-16 12:39:30 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:30 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard
+2026-01-16 12:39:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "EfficiencyOutputLossDashboard",
+  "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示",
+  "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC LIMIT 30 ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC LIMIT 30 ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category LIMIT 30 ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST LIMIT 30 ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status LIMIT 30",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:39:33 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:33 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:33 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:34 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:39:34 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning
+2026-01-16 12:39:34 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:39:34 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
   "datasourceId": "19",
   "businessName": "MetricTrendAndTurningPointWarning",
   "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警",
-  "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10",
+  "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC LIMIT 30 ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC LIMIT 30 ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10",
   "parameters": {},
   "testParams": {}
 }
-2026-01-10 00:47:46 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-10 00:47:46 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-10 00:47:46 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:39:35 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:39:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-10 b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-10
new file mode 100644
index 0000000..4466214
--- /dev/null
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-10
@@ -0,0 +1,45 @@
+2026-01-10 00:10:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-10 00:10:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard
+2026-01-10 00:10:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "EfficiencyOutputLossDashboard",
+  "businessDescription": "人效-产值-损耗三维模型仪表盘：关联订单量×工时×人员数×成本，构建人效—产值—损耗三维模型，按部门(产品类别)汇总展示",
+  "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC ; SELECT lr.worker_name, p.product_category AS department, COUNT(DISTINCT lr.work_order_number) AS work_order_count, SUM(lr.duration_minutes) AS total_work_minutes, ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour, RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY lr.worker_name, p.product_category ORDER BY p.product_category, total_output DESC ; SELECT TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month, p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS active_worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output, ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker, ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category ORDER BY month DESC, p.product_category ; SELECT p.product_category AS department, p.product_name, p.product_code, COALESCE(SUM(qi.pass_qty), 0) AS pass_qty, COALESCE(SUM(qi.fail_qty), 0) AS fail_qty, ROUND(COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100, 2) AS defect_rate, CASE WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST ; SELECT p.product_category AS department, wo.status, COUNT(*) AS order_count, SUM(wo.planned_qty) AS total_planned, SUM(wo.completed_qty) AS total_completed, ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate, ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category, wo.status ORDER BY p.product_category, wo.status",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-10 00:10:04 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-10 00:10:04 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-10 00:10:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-10 00:26:28 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-10 00:26:28 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-10 00:26:28 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "OnePageDecisionBrief",
+  "businessDescription": "一页式决策简报：自动聚合订单、生产、财务、售后等关键数据，生成经营决策简报",
+  "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; WITH current_month AS (SELECT 'current_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)), last_month AS (SELECT 'last_month' AS period, COUNT(*) AS order_count, SUM(deal_amount) AS sales_amount FROM fact_sales_order WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')) SELECT period, order_count, ROUND(sales_amount, 2) AS sales_amount FROM current_month UNION ALL SELECT * FROM last_month ; SELECT c.customer_name, COUNT(so.sales_order_id) AS order_count, ROUND(SUM(so.deal_amount), 2) AS total_order_amount, ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate, SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count, SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount FROM fact_sales_order so INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't' GROUP BY c.customer_name ORDER BY total_order_amount DESC LIMIT 10 ; SELECT p.product_category, COUNT(DISTINCT wo.work_order_id) AS work_order_count, ROUND(SUM(wo.planned_qty), 0) AS planned_qty, ROUND(SUM(wo.completed_qty), 0) AS completed_qty, ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate, COUNT(DISTINCT lr.worker_name) AS worker_count, ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours FROM dim_product p LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id WHERE p.is_current = 't' GROUP BY p.product_category ORDER BY completed_qty DESC ; SELECT 'warning_metrics' AS category, 'unpaid_order_amount' AS metric_name, ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value, 50000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 5, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 70, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 5, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5 THEN 'EXCEEDED' ELSE 'NORMAL' END ; SELECT TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month, COUNT(*) AS order_count, ROUND(SUM(deal_amount), 2) AS sales_amount, ROUND(AVG(deal_amount), 2) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count FROM fact_sales_order GROUP BY DATE_TRUNC('month', order_date_utc::timestamp) ORDER BY month DESC LIMIT 12",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-10 00:26:28 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-10 00:26:28 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-10 00:26:28 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-10 00:47:45 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-10 00:47:45 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning
+2026-01-10 00:47:45 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-10 00:47:45 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-10 00:47:45 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "MetricTrendAndTurningPointWarning",
+  "businessDescription": "指标趋势分析与拐点预警：基于移动平均与线性回归分析人效、产量、废品率趋势，输出上升/下降/平稳判断与拐点预警",
+  "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC ; WITH weekly_metrics AS (SELECT DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)), weekly_quality AS (SELECT DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)), weekly_combined AS (SELECT wm.week_start, wm.worker_count, wm.total_hours, wm.total_output, wm.hourly_efficiency, COALESCE(wq.defect_rate, 0) AS defect_rate FROM weekly_metrics wm LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start), weekly_with_lag AS (SELECT *, LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency, LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output, LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate FROM weekly_combined) SELECT week_start, worker_count, ROUND(total_hours, 1) AS total_hours, ROUND(total_output, 0) AS total_output, ROUND(hourly_efficiency, 2) AS hourly_output, ROUND(defect_rate, 2) AS defect_rate, CASE WHEN prev_efficiency IS NULL THEN 'NONE' WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.1 THEN 'RISING' WHEN total_output < prev_output * 0.9 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC ; WITH recent_data AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency, SUM(lr.report_qty) AS total_output FROM fact_labor_report lr WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(lr.event_time_utc::timestamp)), recent_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate FROM fact_quality_inspection qi WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days' GROUP BY DATE(qi.event_time_utc::timestamp)), period_stats AS (SELECT AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d, AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d, AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d FROM recent_data rd), quality_stats AS (SELECT AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d, AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d FROM recent_quality rq) SELECT 'efficiency_per_hour' AS metric_name, ROUND(ps.avg_eff_7d, 2) AS last_7d_avg, ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg, ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct, CASE WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; WITH daily_eff AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), with_ma_step1 AS (SELECT metric_date, hourly_efficiency, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7 FROM daily_eff), with_ma AS (SELECT metric_date, hourly_efficiency, ma7, LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7, LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7 FROM with_ma_step1), turning_points AS (SELECT metric_date, hourly_efficiency, ma7, prev_ma7, prev2_ma7, CASE WHEN prev2_ma7 IS NOT NULL AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD' WHEN prev2_ma7 IS NOT NULL AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD' ELSE NULL END AS turning_type FROM with_ma) SELECT metric_date, ROUND(hourly_efficiency, 2) AS daily_efficiency, ROUND(ma7, 2) AS ma7_line, turning_type, CASE WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES' WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE' ELSE 'NONE' END AS recommendation FROM turning_points WHERE turning_type IS NOT NULL ORDER BY metric_date DESC LIMIT 10",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-10 00:47:46 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-10 00:47:46 - lzwcai_mcpskills_mfg_data_agent.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-10 00:47:46 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log
index 1360088..116f876 100644
--- a/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log
+++ b/lzwcai_mcpskills_mfg_data_agent/lzwcai_mcpskills_mfg_data_agent/logs/mcp_services.log
@@ -206,3 +206,37 @@
 2026-01-10 00:47:45 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning
 2026-01-10 00:47:45 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
 2026-01-10 00:47:46 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-16 12:39:13 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-16 12:39:14 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-16 12:39:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OrderDelayWarningAnalysis
+2026-01-16 12:39:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:21 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: WorkOrderProgressAndAnomalyNodes
+2026-01-16 12:39:21 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:22 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:26 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplyChainRiskWarning
+2026-01-16 12:39:26 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:26 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:30 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: OnePageDecisionBrief
+2026-01-16 12:39:30 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:30 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: EfficiencyOutputLossDashboard
+2026-01-16 12:39:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:33 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:39:34 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: MetricTrendAndTurningPointWarning
+2026-01-16 12:39:34 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:39:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agent/pyproject.toml b/lzwcai_mcpskills_mfg_data_agent/pyproject.toml
index 038d392..ed5e3b1 100644
--- a/lzwcai_mcpskills_mfg_data_agent/pyproject.toml
+++ b/lzwcai_mcpskills_mfg_data_agent/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "lzwcai-mcpskills-mfg-data-agent"
-version = "0.1.2"
+version = "0.1.4"
 description = "制造业数据智能体 - MCP server for manufacturing data intelligence with dynamic tool generation"
 readme = "README.md"
 requires-python = ">=3.13"
diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json
index 7ea5f89..fc1def5 100644
--- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json
+++ b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/businessQueries.json
@@ -4,7 +4,7 @@
     "businessName": "SupplierEvaluationAndSmartReplenishment",
     "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。",
     "datasourceId": "19",
-    "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;",
+    "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name LIMIT 30 ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code LIMIT 30 ;",
     "parameters": {}
   },
   {
@@ -12,7 +12,7 @@
     "businessName": "SalesBIIntelligentAnalyticsPlatform",
     "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
     "datasourceId": "19",
-    "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+    "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name LIMIT 30 ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE LIMIT 30 ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC LIMIT 30 ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
     "parameters": {}
   },
   {
@@ -20,7 +20,7 @@
     "businessName": "SalesPerformanceIntelligentStatistics",
     "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。",
     "datasourceId": "19",
-    "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;",
+    "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n LIMIT 30 ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC LIMIT 30 ;",
     "parameters": {}
   },
   {
@@ -28,7 +28,7 @@
     "businessName": "FinancialAnalyticsDashboard",
     "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。",
     "datasourceId": "19",
-    "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;",
+    "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d LIMIT 30 ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC LIMIT 30 ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym LIMIT 30 ;",
     "parameters": {}
   },
   {
@@ -36,7 +36,7 @@
     "businessName": "SmartCostPredictionModel",
     "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。",
     "datasourceId": "19",
-    "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;",
+    "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' LIMIT 30 ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code LIMIT 30 ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC LIMIT 30 ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC LIMIT 30 ;",
     "parameters": {}
   },
   {
@@ -44,7 +44,7 @@
     "businessName": "HumanResourcesAnalytics",
     "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
     "datasourceId": "19",
-    "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0)::numeric / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;",
+    "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position LIMIT 30 ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0)::numeric / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC LIMIT 30 ;",
     "parameters": {}
   }
 ]
\ No newline at end of file
diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor.log b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor.log
index 2d05afb..04d9750 100644
--- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor.log
+++ b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor.log
@@ -400,3 +400,97 @@
 2026-01-09 20:43:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
 2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
 2026-01-09 20:43:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:03 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs
+2026-01-16 12:40:03 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-16 12:40:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-16 12:40:05 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:05 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment
+2026-01-16 12:40:05 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SupplierEvaluationAndSmartReplenishment",
+  "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。",
+  "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name LIMIT 30 ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code LIMIT 30 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:40:05 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:05 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:06 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:06 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-16 12:40:06 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesBIIntelligentAnalyticsPlatform",
+  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
+  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name LIMIT 30 ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE LIMIT 30 ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC LIMIT 30 ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:40:06 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:06 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics
+2026-01-16 12:40:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesPerformanceIntelligentStatistics",
+  "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。",
+  "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n LIMIT 30 ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC LIMIT 30 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:40:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:08 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:08 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard
+2026-01-16 12:40:08 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:08 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:08 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "FinancialAnalyticsDashboard",
+  "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。",
+  "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d LIMIT 30 ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC LIMIT 30 ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym LIMIT 30 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:40:09 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:09 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:09 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:11 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:11 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
+2026-01-16 12:40:11 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "HumanResourcesAnalytics",
+  "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
+  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position LIMIT 30 ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0)::numeric / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC LIMIT 30 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-16 12:40:11 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:11 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log
index 2d05afb..b786195 100644
--- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log
+++ b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log
@@ -1,402 +1,94 @@
-2026-01-09 18:44:45 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs
-2026-01-09 18:44:45 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:313] - ============================================================
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:316] - ============================================================
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:324] - ============================================================
-2026-01-09 18:44:45 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
-2026-01-09 18:44:46 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2026-01-09 18:44:46 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
-2026-01-09 18:44:46 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
-2026-01-09 18:44:46 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
-2026-01-09 18:44:46 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
-2026-01-09 18:44:46 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
-2026-01-09 18:44:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 18:44:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
-2026-01-09 18:44:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SalesBIIntelligentAnalyticsPlatform",
-  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
-  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 18:44:47 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 18:44:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 18:49:19 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 18:49:19 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics
-2026-01-09 18:49:19 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SalesPerformanceIntelligentStatistics",
-  "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。",
-  "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 18:49:19 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 18:49:19 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 18:49:24 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 18:49:24 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard
-2026-01-09 18:49:24 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "FinancialAnalyticsDashboard",
-  "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。",
-  "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 18:49:24 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 18:49:24 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 18:49:27 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 18:49:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel
-2026-01-09 18:49:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SmartCostPredictionModel",
-  "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。",
-  "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = 'RAW' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = 'RAW' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = 'RAW' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 18:49:27 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 18:49:27 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 18:57:52 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 18:57:52 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
-2026-01-09 18:57:52 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SalesBIIntelligentAnalyticsPlatform",
-  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
-  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 18:57:52 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 18:57:52 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 18:58:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 18:58:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment
-2026-01-09 18:58:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+2026-01-16 12:40:03 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs
+2026-01-16 12:40:03 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-16 12:40:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-16 12:40:05 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:05 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment
+2026-01-16 12:40:05 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
   "datasourceId": "19",
   "businessName": "SupplierEvaluationAndSmartReplenishment",
   "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。",
-  "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = 'RAW' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = 'RAW' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;",
+  "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name LIMIT 30 ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code LIMIT 30 ;",
   "parameters": {},
   "testParams": {}
 }
-2026-01-09 18:58:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 18:58:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 18:58:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 18:58:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
-2026-01-09 18:58:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+2026-01-16 12:40:05 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:05 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:05 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:06 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:06 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-16 12:40:06 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
   "datasourceId": "19",
   "businessName": "SalesBIIntelligentAnalyticsPlatform",
   "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
-  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name LIMIT 30 ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE LIMIT 30 ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC LIMIT 30 ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
   "parameters": {},
   "testParams": {}
 }
-2026-01-09 18:58:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 18:58:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:02:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:02:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
-2026-01-09 19:02:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SalesBIIntelligentAnalyticsPlatform",
-  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
-  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 19:02:47 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:02:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:31:56 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs
-2026-01-09 19:31:56 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:313] - ============================================================
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:316] - ============================================================
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:324] - ============================================================
-2026-01-09 19:31:56 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
-2026-01-09 19:31:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2026-01-09 19:31:58 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
-2026-01-09 19:31:58 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
-2026-01-09 19:31:58 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
-2026-01-09 19:31:58 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
-2026-01-09 19:31:58 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
-2026-01-09 19:32:01 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:32:01 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment
-2026-01-09 19:32:01 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SupplierEvaluationAndSmartReplenishment",
-  "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。",
-  "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 19:32:01 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:32:01 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:32:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:32:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
-2026-01-09 19:32:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SalesBIIntelligentAnalyticsPlatform",
-  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
-  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 19:32:04 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:32:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:32:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:32:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics
-2026-01-09 19:32:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+2026-01-16 12:40:06 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:06 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:06 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics
+2026-01-16 12:40:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
   "datasourceId": "19",
   "businessName": "SalesPerformanceIntelligentStatistics",
   "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。",
-  "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;",
+  "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n LIMIT 30 ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC LIMIT 30 ;",
   "parameters": {},
   "testParams": {}
 }
-2026-01-09 19:32:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:32:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:32:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:32:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard
-2026-01-09 19:32:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+2026-01-16 12:40:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:08 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:08 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard
+2026-01-16 12:40:08 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:08 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:08 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
   "datasourceId": "19",
   "businessName": "FinancialAnalyticsDashboard",
   "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。",
-  "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;",
+  "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d LIMIT 30 ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC LIMIT 30 ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym LIMIT 30 ;",
   "parameters": {},
   "testParams": {}
 }
-2026-01-09 19:32:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:32:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:32:16 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:32:16 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel
-2026-01-09 19:32:16 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SmartCostPredictionModel",
-  "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。",
-  "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 19:32:16 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:32:16 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:32:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:32:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
-2026-01-09 19:32:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:32:33 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:32:33 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SalesBIIntelligentAnalyticsPlatform",
-  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
-  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 19:32:34 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:32:34 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:32:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:34:32 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:34:32 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
-2026-01-09 19:34:32 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SalesBIIntelligentAnalyticsPlatform",
-  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
-  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 19:34:32 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:34:32 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:48:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:48:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment
-2026-01-09 19:48:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SupplierEvaluationAndSmartReplenishment",
-  "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。",
-  "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 19:48:17 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:48:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 19:57:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 19:57:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics
-2026-01-09 19:57:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 19:57:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 19:57:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SalesPerformanceIntelligentStatistics",
-  "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。",
-  "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 19:57:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 19:57:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 19:57:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 20:02:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 20:02:14 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard
-2026-01-09 20:02:14 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "FinancialAnalyticsDashboard",
-  "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。",
-  "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 20:02:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 20:02:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 20:06:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 20:06:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel
-2026-01-09 20:06:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "SmartCostPredictionModel",
-  "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。",
-  "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 20:06:58 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 20:06:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 20:23:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 20:23:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
-2026-01-09 20:23:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+2026-01-16 12:40:09 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:09 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:09 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:11 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-16 12:40:11 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
+2026-01-16 12:40:11 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
   "datasourceId": "19",
   "businessName": "HumanResourcesAnalytics",
   "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
-  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;",
+  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position LIMIT 30 ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position LIMIT 30 ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0)::numeric / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym LIMIT 30 ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC LIMIT 30 ;",
   "parameters": {},
   "testParams": {}
 }
-2026-01-09 20:23:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 20:23:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 20:26:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 20:26:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
-2026-01-09 20:26:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 20:26:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 20:26:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "HumanResourcesAnalytics",
-  "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
-  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 20:26:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 20:26:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 20:26:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 20:38:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 20:38:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
-2026-01-09 20:38:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "HumanResourcesAnalytics",
-  "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
-  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 20:38:58 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 20:38:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
-2026-01-09 20:43:32 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs
-2026-01-09 20:43:32 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:313] - ============================================================
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:316] - ============================================================
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:324] - ============================================================
-2026-01-09 20:43:32 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
-2026-01-09 20:43:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2026-01-09 20:43:33 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
-2026-01-09 20:43:33 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
-2026-01-09 20:43:33 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
-2026-01-09 20:43:33 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
-2026-01-09 20:43:33 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
-2026-01-09 20:43:35 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2026-01-09 20:43:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
-2026-01-09 20:43:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
-2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
-2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
-  "datasourceId": "19",
-  "businessName": "HumanResourcesAnalytics",
-  "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
-  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;",
-  "parameters": {},
-  "testParams": {}
-}
-2026-01-09 20:43:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
-2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
-2026-01-09 20:43:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:11 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-16 12:40:11 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-16 12:40:11 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-09 b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-09
new file mode 100644
index 0000000..2d05afb
--- /dev/null
+++ b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/lzwcai_mcp_sqlexecutor_daily.log.2026-01-09
@@ -0,0 +1,402 @@
+2026-01-09 18:44:45 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs
+2026-01-09 18:44:45 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-09 18:44:45 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-09 18:44:46 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-09 18:44:46 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-09 18:44:46 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-09 18:44:46 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-09 18:44:46 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-09 18:44:46 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-09 18:44:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 18:44:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-09 18:44:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesBIIntelligentAnalyticsPlatform",
+  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
+  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 18:44:47 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 18:44:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 18:44:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 18:49:19 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 18:49:19 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics
+2026-01-09 18:49:19 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesPerformanceIntelligentStatistics",
+  "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。",
+  "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 18:49:19 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 18:49:19 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 18:49:19 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 18:49:24 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 18:49:24 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard
+2026-01-09 18:49:24 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "FinancialAnalyticsDashboard",
+  "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。",
+  "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 18:49:24 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 18:49:24 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 18:49:24 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 18:49:27 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 18:49:27 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel
+2026-01-09 18:49:27 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SmartCostPredictionModel",
+  "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。",
+  "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = 'RAW' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = 'RAW' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = 'RAW' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 18:49:27 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 18:49:27 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 18:49:27 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 18:57:52 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 18:57:52 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-09 18:57:52 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesBIIntelligentAnalyticsPlatform",
+  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
+  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 18:57:52 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 18:57:52 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 18:57:52 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 18:58:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 18:58:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment
+2026-01-09 18:58:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SupplierEvaluationAndSmartReplenishment",
+  "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。",
+  "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = 'RAW' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = 'RAW' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 18:58:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 18:58:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 18:58:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 18:58:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 18:58:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-09 18:58:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesBIIntelligentAnalyticsPlatform",
+  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
+  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 18:58:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 18:58:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 18:58:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:02:47 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:02:47 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-09 19:02:47 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesBIIntelligentAnalyticsPlatform",
+  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
+  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:02:47 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:02:47 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:02:47 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:31:56 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs
+2026-01-09 19:31:56 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-09 19:31:56 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-09 19:31:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-09 19:31:58 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-09 19:31:58 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-09 19:31:58 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-09 19:31:58 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-09 19:31:58 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-09 19:32:01 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:32:01 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment
+2026-01-09 19:32:01 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SupplierEvaluationAndSmartReplenishment",
+  "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。",
+  "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:32:01 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:32:01 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:32:01 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:32:04 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:32:04 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-09 19:32:04 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesBIIntelligentAnalyticsPlatform",
+  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
+  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:32:04 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:32:04 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:32:04 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:32:07 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:32:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics
+2026-01-09 19:32:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesPerformanceIntelligentStatistics",
+  "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。",
+  "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:32:07 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:32:07 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:32:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:32:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:32:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard
+2026-01-09 19:32:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "FinancialAnalyticsDashboard",
+  "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。",
+  "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:32:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:32:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:32:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:32:16 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:32:16 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel
+2026-01-09 19:32:16 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SmartCostPredictionModel",
+  "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。",
+  "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:32:16 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:32:16 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:32:16 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:32:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:32:33 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-09 19:32:33 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:32:33 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:32:33 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesBIIntelligentAnalyticsPlatform",
+  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
+  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:32:34 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:32:34 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:32:34 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:34:32 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:34:32 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-09 19:34:32 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesBIIntelligentAnalyticsPlatform",
+  "businessDescription": "销售BI智能分析平台：面向订单、发货/退货、客户复购与毛利贡献等核心指标，提供多维汇总、趋势分析与TOP客户洞察。",
+  "sqlTemplate": "SELECT DATE(so.order_date_utc) AS order_date, so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name AS sales_person_name, COUNT(DISTINCT so.sales_order_id) AS order_count, SUM(COALESCE(so.deal_amount, 0)) AS order_amount FROM fact_sales_order so LEFT JOIN dim_customer c ON c.customer_id = so.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = so.sales_person_id AND p.is_current = TRUE GROUP BY DATE(so.order_date_utc), so.customer_id, c.customer_name, c.customer_level, c.industry, c.address, so.sales_person_id, p.person_name ; WITH ship AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), ret AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, customer_id, sales_person_id, SUM(COALESCE(amount, 0)) AS return_amount FROM fact_sales_return WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM'), customer_id, sales_person_id ), keys AS ( SELECT ym, customer_id, sales_person_id FROM ship UNION SELECT ym, customer_id, sales_person_id FROM ret ) SELECT k.ym, k.customer_id, c.customer_name, k.sales_person_id, p.person_name AS sales_person_name, COALESCE(s.ship_amount, 0) AS ship_amount, COALESCE(r.return_amount, 0) AS return_amount, (COALESCE(s.ship_amount, 0) - COALESCE(r.return_amount, 0)) AS net_amount FROM keys k LEFT JOIN ship s ON s.ym = k.ym AND s.customer_id IS NOT DISTINCT FROM k.customer_id AND s.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN ret r ON r.ym = k.ym AND r.customer_id IS NOT DISTINCT FROM k.customer_id AND r.sales_person_id IS NOT DISTINCT FROM k.sales_person_id LEFT JOIN dim_customer c ON c.customer_id = k.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = k.sales_person_id AND p.is_current = TRUE ; WITH orders AS ( SELECT so.sales_person_id, so.customer_id, COUNT(DISTINCT so.sales_order_id) AS order_cnt FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.sales_person_id, so.customer_id ), rate AS ( SELECT sales_person_id, COUNT(DISTINCT customer_id) AS customer_cnt, SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) AS repeat_customer_cnt, CASE WHEN COUNT(DISTINCT customer_id) > 0 THEN SUM(CASE WHEN order_cnt >= 2 THEN 1 ELSE 0 END) / COUNT(DISTINCT customer_id) ELSE NULL END AS repurchase_rate FROM orders GROUP BY sales_person_id ), avg_rate AS ( SELECT AVG(repurchase_rate) AS avg_repurchase_rate FROM rate WHERE repurchase_rate IS NOT NULL ) SELECT r.sales_person_id, p.person_name AS sales_person_name, r.customer_cnt, r.repeat_customer_cnt, r.repurchase_rate, a.avg_repurchase_rate FROM rate r CROSS JOIN avg_rate a LEFT JOIN dim_person p ON p.person_id = r.sales_person_id AND p.is_current = TRUE WHERE r.repurchase_rate IS NOT NULL AND r.repurchase_rate < a.avg_repurchase_rate ORDER BY (a.avg_repurchase_rate - r.repurchase_rate) DESC ; WITH revenue AS ( SELECT so.customer_id, SUM(COALESCE(so.deal_amount, 0)) AS revenue_amount FROM fact_sales_order so WHERE so.order_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ), cost AS ( SELECT so.customer_id, SUM(COALESCE(ABS(m.movement_qty) * m.unit_price, 0)) AS cost_amount FROM fact_inventory_movement m JOIN fact_sales_order so ON so.sales_order_id = m.sales_order_id WHERE m.sales_order_id IS NOT NULL AND COALESCE(m.movement_qty, 0) < 0 AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY so.customer_id ) SELECT r.customer_id, c.customer_name, r.revenue_amount, COALESCE(k.cost_amount, 0) AS cost_amount, (r.revenue_amount - COALESCE(k.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.revenue_amount > 0 THEN (r.revenue_amount - COALESCE(k.cost_amount, 0)) / r.revenue_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost k ON k.customer_id = r.customer_id LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE ORDER BY gross_profit_amount DESC LIMIT 10 ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:34:32 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:34:32 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:34:32 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:48:17 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:48:17 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment
+2026-01-09 19:48:17 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SupplierEvaluationAndSmartReplenishment",
+  "businessDescription": "供应商评估与智能补货：基于物料消耗、交付周期、到货准时率与来料质检结果，输出供应商绩效指标与安全库存/再订货点/建议补货量。",
+  "sqlTemplate": "WITH mat_out AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM mat_out GROUP BY product_id ) SELECT p.product_code, p.product_name, p.uom_code, s.avg_daily_consume, s.std_daily_consume FROM stats s JOIN dim_product p ON p.product_id = s.product_id AND p.is_current = TRUE WHERE p.product_type = '标准产品' ; WITH lead_time AS ( SELECT po.supplier_id, (DATE(pr.doc_date_utc) - DATE(po.doc_date_utc)) AS lead_days, CASE WHEN po.delivery_date_utc IS NOT NULL AND pr.doc_date_utc IS NOT NULL THEN CASE WHEN DATE(pr.doc_date_utc) <= DATE(po.delivery_date_utc) THEN 1 ELSE 0 END ELSE NULL END AS is_ontime FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL ), quality AS ( SELECT supplier_id, SUM(COALESCE(pass_qty, 0)) AS pass_qty, SUM(COALESCE(fail_qty, 0)) AS fail_qty FROM fact_quality_inspection WHERE qc_type = 'INCOMING' AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' GROUP BY supplier_id ), agg AS ( SELECT supplier_id, AVG(lead_days) AS avg_lead_days, STDDEV_POP(lead_days) AS std_lead_days, AVG(is_ontime) AS ontime_rate FROM lead_time WHERE lead_days IS NOT NULL AND lead_days >= 0 GROUP BY supplier_id ) SELECT s.supplier_id, s.supplier_name, a.avg_lead_days, a.std_lead_days, a.ontime_rate, CASE WHEN (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) > 0 THEN COALESCE(q.pass_qty, 0) / (COALESCE(q.pass_qty, 0) + COALESCE(q.fail_qty, 0)) ELSE NULL END AS incoming_pass_rate FROM agg a LEFT JOIN dim_supplier s ON s.supplier_id = a.supplier_id AND s.is_current = TRUE LEFT JOIN quality q ON q.supplier_id = a.supplier_id ORDER BY s.supplier_name ; WITH params AS ( SELECT 1.65::numeric AS z, 7::int AS review_days ), consume_daily AS ( SELECT product_id, DATE(event_time_utc) AS d, SUM(CASE WHEN doc_type IN ('OUT','OTHER_OUT') THEN ABS(COALESCE(movement_qty, 0)) ELSE 0 END) AS daily_consume FROM fact_inventory_movement WHERE event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '90 days' GROUP BY product_id, DATE(event_time_utc) ), consume_stats AS ( SELECT product_id, AVG(daily_consume) AS avg_daily_consume, STDDEV_POP(daily_consume) AS std_daily_consume FROM consume_daily GROUP BY product_id ), lead_time AS ( SELECT pr.supplier_id, AVG((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS avg_lead_days, STDDEV_POP((DATE(pr.doc_date_utc) - DATE(po.doc_date_utc))) AS std_lead_days FROM fact_purchase_order po JOIN fact_purchase_receipt pr ON pr.purchase_order_number = po.purchase_order_number WHERE po.doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND pr.doc_date_utc IS NOT NULL GROUP BY pr.supplier_id ), latest_onhand_row AS ( SELECT m.product_id, m.warehouse_id, m.onhand_qty_after, ROW_NUMBER() OVER (PARTITION BY m.product_id, m.warehouse_id ORDER BY m.event_time_utc DESC, m.inventory_movement_id DESC) AS rn FROM fact_inventory_movement m WHERE m.product_id IS NOT NULL ), onhand AS ( SELECT product_id, SUM(COALESCE(onhand_qty_after, 0)) AS onhand_qty FROM latest_onhand_row WHERE rn = 1 GROUP BY product_id ), product_supplier AS ( SELECT p.product_id, p.product_code, p.product_name, b.supplier_name FROM dim_product p LEFT JOIN dim_bom b ON b.product_code = p.product_code AND b.is_current = TRUE WHERE p.is_current = TRUE AND p.product_type = '标准产品' ), supplier_id_map AS ( SELECT supplier_id, supplier_name FROM dim_supplier WHERE is_current = TRUE ), merged AS ( SELECT ps.product_id, ps.product_code, ps.product_name, sm.supplier_id, COALESCE(cs.avg_daily_consume, 0) AS avg_daily_consume, COALESCE(cs.std_daily_consume, 0) AS std_daily_consume, COALESCE(lt.avg_lead_days, 0) AS avg_lead_days, COALESCE(lt.std_lead_days, 0) AS std_lead_days, COALESCE(oh.onhand_qty, 0) AS onhand_qty FROM product_supplier ps LEFT JOIN supplier_id_map sm ON sm.supplier_name IS NOT DISTINCT FROM ps.supplier_name LEFT JOIN consume_stats cs ON cs.product_id = ps.product_id LEFT JOIN lead_time lt ON lt.supplier_id = sm.supplier_id LEFT JOIN onhand oh ON oh.product_id = ps.product_id ) SELECT product_code, product_name, onhand_qty, avg_daily_consume, std_daily_consume, avg_lead_days, (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS safety_stock_qty, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) AS reorder_point_qty, GREATEST( 0, (avg_daily_consume * (avg_lead_days + params.review_days)) + (params.z * std_daily_consume * SQRT(GREATEST(avg_lead_days, 0))) - onhand_qty ) AS suggest_order_qty, supplier_id FROM merged CROSS JOIN params ORDER BY suggest_order_qty DESC, product_code ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:48:17 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:48:17 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:48:17 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 19:57:13 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 19:57:13 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics
+2026-01-09 19:57:13 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 19:57:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 19:57:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SalesPerformanceIntelligentStatistics",
+  "businessDescription": "销售业绩智能统计系统：基于订单成交与合同归属，计算首触/跟进/协同的贡献度分摊，并据此估算推荐佣金与人员贡献排名。",
+  "sqlTemplate": "WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.sales_person_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, d.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, d.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ), normalized AS ( SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT n.deal_id, n.person_id, n.role, n.contribution_pct FROM normalized n ; WITH deal AS ( SELECT so.sales_order_id AS deal_id, so.contract_id, so.customer_id, so.deal_amount, so.order_date_utc AS deal_time_utc FROM fact_sales_order so ), contrib AS ( WITH role_first AS ( SELECT d.deal_id, COALESCE(dc.owner_id, so.sales_person_id) AS person_id, 'FIRST_TOUCH' AS role, 0.30 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id LEFT JOIN dim_contract dc ON dc.contract_id = d.contract_id AND dc.is_current = TRUE ), role_follow AS ( SELECT d.deal_id, so.sales_person_id AS person_id, 'FOLLOW_UP' AS role, 0.50 AS role_weight FROM deal d JOIN fact_sales_order so ON so.sales_order_id = d.deal_id ), collab_people AS ( SELECT DISTINCT s.contract_id, s.operator_id AS person_id FROM fact_sales_shipment s WHERE s.operator_id IS NOT NULL UNION DISTINCT SELECT DISTINCT i.contract_id, i.handler_id AS person_id FROM fact_invoice i WHERE i.handler_id IS NOT NULL UNION DISTINCT SELECT DISTINCT w.contract_id, w.handler_id AS person_id FROM fact_write_off w WHERE w.handler_id IS NOT NULL ), role_collab AS ( SELECT d.deal_id, cp.person_id, 'COLLAB' AS role, 0.20 AS role_weight FROM deal d JOIN collab_people cp ON cp.contract_id = d.contract_id ), roles_union AS ( SELECT * FROM role_first UNION ALL SELECT * FROM role_follow UNION ALL SELECT * FROM role_collab ), collab_cnt AS ( SELECT deal_id, COUNT(DISTINCT person_id) AS collab_person_cnt FROM role_collab GROUP BY deal_id ) SELECT ru.deal_id, ru.person_id, ru.role, CASE WHEN ru.role = 'COLLAB' THEN ru.role_weight / NULLIF(cc.collab_person_cnt, 0) ELSE ru.role_weight END AS contribution_pct FROM roles_union ru LEFT JOIN collab_cnt cc ON cc.deal_id = ru.deal_id ) SELECT d.deal_id, d.deal_time_utc, d.contract_id, d.customer_id, c.customer_name, co.person_id, p.person_name, co.role, co.contribution_pct, d.deal_amount, (d.deal_amount * 0.05 * co.contribution_pct) AS recommend_commission_amount FROM deal d JOIN contrib co ON co.deal_id = d.deal_id LEFT JOIN dim_customer c ON c.customer_id = d.customer_id AND c.is_current = TRUE LEFT JOIN dim_person p ON p.person_id = co.person_id AND p.is_current = TRUE ORDER BY d.deal_time_utc DESC, d.deal_id, co.contribution_pct DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 19:57:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 19:57:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 19:57:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 20:02:14 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 20:02:14 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard
+2026-01-09 20:02:14 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "FinancialAnalyticsDashboard",
+  "businessDescription": "财务数据分析看板：覆盖应收/应付月度趋势、现金流收支、逾期应收预警，以及销售毛利与毛利率的月度拆解。",
+  "sqlTemplate": "SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ar_amount, SUM(COALESCE(written_off_amount, 0)) AS ar_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ar_unwritten_off_amount FROM fact_ar_receipt WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ap_amount, SUM(COALESCE(written_off_amount, 0)) AS ap_written_off_amount, SUM(COALESCE(unwritten_off_amount, 0)) AS ap_unwritten_off_amount FROM fact_ap_payment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ORDER BY ym ; SELECT DATE(doc_date_utc) AS d, SUM(COALESCE(income_amount, 0)) AS cash_in, SUM(COALESCE(expense_amount, 0)) AS cash_out, SUM(COALESCE(income_amount, 0)) - SUM(COALESCE(expense_amount, 0)) AS net_cash FROM fact_cash_flow WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '60 days' GROUP BY DATE(doc_date_utc) ORDER BY d ; SELECT r.customer_id, c.customer_name, SUM(COALESCE(r.unwritten_off_amount, 0)) AS ar_overdue_balance, MIN(DATE(r.doc_date_utc)) AS oldest_doc_date FROM fact_ar_receipt r LEFT JOIN dim_customer c ON c.customer_id = r.customer_id AND c.is_current = TRUE WHERE COALESCE(r.unwritten_off_amount, 0) > 0 AND (CURRENT_DATE - DATE(r.doc_date_utc)) > 60 GROUP BY r.customer_id, c.customer_name ORDER BY ar_overdue_balance DESC ; WITH revenue AS ( SELECT to_char(doc_date_utc, 'YYYY-MM') AS ym, SUM(COALESCE(amount, 0)) AS ship_amount FROM fact_sales_shipment WHERE doc_date_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(doc_date_utc, 'YYYY-MM') ), cost AS ( SELECT to_char(event_time_utc, 'YYYY-MM') AS ym, SUM(COALESCE(ABS(movement_qty) * unit_price, 0)) AS cost_amount FROM fact_inventory_movement WHERE sales_order_id IS NOT NULL AND COALESCE(movement_qty, 0) < 0 AND event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(event_time_utc, 'YYYY-MM') ) SELECT r.ym, r.ship_amount, c.cost_amount, (r.ship_amount - COALESCE(c.cost_amount, 0)) AS gross_profit_amount, CASE WHEN r.ship_amount > 0 THEN (r.ship_amount - COALESCE(c.cost_amount, 0)) / r.ship_amount ELSE NULL END AS gross_margin_rate FROM revenue r LEFT JOIN cost c ON c.ym = r.ym ORDER BY r.ym ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 20:02:14 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 20:02:14 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 20:02:14 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 20:06:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 20:06:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SmartCostPredictionModel
+2026-01-09 20:06:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "SmartCostPredictionModel",
+  "businessDescription": "智能成本预测模型：基于历史入库单价估算原材料基准成本，结合BOM用量拆解标准成本结构，并支持原材涨跌价情景推演。",
+  "sqlTemplate": "WITH raw_in AS ( SELECT m.product_id, SUM(COALESCE(m.movement_qty, 0)) AS in_qty, SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) AS in_amount FROM fact_inventory_movement m WHERE m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 GROUP BY m.product_id ) SELECT p.product_code, p.product_name, CASE WHEN ri.in_qty > 0 THEN ri.in_amount / ri.in_qty ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN raw_in ri ON ri.product_id = p.product_id WHERE p.is_current = TRUE AND p.product_type = '标准产品' ; WITH bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ) SELECT p.bom_number, p.parent_product_code, c.component_product_code, c.unit_usage_qty FROM parent p JOIN component c ON c.bom_number = p.bom_number ORDER BY p.parent_product_code, c.component_product_code ; WITH raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ) SELECT lc.parent_product_code, lc.component_product_code, lc.unit_usage_qty, lc.base_unit_cost, lc.component_cost, tc.standard_cost, CASE WHEN tc.standard_cost > 0 THEN lc.component_cost / tc.standard_cost ELSE NULL END AS cost_share FROM line_cost lc JOIN total_cost tc ON tc.parent_product_code = lc.parent_product_code ORDER BY lc.parent_product_code, cost_share DESC ; WITH scenario AS ( SELECT 'RAW-001' AS material_product_code, 0.10 AS delta_pct ), raw_price AS ( SELECT p.product_code, CASE WHEN SUM(COALESCE(m.movement_qty, 0)) > 0 THEN SUM(COALESCE(m.movement_qty, 0) * COALESCE(m.unit_price, 0)) / SUM(COALESCE(m.movement_qty, 0)) ELSE NULL END AS base_unit_cost FROM dim_product p LEFT JOIN fact_inventory_movement m ON m.product_id = p.product_id AND m.event_time_utc >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '180 days' AND COALESCE(m.movement_qty, 0) > 0 AND COALESCE(m.unit_price, 0) > 0 WHERE p.is_current = TRUE AND p.product_type = '标准产品' GROUP BY p.product_code ), bom_min_level AS ( SELECT bom_number, MIN(COALESCE(level_number, 0)) AS min_level FROM dim_bom WHERE is_current = TRUE GROUP BY bom_number ), parent AS ( SELECT b.bom_number, b.product_code AS parent_product_code FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) = ml.min_level WHERE b.is_current = TRUE ), component AS ( SELECT b.bom_number, b.product_code AS component_product_code, COALESCE(b.unit_usage_qty, 0) AS unit_usage_qty FROM dim_bom b JOIN bom_min_level ml ON ml.bom_number = b.bom_number AND COALESCE(b.level_number, 0) > ml.min_level WHERE b.is_current = TRUE ), line_cost AS ( SELECT p.parent_product_code, c.component_product_code, c.unit_usage_qty, rp.base_unit_cost, (c.unit_usage_qty * rp.base_unit_cost) AS component_cost FROM parent p JOIN component c ON c.bom_number = p.bom_number LEFT JOIN raw_price rp ON rp.product_code = c.component_product_code ), total_cost AS ( SELECT parent_product_code, SUM(COALESCE(component_cost, 0)) AS standard_cost FROM line_cost GROUP BY parent_product_code ), delta_cost AS ( SELECT lc.parent_product_code, SUM( CASE WHEN lc.component_product_code = s.material_product_code THEN COALESCE(lc.component_cost, 0) * s.delta_pct ELSE 0 END ) AS scenario_cost_delta FROM line_cost lc CROSS JOIN scenario s GROUP BY lc.parent_product_code ) SELECT tc.parent_product_code, tc.standard_cost AS base_standard_cost, dc.scenario_cost_delta, (tc.standard_cost + COALESCE(dc.scenario_cost_delta, 0)) AS new_standard_cost FROM total_cost tc LEFT JOIN delta_cost dc ON dc.parent_product_code = tc.parent_product_code ORDER BY dc.scenario_cost_delta DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 20:06:58 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 20:06:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 20:06:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 20:23:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 20:23:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
+2026-01-09 20:23:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "HumanResourcesAnalytics",
+  "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
+  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 20:23:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 20:23:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 20:23:12 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 20:26:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 20:26:12 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
+2026-01-09 20:26:12 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 20:26:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 20:26:12 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "HumanResourcesAnalytics",
+  "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
+  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 20:26:13 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 20:26:13 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 20:26:13 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 20:38:58 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 20:38:58 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
+2026-01-09 20:38:58 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "HumanResourcesAnalytics",
+  "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
+  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' WHERE p.department LIKE '%产线%' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person WHERE department LIKE '%产线%' ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 20:38:58 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 20:38:58 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 20:38:58 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-09 20:43:32 - root - INFO - [logger_config.py:151] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_mcpskills_mfg_data_agentv2\lzwcai_mcpskills_mfg_data_agentv2\logs
+2026-01-09 20:43:32 - root - INFO - [logger_config.py:152] - 日志配置 - 级别: INFO, 文件大小限制: 10MB, 备份数量: 5
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-09 20:43:32 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-09 20:43:33 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
+2026-01-09 20:43:33 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-09 20:43:33 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-09 20:43:33 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-09 20:43:33 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-09 20:43:33 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-09 20:43:35 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
+2026-01-09 20:43:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
+2026-01-09 20:43:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:138] - 正在调用测试SQL API: http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema
+2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:139] - 请求参数: {
+  "datasourceId": "19",
+  "businessName": "HumanResourcesAnalytics",
+  "businessDescription": "人力资源数据分析：提供月度在岗人数、招聘/离职趋势、产线部门离职率连续上升预警，以及岗位空缺天数分析。",
+  "sqlTemplate": "WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ) SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, p.position, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department, p.position ORDER BY ym, p.department, p.position ; SELECT to_char(employment_day, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS hire_count FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL AND employment_day >= (CURRENT_DATE - INTERVAL '12 months') GROUP BY to_char(employment_day, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ) SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, position, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department, position ORDER BY ym, department, position ; WITH months AS ( SELECT generate_series( date_trunc('month', CURRENT_DATE) - INTERVAL '11 months', date_trunc('month', CURRENT_DATE), INTERVAL '1 month' )::date AS month_start ), month_end AS ( SELECT month_start, (month_start + INTERVAL '1 month' - INTERVAL '1 day')::date AS month_end FROM months ), headcount AS ( SELECT to_char(me.month_start, 'YYYY-MM') AS ym, p.department, COUNT(DISTINCT p.person_id) AS headcount_active FROM month_end me JOIN dim_person p ON p.row_valid_from <= me.month_end AND p.row_valid_to > me.month_end AND p.status = 'ACTIVE' GROUP BY to_char(me.month_start, 'YYYY-MM'), p.department ), ordered AS ( SELECT person_id, department, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), exit_cnt AS ( SELECT to_char(exit_time, 'YYYY-MM') AS ym, department, COUNT(DISTINCT person_id) AS exit_count FROM exit_event WHERE exit_time >= (CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - INTERVAL '12 months' GROUP BY to_char(exit_time, 'YYYY-MM'), department ), rate AS ( SELECT h.ym, h.department, COALESCE(e.exit_count, 0) AS exit_count, h.headcount_active, CASE WHEN h.headcount_active > 0 THEN COALESCE(e.exit_count, 0) / h.headcount_active ELSE NULL END AS monthly_turnover_rate FROM headcount h LEFT JOIN exit_cnt e ON e.ym = h.ym AND e.department IS NOT DISTINCT FROM h.department ), flag AS ( SELECT r.*, LAG(monthly_turnover_rate, 1) OVER (PARTITION BY department ORDER BY ym) AS r_m1, LAG(monthly_turnover_rate, 2) OVER (PARTITION BY department ORDER BY ym) AS r_m2 FROM rate r ) SELECT ym, department, exit_count, headcount_active, monthly_turnover_rate FROM flag WHERE monthly_turnover_rate IS NOT NULL AND r_m1 IS NOT NULL AND r_m2 IS NOT NULL AND monthly_turnover_rate > r_m1 AND r_m1 > r_m2 ORDER BY department, ym ; WITH ordered AS ( SELECT person_id, department, position, status, row_valid_from, LAG(status) OVER (PARTITION BY person_id ORDER BY row_valid_from) AS prev_status FROM dim_person ), exit_event AS ( SELECT person_id, department, position, row_valid_from AS exit_time FROM ordered WHERE status = 'INACTIVE' AND COALESCE(prev_status, 'UNKNOWN') <> 'INACTIVE' ), hire_event AS ( SELECT department, position, employment_day AS hire_date, person_id FROM dim_person WHERE is_current = TRUE AND employment_day IS NOT NULL ), matched AS ( SELECT e.department, e.position, e.exit_time, ( SELECT MIN(h.hire_date) FROM hire_event h WHERE h.department IS NOT DISTINCT FROM e.department AND h.position IS NOT DISTINCT FROM e.position AND h.hire_date > DATE(e.exit_time) ) AS next_hire_date FROM exit_event e ) SELECT department, position, DATE(exit_time) AS exit_date, next_hire_date, (next_hire_date - DATE(exit_time)) AS vacancy_days FROM matched WHERE next_hire_date IS NOT NULL ORDER BY vacancy_days DESC ;",
+  "parameters": {},
+  "testParams": {}
+}
+2026-01-09 20:43:35 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.11.24:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema "HTTP/1.1 200 "
+2026-01-09 20:43:35 - lzwcai_mcpskills_mfg_data_agentv2.utils.api_client - INFO - [api_client.py:154] - 测试SQL API调用成功
+2026-01-09 20:43:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/mcp_services.log b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/mcp_services.log
index f967584..886fc14 100644
--- a/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/mcp_services.log
+++ b/lzwcai_mcpskills_mfg_data_agentv2/lzwcai_mcpskills_mfg_data_agentv2/logs/mcp_services.log
@@ -115,3 +115,34 @@
 2026-01-09 20:43:35 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
 2026-01-09 20:43:35 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
 2026-01-09 20:43:35 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:362] - 开始运行 MCP SQL Executor 服务器
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:313] - ============================================================
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:314] - 正在启动 MCP 服务器: lzwcai-mcpskills-analyzeOrder
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:315] - 版本: 0.1.0
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:316] - ============================================================
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:320] - 环境配置 - Database ID: 19
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:321] - 环境配置 - Datasource ID: 19
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:322] - 环境配置 - Skill ID: 
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:323] - 环境配置 - Backend Base URL: http://192.168.11.24:8088
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:324] - ============================================================
+2026-01-16 12:40:03 - mcp_services - INFO - [main.py:329] - MCP 服务器已启动，等待客户端连接...
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:156] - 收到列出工具请求
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:119] - 初始化查询配置（数据源: local）...
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:55] - 成功加载 6 个业务查询配置
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:123] - 本地配置: 6 条
+2026-01-16 12:40:04 - mcp_services - INFO - [main.py:165] - 成功生成 6 个 MCP 工具
+2026-01-16 12:40:05 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SupplierEvaluationAndSmartReplenishment
+2026-01-16 12:40:05 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:05 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:06 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesBIIntelligentAnalyticsPlatform
+2026-01-16 12:40:06 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:06 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:07 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: SalesPerformanceIntelligentStatistics
+2026-01-16 12:40:07 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:07 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:08 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: FinancialAnalyticsDashboard
+2026-01-16 12:40:08 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:09 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
+2026-01-16 12:40:11 - mcp_services - INFO - [main.py:190] - 收到工具调用请求: HumanResourcesAnalytics
+2026-01-16 12:40:11 - mcp_services - INFO - [main.py:225] - 正在调用测试SQL API...
+2026-01-16 12:40:11 - mcp_services - INFO - [main.py:227] - 测试SQL API调用成功
diff --git a/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml b/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml
index 85f9e02..c165f8d 100644
--- a/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml
+++ b/lzwcai_mcpskills_mfg_data_agentv2/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "lzwcai-mcpskills-mfg-data-agentv2"
-version = "0.1.3"
+version = "0.1.5"
 description = "制造业数据智能体 - MCP server for manufacturing data intelligence with dynamic tool generation"
 readme = "README.md"
 requires-python = ">=3.13"
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/.gitkeep b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp.log b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp.log
deleted file mode 100644
index c08ee56..0000000
--- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp.log
+++ /dev/null
@@ -1,979 +0,0 @@
-2025-12-20 18:03:07 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs
-2025-12-20 18:03:07 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'workflow_mcp_server'
-2025-12-20 18:03:07 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest
-2025-12-20 18:03:07 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:272] - ==================================================
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:273] - Workflow MCP Server 启动
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:274] - ==================================================
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:279] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None}
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:282] - 使用模式: api
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2002300699510763521
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2002300699510763521
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:56] - [客户端初始化] base_url=http://192.168.2.236:8088
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:57] - [客户端初始化] token=wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:58] - [客户端初始化] execute_timeout=300.0s
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:89] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:90] - [API请求] Headers: {'X-API-Key': ' wf_buh230o9iaea4n6aefsddcexa7p27ydl'}
-2025-12-20 18:03:07 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None
-2025-12-20 18:03:07 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x00000155217CC380>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'GET']>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'GET']>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'GET']>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Sat, 20 Dec 2025 10:03:06 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-20 18:03:07 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521 "HTTP/1.1 200 "
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'GET']>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:97] - [API响应] HTTP 200
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:98] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Sat, 20 Dec 2025 10:03:06 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:103] - [API响应] 获取工作流配置成功: workflow_id=2002300699510763521
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:104] - [API响应] Body: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 17:59:26",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "未命名工的方式作流001",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
-      "datasourceId": null,
-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": null,
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:116] - API 响应原始数据: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 17:59:26",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "未命名工的方式作流001",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
-      "datasourceId": null,
-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": null,
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [
-  {
-    "id": "2002300699812753409",
-    "createBy": "yy8z9",
-    "createTime": "2025-12-20 16:51:02",
-    "updateBy": "yy8z9",
-    "updateTime": "2025-12-20 17:59:26",
-    "serviceId": "2002300699804364801",
-    "uniqueName": "未命名工的方式作流001",
-    "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-    "description": "3123123123",
-    "visualizable": 0,
-    "toolPrompt": null,
-    "toolType": "api",
-    "datasourceId": null,
-    "sqlTemplate": null,
-    "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-    "resultType": null,
-    "sourceType": null,
-    "trainingTaskId": null,
-    "tableMetadataIds": null,
-    "executionCount": 0,
-    "visualizationConfigs": null,
-    "inputJsonSchema": null,
-    "outputJsonSchema": null,
-    "lastExecutionTime": null
-  }
-]
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2002300699510763521, 配置数量: 1
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:290] - 开始运行 MCP Server (stdio 模式)
-2025-12-20 18:03:07 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor
-2025-12-20 18:03:07 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0')
-2025-12-20 18:03:08 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x000001552178D700>
-2025-12-20 18:03:08 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2025-12-20 18:03:08 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest
-2025-12-20 18:03:08 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1
-2025-12-20 18:03:08 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:179] - 处理工具配置: name=weimingminggongdefangshizuoliu001_91bc43ee, description=3123123123...
-2025-12-20 18:03:08 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:183] - 工具 weimingminggongdefangshizuoliu001_91bc43ee 的 inputSchema: {"type": "object", "properties": {"query": {"description": "问题", "type": "string", "format": "paragraph", "maxLength": 3000}}, "required": ["query"]}
-2025-12-20 18:03:08 - lzwcai_workflow_to_mcp.main - INFO - [main.py:193] - ListTools 响应: 返回 1 个工具
-2025-12-20 18:03:08 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-20 18:03:12 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x00000155218D4A70>
-2025-12-20 18:03:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2025-12-20 18:03:12 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - INFO - [main.py:203] - 收到 CallTool 请求: name=weimingminggongdefangshizuoliu001_91bc43ee, arguments={"query": "你好啊"}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:216] - 找到工具配置: {
-  "id": "2002300699812753409",
-  "createBy": "yy8z9",
-  "createTime": "2025-12-20 16:51:02",
-  "updateBy": "yy8z9",
-  "updateTime": "2025-12-20 17:59:26",
-  "serviceId": "2002300699804364801",
-  "uniqueName": "未命名工的方式作流001",
-  "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-  "description": "3123123123",
-  "visualizable": 0,
-  "toolPrompt": null,
-  "toolType": "api",
-  "datasourceId": null,
-  "sqlTemplate": null,
-  "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-  "resultType": null,
-  "sourceType": null,
-  "trainingTaskId": null,
-  "tableMetadataIds": null,
-  "executionCount": 0,
-  "visualizationConfigs": null,
-  "inputJsonSchema": null,
-  "outputJsonSchema": null,
-  "lastExecutionTime": null
-}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - INFO - [main.py:220] - 使用工作流ID: 2002300699510763521
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - INFO - [main.py:228] - 执行工作流请求数据: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:150] - [执行工作流] URL: http://192.168.2.236:8088/open/workflow/execute
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:151] - [执行工作流] Token: wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:152] - [执行工作流] Headers: {"X-API-Key": "Bearer wf_buh230o9iaea4n6aefsddcexa7p27ydl", "Content-Type": "application/json", "Accept": "*/*"}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:153] - [执行工作流] 请求参数: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:154] - [执行工作流] 超时时间: 300.0s
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'POST']>
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'POST']>
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'POST']>
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 401, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Trace-Id', b'40f82dcb-2055-46b4-a9ae-1bbee5591306'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json;charset=UTF-8'), (b'Content-Length', b'61'), (b'Date', b'Sat, 20 Dec 2025 10:03:10 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-20 18:03:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/open/workflow/execute "HTTP/1.1 401 "
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'POST']>
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:164] - [API响应] HTTP 401
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:165] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-trace-id': '40f82dcb-2055-46b4-a9ae-1bbee5591306', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json;charset=UTF-8', 'content-length': '61', 'date': 'Sat, 20 Dec 2025 10:03:10 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - ERROR - [api_client.py:182] - [API错误] 执行工作流API请求失败 (HTTP 401): http://192.168.2.236:8088/open/workflow/execute
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - ERROR - [api_client.py:183] - [API错误] 响应内容: {"msg":"API Key无效","code":401,"errorCode":"UNAUTHORIZED"}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - ERROR - [main.py:236] - 工作流执行失败: 执行工作流API请求失败 (HTTP 401): http://192.168.2.236:8088/open/workflow/execute
-Traceback (most recent call last):
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\utils\api_client.py", line 167, in execute_workflow
-    response.raise_for_status()
-  File "D:\anaconda3\Lib\site-packages\httpx\_models.py", line 829, in raise_for_status
-    raise HTTPStatusError(message, request=request, response=self)
-httpx.HTTPStatusError: Client error '401 ' for url 'http://192.168.2.236:8088/open/workflow/execute'
-For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/401
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\main.py", line 232, in handle_call_tool
-    result = execute_workflow(request_data)
-             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\utils\api_client.py", line 268, in execute_workflow
-    return default_client.execute_workflow(request_data)
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\utils\api_client.py", line 184, in execute_workflow
-    raise Exception(error_msg)
-Exception: 执行工作流API请求失败 (HTTP 401): http://192.168.2.236:8088/open/workflow/execute
-2025-12-20 18:03:12 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-20 18:04:18 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs
-2025-12-20 18:04:18 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'workflow_mcp_server'
-2025-12-20 18:04:18 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest
-2025-12-20 18:04:18 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:272] - ==================================================
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:273] - Workflow MCP Server 启动
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:274] - ==================================================
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:279] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None}
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:282] - 使用模式: api
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2002300699510763521
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2002300699510763521
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:56] - [客户端初始化] base_url=http://192.168.2.236:8088
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:57] - [客户端初始化] token=wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:58] - [客户端初始化] execute_timeout=300.0s
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:89] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:90] - [API请求] Headers: {'X-API-Key': 'wf_buh230o9iaea4n6aefsddcexa7p27ydl'}
-2025-12-20 18:04:18 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None
-2025-12-20 18:04:18 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x0000025DD0ED1F70>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'GET']>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'GET']>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'GET']>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Sat, 20 Dec 2025 10:04:16 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-20 18:04:18 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521 "HTTP/1.1 200 "
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'GET']>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:97] - [API响应] HTTP 200
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:98] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Sat, 20 Dec 2025 10:04:16 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:103] - [API响应] 获取工作流配置成功: workflow_id=2002300699510763521
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:104] - [API响应] Body: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 17:59:26",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "未命名工的方式作流001",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
-      "datasourceId": null,
-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": null,
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:116] - API 响应原始数据: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 17:59:26",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "未命名工的方式作流001",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
-      "datasourceId": null,
-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": null,
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [
-  {
-    "id": "2002300699812753409",
-    "createBy": "yy8z9",
-    "createTime": "2025-12-20 16:51:02",
-    "updateBy": "yy8z9",
-    "updateTime": "2025-12-20 17:59:26",
-    "serviceId": "2002300699804364801",
-    "uniqueName": "未命名工的方式作流001",
-    "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-    "description": "3123123123",
-    "visualizable": 0,
-    "toolPrompt": null,
-    "toolType": "api",
-    "datasourceId": null,
-    "sqlTemplate": null,
-    "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-    "resultType": null,
-    "sourceType": null,
-    "trainingTaskId": null,
-    "tableMetadataIds": null,
-    "executionCount": 0,
-    "visualizationConfigs": null,
-    "inputJsonSchema": null,
-    "outputJsonSchema": null,
-    "lastExecutionTime": null
-  }
-]
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2002300699510763521, 配置数量: 1
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:290] - 开始运行 MCP Server (stdio 模式)
-2025-12-20 18:04:18 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor
-2025-12-20 18:04:18 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0')
-2025-12-20 18:04:19 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x0000025DCF92FF50>
-2025-12-20 18:04:19 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2025-12-20 18:04:19 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest
-2025-12-20 18:04:19 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1
-2025-12-20 18:04:19 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:179] - 处理工具配置: name=weimingminggongdefangshizuoliu001_91bc43ee, description=3123123123...
-2025-12-20 18:04:19 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:183] - 工具 weimingminggongdefangshizuoliu001_91bc43ee 的 inputSchema: {"type": "object", "properties": {"query": {"description": "问题", "type": "string", "format": "paragraph", "maxLength": 3000}}, "required": ["query"]}
-2025-12-20 18:04:19 - lzwcai_workflow_to_mcp.main - INFO - [main.py:193] - ListTools 响应: 返回 1 个工具
-2025-12-20 18:04:19 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-20 18:04:22 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x0000025DD0D21340>
-2025-12-20 18:04:22 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2025-12-20 18:04:22 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.main - INFO - [main.py:203] - 收到 CallTool 请求: name=weimingminggongdefangshizuoliu001_91bc43ee, arguments={"query": "你好啊"}
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:216] - 找到工具配置: {
-  "id": "2002300699812753409",
-  "createBy": "yy8z9",
-  "createTime": "2025-12-20 16:51:02",
-  "updateBy": "yy8z9",
-  "updateTime": "2025-12-20 17:59:26",
-  "serviceId": "2002300699804364801",
-  "uniqueName": "未命名工的方式作流001",
-  "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-  "description": "3123123123",
-  "visualizable": 0,
-  "toolPrompt": null,
-  "toolType": "api",
-  "datasourceId": null,
-  "sqlTemplate": null,
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-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.main - INFO - [main.py:228] - 执行工作流请求数据: {
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-  "inputs": {
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-}
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-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:151] - [执行工作流] Token: wf_buh230o9iaea4n6aefsddcexa7p27ydl
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-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
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-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:154] - [执行工作流] 超时时间: 300.0s
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-2025-12-20 18:04:24 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/open/workflow/execute "HTTP/1.1 200 "
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-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:164] - [API响应] HTTP 200
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:165] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-trace-id': '62311551-e7ab-4d5d-adef-f59b1969a4cf', 'x-ratelimit-limit': '100', 'x-ratelimit-remaining': '99', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json;charset=UTF-8', 'transfer-encoding': 'chunked', 'date': 'Sat, 20 Dec 2025 10:04:22 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:170] - [API响应] 执行工作流成功
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:171] - [API响应] Body: {
-  "msg": "操作成功",
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-  "data": {
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-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好呀！✨ 很高兴见到你！今天过得怎么样呀？希望你度过了愉快的一天。我今天刚整理完知识库，准备了很多有趣的话题和实用的信息，希望能和你一起探讨、学习。不知道你今天想聊些什么呢？无论是有趣的故事、需要解决的问题，还是纯粹想聊聊日常，我都很乐意陪伴你聊天哦！😊"
-      },
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-        "q": "你好呀！✨ 很高兴见到你！今天过得怎么样呀？希望你度过了愉快的一天。我今天刚整理完知识库，准备了很多有趣的话题和实用的信息，希望能和你一起探讨、学习。不知道你今天想聊些什么呢？无论是有趣的故事、需要解决的问题，还是纯粹想聊聊日常，我都很乐意陪伴你聊天哦！😊"
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-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.main - INFO - [main.py:233] - 工作流执行成功: 2002300699510763521
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:234] - 工作流执行结果: {
-  "msg": "操作成功",
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-        "text": "你好呀！✨ 很高兴见到你！今天过得怎么样呀？希望你度过了愉快的一天。我今天刚整理完知识库，准备了很多有趣的话题和实用的信息，希望能和你一起探讨、学习。不知道你今天想聊些什么呢？无论是有趣的故事、需要解决的问题，还是纯粹想聊聊日常，我都很乐意陪伴你聊天哦！😊"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好呀！✨ 很高兴见到你！今天过得怎么样呀？希望你度过了愉快的一天。我今天刚整理完知识库，准备了很多有趣的话题和实用的信息，希望能和你一起探讨、学习。不知道你今天想聊些什么呢？无论是有趣的故事、需要解决的问题，还是纯粹想聊聊日常，我都很乐意陪伴你聊天哦！😊"
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-  }
-}
-2025-12-20 18:04:24 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-23 18:19:55 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs
-2025-12-23 18:19:55 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'workflow_mcp_server'
-2025-12-23 18:19:55 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest
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-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:272] - ==================================================
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:273] - Workflow MCP Server 启动
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:274] - ==================================================
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:279] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None}
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:282] - 使用模式: api
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2002300699510763521
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2002300699510763521
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:56] - [客户端初始化] base_url=http://192.168.2.236:8088
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:57] - [客户端初始化] token=wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:58] - [客户端初始化] execute_timeout=300.0s
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:89] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521
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-2025-12-23 18:19:55 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None
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-2025-12-23 18:19:55 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521 "HTTP/1.1 200 "
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'GET']>
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-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:97] - [API响应] HTTP 200
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:98] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Tue, 23 Dec 2025 10:19:54 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:103] - [API响应] 获取工作流配置成功: workflow_id=2002300699510763521
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:104] - [API响应] Body: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
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-      "createBy": "yy8z9",
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-  "msg": "查询成功",
-  "code": 200,
-  "data": [
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-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
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-}
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [
-  {
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-    "createBy": "yy8z9",
-    "createTime": "2025-12-20 16:51:02",
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-    "serviceId": "2002300699804364801",
-    "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-    "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-    "description": "3123123123",
-    "visualizable": 0,
-    "toolPrompt": null,
-    "toolType": "api",
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-    "sqlTemplate": null,
-    "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
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-    "outputJsonSchema": null,
-    "lastExecutionTime": null
-  }
-]
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2002300699510763521, 配置数量: 1
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:290] - 开始运行 MCP Server (stdio 模式)
-2025-12-23 18:19:55 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor
-2025-12-23 18:19:55 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0')
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-2025-12-23 18:19:56 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
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-2025-12-23 18:19:56 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1
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-2025-12-23 18:19:56 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:183] - 工具 weimingminggongdefangshizuoliu001_91bc43ee 的 inputSchema: {"type": "object", "properties": {"query": {"description": "问题", "type": "string", "format": "paragraph", "maxLength": 3000}}, "required": ["query"]}
-2025-12-23 18:19:56 - lzwcai_workflow_to_mcp.main - INFO - [main.py:193] - ListTools 响应: 返回 1 个工具
-2025-12-23 18:19:56 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-23 18:20:00 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x000001E350DDA3C0>
-2025-12-23 18:20:00 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2025-12-23 18:20:00 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.main - INFO - [main.py:203] - 收到 CallTool 请求: name=weimingminggongdefangshizuoliu001_91bc43ee, arguments={"query": "你好啊"}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:216] - 找到工具配置: {
-  "id": "2002300699812753409",
-  "createBy": "yy8z9",
-  "createTime": "2025-12-20 16:51:02",
-  "updateBy": "yy8z9",
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-  "toolType": "api",
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-  "resultType": null,
-  "sourceType": null,
-  "trainingTaskId": null,
-  "tableMetadataIds": null,
-  "executionCount": 0,
-  "visualizationConfigs": null,
-  "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-  "outputJsonSchema": null,
-  "lastExecutionTime": null
-}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.main - INFO - [main.py:220] - 使用工作流ID: 2002300699510763521
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.main - INFO - [main.py:228] - 执行工作流请求数据: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:150] - [执行工作流] URL: http://192.168.2.236:8088/open/workflow/execute
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:151] - [执行工作流] Token: wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:152] - [执行工作流] Headers: {"X-API-Key": "wf_buh230o9iaea4n6aefsddcexa7p27ydl", "Content-Type": "application/json", "Accept": "*/*"}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:153] - [执行工作流] 请求参数: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:154] - [执行工作流] 超时时间: 300.0s
-2025-12-23 18:20:00 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'POST']>
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-2025-12-23 18:20:01 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/open/workflow/execute "HTTP/1.1 200 "
-2025-12-23 18:20:01 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'POST']>
-2025-12-23 18:20:01 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-23 18:20:01 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-23 18:20:01 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:164] - [API响应] HTTP 200
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:165] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-trace-id': '2c574f18-2d6b-4580-bd8a-eda8c94c26eb', 'x-ratelimit-limit': '100', 'x-ratelimit-remaining': '99', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json;charset=UTF-8', 'transfer-encoding': 'chunked', 'date': 'Tue, 23 Dec 2025 10:20:00 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:170] - [API响应] 执行工作流成功
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:171] - [API响应] Body: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "d8ff9fc7-530c-4b77-a8a8-07d51aa79870",
-    "workflow_id": "3dc9d954-c270-4bbf-aa3d-5945c6acb2f5",
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-        "query": "你好啊"
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-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好！看起来你可能感兴趣于某方面，但还没具体说明？如果需要帮助，尽管告诉我，我很乐意帮忙解答或讨论各种话题！😊"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好！看起来你可能感兴趣于某方面，但还没具体说明？如果需要帮助，尽管告诉我，我很乐意帮忙解答或讨论各种话题！😊"
-      }
-    },
-    "error": null,
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-  }
-}
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.main - INFO - [main.py:233] - 工作流执行成功: 2002300699510763521
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:234] - 工作流执行结果: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "d8ff9fc7-530c-4b77-a8a8-07d51aa79870",
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-    "status": "success",
-    "duration": 0.926932,
-    "nodes_executed": 3,
-    "errors_count": 0,
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-        "query": "你好啊"
-      },
-      "a480a344-7fa9-439b-8db6-79b136a79736": {
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-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好！看起来你可能感兴趣于某方面，但还没具体说明？如果需要帮助，尽管告诉我，我很乐意帮忙解答或讨论各种话题！😊"
-      }
-    },
-    "error": null,
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-  }
-}
-2025-12-23 18:20:01 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-23 18:24:44 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs
-2025-12-23 18:24:44 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'workflow_mcp_server'
-2025-12-23 18:24:44 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest
-2025-12-23 18:24:44 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:275] - ==================================================
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:276] - Workflow MCP Server 启动
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:277] - ==================================================
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:282] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None}
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:285] - 使用模式: api
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2002300699510763521
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2002300699510763521
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:56] - [客户端初始化] base_url=http://192.168.2.236:8088
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:57] - [客户端初始化] token=wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:58] - [客户端初始化] execute_timeout=300.0s
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:89] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:90] - [API请求] Headers: {'X-API-Key': 'wf_buh230o9iaea4n6aefsddcexa7p27ydl'}
-2025-12-23 18:24:44 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None
-2025-12-23 18:24:44 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x000001B44A89F380>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'GET']>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'GET']>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'GET']>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Tue, 23 Dec 2025 10:24:43 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-23 18:24:44 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521 "HTTP/1.1 200 "
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'GET']>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
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-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:97] - [API响应] HTTP 200
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:98] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Tue, 23 Dec 2025 10:24:43 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:103] - [API响应] 获取工作流配置成功: workflow_id=2002300699510763521
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:104] - [API响应] Body: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
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-      "updateTime": "2025-12-20 18:31:56",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
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-      "toolPrompt": null,
-      "toolType": "api",
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-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
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-      "tableMetadataIds": null,
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-      "visualizationConfigs": null,
-      "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:116] - API 响应原始数据: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
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-      "serviceId": "2002300699804364801",
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-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
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-      "trainingTaskId": null,
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-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [
-  {
-    "id": "2002300699812753409",
-    "createBy": "yy8z9",
-    "createTime": "2025-12-20 16:51:02",
-    "updateBy": "yy8z9",
-    "updateTime": "2025-12-20 18:31:56",
-    "serviceId": "2002300699804364801",
-    "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-    "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-    "description": "3123123123",
-    "visualizable": 0,
-    "toolPrompt": null,
-    "toolType": "api",
-    "datasourceId": null,
-    "sqlTemplate": null,
-    "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-    "resultType": null,
-    "sourceType": null,
-    "trainingTaskId": null,
-    "tableMetadataIds": null,
-    "executionCount": 0,
-    "visualizationConfigs": null,
-    "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-    "outputJsonSchema": null,
-    "lastExecutionTime": null
-  }
-]
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2002300699510763521, 配置数量: 1
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:293] - 开始运行 MCP Server (stdio 模式)
-2025-12-23 18:24:44 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor
-2025-12-23 18:24:44 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0')
-2025-12-23 18:24:45 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x000001B44A911070>
-2025-12-23 18:24:45 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2025-12-23 18:24:45 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest
-2025-12-23 18:24:45 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1
-2025-12-23 18:24:45 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:179] - 处理工具配置: name=weimingminggongdefangshizuoliu001_91bc43ee, description=3123123123...
-2025-12-23 18:24:45 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:183] - 工具 weimingminggongdefangshizuoliu001_91bc43ee 的 inputSchema: {"type": "object", "properties": {"query": {"description": "问题", "type": "string", "format": "paragraph", "maxLength": 3000}}, "required": ["query"]}
-2025-12-23 18:24:45 - lzwcai_workflow_to_mcp.main - INFO - [main.py:193] - ListTools 响应: 返回 1 个工具
-2025-12-23 18:24:45 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-23 18:24:48 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x000001B44A8D15E0>
-2025-12-23 18:24:48 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2025-12-23 18:24:48 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.main - INFO - [main.py:203] - 收到 CallTool 请求: name=weimingminggongdefangshizuoliu001_91bc43ee, arguments={"query": "你好啊"}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:216] - 找到工具配置: {
-  "id": "2002300699812753409",
-  "createBy": "yy8z9",
-  "createTime": "2025-12-20 16:51:02",
-  "updateBy": "yy8z9",
-  "updateTime": "2025-12-20 18:31:56",
-  "serviceId": "2002300699804364801",
-  "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-  "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-  "description": "3123123123",
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-  "toolPrompt": null,
-  "toolType": "api",
-  "datasourceId": null,
-  "sqlTemplate": null,
-  "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-  "resultType": null,
-  "sourceType": null,
-  "trainingTaskId": null,
-  "tableMetadataIds": null,
-  "executionCount": 0,
-  "visualizationConfigs": null,
-  "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-  "outputJsonSchema": null,
-  "lastExecutionTime": null
-}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.main - INFO - [main.py:220] - 使用工作流ID: 2002300699510763521
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.main - INFO - [main.py:228] - 执行工作流请求数据: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:150] - [执行工作流] URL: http://192.168.2.236:8088/open/workflow/execute
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:151] - [执行工作流] Token: wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:152] - [执行工作流] Headers: {"X-API-Key": "wf_buh230o9iaea4n6aefsddcexa7p27ydl", "Content-Type": "application/json", "Accept": "*/*"}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:153] - [执行工作流] 请求参数: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:154] - [执行工作流] 超时时间: 300.0s
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'POST']>
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'POST']>
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'POST']>
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Trace-Id', b'282e2c5e-e68e-48f5-bfdb-1e04ffc18682'), (b'X-RateLimit-Limit', b'100'), (b'X-RateLimit-Remaining', b'99'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json;charset=UTF-8'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Tue, 23 Dec 2025 10:24:48 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-23 18:24:49 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/open/workflow/execute "HTTP/1.1 200 "
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'POST']>
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:164] - [API响应] HTTP 200
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:165] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-trace-id': '282e2c5e-e68e-48f5-bfdb-1e04ffc18682', 'x-ratelimit-limit': '100', 'x-ratelimit-remaining': '99', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json;charset=UTF-8', 'transfer-encoding': 'chunked', 'date': 'Tue, 23 Dec 2025 10:24:48 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:170] - [API响应] 执行工作流成功
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:171] - [API响应] Body: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "1f154317-ca19-4073-b7b5-a2b2beb38a3f",
-    "workflow_id": "3dc9d954-c270-4bbf-aa3d-5945c6acb2f5",
-    "status": "success",
-    "duration": 1.146157,
-    "nodes_executed": 3,
-    "errors_count": 0,
-    "final_output": {
-      "da9400b0-4754-4842-8a5e-dc20e2868cc6": {
-        "query": "你好啊"
-      },
-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"
-      }
-    },
-    "error": null,
-    "log_id": "logs/3dc9d954-c270-4bbf-aa3d-5945c6acb2f5/1f154317-ca19-4073-b7b5-a2b2beb38a3f.json"
-  }
-}
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.main - INFO - [main.py:233] - 工作流执行成功: 2002300699510763521
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:234] - 工作流执行结果: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "1f154317-ca19-4073-b7b5-a2b2beb38a3f",
-    "workflow_id": "3dc9d954-c270-4bbf-aa3d-5945c6acb2f5",
-    "status": "success",
-    "duration": 1.146157,
-    "nodes_executed": 3,
-    "errors_count": 0,
-    "final_output": {
-      "da9400b0-4754-4842-8a5e-dc20e2868cc6": {
-        "query": "你好啊"
-      },
-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"
-      }
-    },
-    "error": null,
-    "log_id": "logs/3dc9d954-c270-4bbf-aa3d-5945c6acb2f5/1f154317-ca19-4073-b7b5-a2b2beb38a3f.json"
-  }
-}
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:294] - 提取 final_output 最后节点成功: key=f54a21ba-e3a3-461e-9a6b-e952087f4a5b
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:295] - 提取结果: {"q": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"}
-2025-12-23 18:24:49 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_daily.log b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_daily.log
deleted file mode 100644
index 198ab0a..0000000
--- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_daily.log
+++ /dev/null
@@ -1,506 +0,0 @@
-2025-12-23 18:19:55 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs
-2025-12-23 18:19:55 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'workflow_mcp_server'
-2025-12-23 18:19:55 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest
-2025-12-23 18:19:55 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:272] - ==================================================
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:273] - Workflow MCP Server 启动
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:274] - ==================================================
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:279] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None}
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:282] - 使用模式: api
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2002300699510763521
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2002300699510763521
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:56] - [客户端初始化] base_url=http://192.168.2.236:8088
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:57] - [客户端初始化] token=wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:58] - [客户端初始化] execute_timeout=300.0s
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:89] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:90] - [API请求] Headers: {'X-API-Key': 'wf_buh230o9iaea4n6aefsddcexa7p27ydl'}
-2025-12-23 18:19:55 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None
-2025-12-23 18:19:55 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x000001E350E119A0>
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'GET']>
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'GET']>
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'GET']>
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Tue, 23 Dec 2025 10:19:54 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-23 18:19:55 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521 "HTTP/1.1 200 "
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'GET']>
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-23 18:19:55 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:97] - [API响应] HTTP 200
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:98] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Tue, 23 Dec 2025 10:19:54 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:103] - [API响应] 获取工作流配置成功: workflow_id=2002300699510763521
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:104] - [API响应] Body: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 18:31:56",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
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-      "toolType": "api",
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-      "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:116] - API 响应原始数据: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 18:31:56",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
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-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [
-  {
-    "id": "2002300699812753409",
-    "createBy": "yy8z9",
-    "createTime": "2025-12-20 16:51:02",
-    "updateBy": "yy8z9",
-    "updateTime": "2025-12-20 18:31:56",
-    "serviceId": "2002300699804364801",
-    "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-    "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-    "description": "3123123123",
-    "visualizable": 0,
-    "toolPrompt": null,
-    "toolType": "api",
-    "datasourceId": null,
-    "sqlTemplate": null,
-    "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-    "resultType": null,
-    "sourceType": null,
-    "trainingTaskId": null,
-    "tableMetadataIds": null,
-    "executionCount": 0,
-    "visualizationConfigs": null,
-    "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-    "outputJsonSchema": null,
-    "lastExecutionTime": null
-  }
-]
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2002300699510763521, 配置数量: 1
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置
-2025-12-23 18:19:55 - lzwcai_workflow_to_mcp.main - INFO - [main.py:290] - 开始运行 MCP Server (stdio 模式)
-2025-12-23 18:19:55 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor
-2025-12-23 18:19:55 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0')
-2025-12-23 18:19:56 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x000001E350E2DC40>
-2025-12-23 18:19:56 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2025-12-23 18:19:56 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest
-2025-12-23 18:19:56 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1
-2025-12-23 18:19:56 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:179] - 处理工具配置: name=weimingminggongdefangshizuoliu001_91bc43ee, description=3123123123...
-2025-12-23 18:19:56 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:183] - 工具 weimingminggongdefangshizuoliu001_91bc43ee 的 inputSchema: {"type": "object", "properties": {"query": {"description": "问题", "type": "string", "format": "paragraph", "maxLength": 3000}}, "required": ["query"]}
-2025-12-23 18:19:56 - lzwcai_workflow_to_mcp.main - INFO - [main.py:193] - ListTools 响应: 返回 1 个工具
-2025-12-23 18:19:56 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-23 18:20:00 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x000001E350DDA3C0>
-2025-12-23 18:20:00 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2025-12-23 18:20:00 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.main - INFO - [main.py:203] - 收到 CallTool 请求: name=weimingminggongdefangshizuoliu001_91bc43ee, arguments={"query": "你好啊"}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:216] - 找到工具配置: {
-  "id": "2002300699812753409",
-  "createBy": "yy8z9",
-  "createTime": "2025-12-20 16:51:02",
-  "updateBy": "yy8z9",
-  "updateTime": "2025-12-20 18:31:56",
-  "serviceId": "2002300699804364801",
-  "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-  "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-  "description": "3123123123",
-  "visualizable": 0,
-  "toolPrompt": null,
-  "toolType": "api",
-  "datasourceId": null,
-  "sqlTemplate": null,
-  "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-  "resultType": null,
-  "sourceType": null,
-  "trainingTaskId": null,
-  "tableMetadataIds": null,
-  "executionCount": 0,
-  "visualizationConfigs": null,
-  "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-  "outputJsonSchema": null,
-  "lastExecutionTime": null
-}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.main - INFO - [main.py:220] - 使用工作流ID: 2002300699510763521
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.main - INFO - [main.py:228] - 执行工作流请求数据: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:150] - [执行工作流] URL: http://192.168.2.236:8088/open/workflow/execute
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:151] - [执行工作流] Token: wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:152] - [执行工作流] Headers: {"X-API-Key": "wf_buh230o9iaea4n6aefsddcexa7p27ydl", "Content-Type": "application/json", "Accept": "*/*"}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:153] - [执行工作流] 请求参数: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-23 18:20:00 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:154] - [执行工作流] 超时时间: 300.0s
-2025-12-23 18:20:00 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'POST']>
-2025-12-23 18:20:00 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-23 18:20:00 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'POST']>
-2025-12-23 18:20:00 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-23 18:20:00 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'POST']>
-2025-12-23 18:20:01 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Trace-Id', b'2c574f18-2d6b-4580-bd8a-eda8c94c26eb'), (b'X-RateLimit-Limit', b'100'), (b'X-RateLimit-Remaining', b'99'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json;charset=UTF-8'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Tue, 23 Dec 2025 10:20:00 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-23 18:20:01 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/open/workflow/execute "HTTP/1.1 200 "
-2025-12-23 18:20:01 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'POST']>
-2025-12-23 18:20:01 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-23 18:20:01 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-23 18:20:01 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:164] - [API响应] HTTP 200
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:165] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-trace-id': '2c574f18-2d6b-4580-bd8a-eda8c94c26eb', 'x-ratelimit-limit': '100', 'x-ratelimit-remaining': '99', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json;charset=UTF-8', 'transfer-encoding': 'chunked', 'date': 'Tue, 23 Dec 2025 10:20:00 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:170] - [API响应] 执行工作流成功
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:171] - [API响应] Body: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "d8ff9fc7-530c-4b77-a8a8-07d51aa79870",
-    "workflow_id": "3dc9d954-c270-4bbf-aa3d-5945c6acb2f5",
-    "status": "success",
-    "duration": 0.926932,
-    "nodes_executed": 3,
-    "errors_count": 0,
-    "final_output": {
-      "da9400b0-4754-4842-8a5e-dc20e2868cc6": {
-        "query": "你好啊"
-      },
-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好！看起来你可能感兴趣于某方面，但还没具体说明？如果需要帮助，尽管告诉我，我很乐意帮忙解答或讨论各种话题！😊"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好！看起来你可能感兴趣于某方面，但还没具体说明？如果需要帮助，尽管告诉我，我很乐意帮忙解答或讨论各种话题！😊"
-      }
-    },
-    "error": null,
-    "log_id": "logs/3dc9d954-c270-4bbf-aa3d-5945c6acb2f5/d8ff9fc7-530c-4b77-a8a8-07d51aa79870.json"
-  }
-}
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.main - INFO - [main.py:233] - 工作流执行成功: 2002300699510763521
-2025-12-23 18:20:01 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:234] - 工作流执行结果: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "d8ff9fc7-530c-4b77-a8a8-07d51aa79870",
-    "workflow_id": "3dc9d954-c270-4bbf-aa3d-5945c6acb2f5",
-    "status": "success",
-    "duration": 0.926932,
-    "nodes_executed": 3,
-    "errors_count": 0,
-    "final_output": {
-      "da9400b0-4754-4842-8a5e-dc20e2868cc6": {
-        "query": "你好啊"
-      },
-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好！看起来你可能感兴趣于某方面，但还没具体说明？如果需要帮助，尽管告诉我，我很乐意帮忙解答或讨论各种话题！😊"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好！看起来你可能感兴趣于某方面，但还没具体说明？如果需要帮助，尽管告诉我，我很乐意帮忙解答或讨论各种话题！😊"
-      }
-    },
-    "error": null,
-    "log_id": "logs/3dc9d954-c270-4bbf-aa3d-5945c6acb2f5/d8ff9fc7-530c-4b77-a8a8-07d51aa79870.json"
-  }
-}
-2025-12-23 18:20:01 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-23 18:24:44 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs
-2025-12-23 18:24:44 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'workflow_mcp_server'
-2025-12-23 18:24:44 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest
-2025-12-23 18:24:44 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:275] - ==================================================
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:276] - Workflow MCP Server 启动
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:277] - ==================================================
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:282] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None}
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:285] - 使用模式: api
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2002300699510763521
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2002300699510763521
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:56] - [客户端初始化] base_url=http://192.168.2.236:8088
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:57] - [客户端初始化] token=wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:58] - [客户端初始化] execute_timeout=300.0s
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:89] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:90] - [API请求] Headers: {'X-API-Key': 'wf_buh230o9iaea4n6aefsddcexa7p27ydl'}
-2025-12-23 18:24:44 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None
-2025-12-23 18:24:44 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x000001B44A89F380>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'GET']>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'GET']>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'GET']>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Tue, 23 Dec 2025 10:24:43 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-23 18:24:44 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521 "HTTP/1.1 200 "
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'GET']>
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-23 18:24:44 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:97] - [API响应] HTTP 200
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:98] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Tue, 23 Dec 2025 10:24:43 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:103] - [API响应] 获取工作流配置成功: workflow_id=2002300699510763521
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:104] - [API响应] Body: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 18:31:56",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
-      "datasourceId": null,
-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:116] - API 响应原始数据: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 18:31:56",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
-      "datasourceId": null,
-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [
-  {
-    "id": "2002300699812753409",
-    "createBy": "yy8z9",
-    "createTime": "2025-12-20 16:51:02",
-    "updateBy": "yy8z9",
-    "updateTime": "2025-12-20 18:31:56",
-    "serviceId": "2002300699804364801",
-    "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-    "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-    "description": "3123123123",
-    "visualizable": 0,
-    "toolPrompt": null,
-    "toolType": "api",
-    "datasourceId": null,
-    "sqlTemplate": null,
-    "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-    "resultType": null,
-    "sourceType": null,
-    "trainingTaskId": null,
-    "tableMetadataIds": null,
-    "executionCount": 0,
-    "visualizationConfigs": null,
-    "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-    "outputJsonSchema": null,
-    "lastExecutionTime": null
-  }
-]
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2002300699510763521, 配置数量: 1
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置
-2025-12-23 18:24:44 - lzwcai_workflow_to_mcp.main - INFO - [main.py:293] - 开始运行 MCP Server (stdio 模式)
-2025-12-23 18:24:44 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor
-2025-12-23 18:24:44 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0')
-2025-12-23 18:24:45 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x000001B44A911070>
-2025-12-23 18:24:45 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2025-12-23 18:24:45 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest
-2025-12-23 18:24:45 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1
-2025-12-23 18:24:45 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:179] - 处理工具配置: name=weimingminggongdefangshizuoliu001_91bc43ee, description=3123123123...
-2025-12-23 18:24:45 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:183] - 工具 weimingminggongdefangshizuoliu001_91bc43ee 的 inputSchema: {"type": "object", "properties": {"query": {"description": "问题", "type": "string", "format": "paragraph", "maxLength": 3000}}, "required": ["query"]}
-2025-12-23 18:24:45 - lzwcai_workflow_to_mcp.main - INFO - [main.py:193] - ListTools 响应: 返回 1 个工具
-2025-12-23 18:24:45 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-23 18:24:48 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x000001B44A8D15E0>
-2025-12-23 18:24:48 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2025-12-23 18:24:48 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.main - INFO - [main.py:203] - 收到 CallTool 请求: name=weimingminggongdefangshizuoliu001_91bc43ee, arguments={"query": "你好啊"}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:216] - 找到工具配置: {
-  "id": "2002300699812753409",
-  "createBy": "yy8z9",
-  "createTime": "2025-12-20 16:51:02",
-  "updateBy": "yy8z9",
-  "updateTime": "2025-12-20 18:31:56",
-  "serviceId": "2002300699804364801",
-  "uniqueName": "3123123123-workflow服务_weimingminggongdefangshizuoliu001_91bc43ee",
-  "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-  "description": "3123123123",
-  "visualizable": 0,
-  "toolPrompt": null,
-  "toolType": "api",
-  "datasourceId": null,
-  "sqlTemplate": null,
-  "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-  "resultType": null,
-  "sourceType": null,
-  "trainingTaskId": null,
-  "tableMetadataIds": null,
-  "executionCount": 0,
-  "visualizationConfigs": null,
-  "inputJsonSchema": "{\"type\":\"object\",\"properties\":{\"query\":{\"description\":\"问题\",\"type\":\"string\",\"format\":\"paragraph\",\"maxLength\":3000}},\"required\":[\"query\"]}",
-  "outputJsonSchema": null,
-  "lastExecutionTime": null
-}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.main - INFO - [main.py:220] - 使用工作流ID: 2002300699510763521
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.main - INFO - [main.py:228] - 执行工作流请求数据: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:150] - [执行工作流] URL: http://192.168.2.236:8088/open/workflow/execute
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:151] - [执行工作流] Token: wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:152] - [执行工作流] Headers: {"X-API-Key": "wf_buh230o9iaea4n6aefsddcexa7p27ydl", "Content-Type": "application/json", "Accept": "*/*"}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:153] - [执行工作流] 请求参数: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-23 18:24:48 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:154] - [执行工作流] 超时时间: 300.0s
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'POST']>
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'POST']>
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-23 18:24:48 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'POST']>
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Trace-Id', b'282e2c5e-e68e-48f5-bfdb-1e04ffc18682'), (b'X-RateLimit-Limit', b'100'), (b'X-RateLimit-Remaining', b'99'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json;charset=UTF-8'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Tue, 23 Dec 2025 10:24:48 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-23 18:24:49 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/open/workflow/execute "HTTP/1.1 200 "
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'POST']>
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-23 18:24:49 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:164] - [API响应] HTTP 200
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:165] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-trace-id': '282e2c5e-e68e-48f5-bfdb-1e04ffc18682', 'x-ratelimit-limit': '100', 'x-ratelimit-remaining': '99', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json;charset=UTF-8', 'transfer-encoding': 'chunked', 'date': 'Tue, 23 Dec 2025 10:24:48 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:170] - [API响应] 执行工作流成功
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:171] - [API响应] Body: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "1f154317-ca19-4073-b7b5-a2b2beb38a3f",
-    "workflow_id": "3dc9d954-c270-4bbf-aa3d-5945c6acb2f5",
-    "status": "success",
-    "duration": 1.146157,
-    "nodes_executed": 3,
-    "errors_count": 0,
-    "final_output": {
-      "da9400b0-4754-4842-8a5e-dc20e2868cc6": {
-        "query": "你好啊"
-      },
-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"
-      }
-    },
-    "error": null,
-    "log_id": "logs/3dc9d954-c270-4bbf-aa3d-5945c6acb2f5/1f154317-ca19-4073-b7b5-a2b2beb38a3f.json"
-  }
-}
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.main - INFO - [main.py:233] - 工作流执行成功: 2002300699510763521
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:234] - 工作流执行结果: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "1f154317-ca19-4073-b7b5-a2b2beb38a3f",
-    "workflow_id": "3dc9d954-c270-4bbf-aa3d-5945c6acb2f5",
-    "status": "success",
-    "duration": 1.146157,
-    "nodes_executed": 3,
-    "errors_count": 0,
-    "final_output": {
-      "da9400b0-4754-4842-8a5e-dc20e2868cc6": {
-        "query": "你好啊"
-      },
-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"
-      }
-    },
-    "error": null,
-    "log_id": "logs/3dc9d954-c270-4bbf-aa3d-5945c6acb2f5/1f154317-ca19-4073-b7b5-a2b2beb38a3f.json"
-  }
-}
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:294] - 提取 final_output 最后节点成功: key=f54a21ba-e3a3-461e-9a6b-e952087f4a5b
-2025-12-23 18:24:49 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:295] - 提取结果: {"q": "你好！很高兴见到你。我注意到你用了特别的表情符号，是想表达亲切吗？🤗 如果你有任何问题或需要帮助，随时告诉我哦。今天想聊些什么呢？"}
-2025-12-23 18:24:49 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_daily.log.2025-12-20 b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_daily.log.2025-12-20
deleted file mode 100644
index b9c9ce5..0000000
--- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_daily.log.2025-12-20
+++ /dev/null
@@ -1,473 +0,0 @@
-2025-12-20 18:03:07 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs
-2025-12-20 18:03:07 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'workflow_mcp_server'
-2025-12-20 18:03:07 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest
-2025-12-20 18:03:07 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:272] - ==================================================
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:273] - Workflow MCP Server 启动
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:274] - ==================================================
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:279] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None}
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:282] - 使用模式: api
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2002300699510763521
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2002300699510763521
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:56] - [客户端初始化] base_url=http://192.168.2.236:8088
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:57] - [客户端初始化] token=wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:58] - [客户端初始化] execute_timeout=300.0s
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:89] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:90] - [API请求] Headers: {'X-API-Key': 'Bearer wf_buh230o9iaea4n6aefsddcexa7p27ydl'}
-2025-12-20 18:03:07 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None
-2025-12-20 18:03:07 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x00000155217CC380>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'GET']>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'GET']>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'GET']>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Sat, 20 Dec 2025 10:03:06 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-20 18:03:07 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521 "HTTP/1.1 200 "
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'GET']>
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-20 18:03:07 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:97] - [API响应] HTTP 200
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:98] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Sat, 20 Dec 2025 10:03:06 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:103] - [API响应] 获取工作流配置成功: workflow_id=2002300699510763521
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:104] - [API响应] Body: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 17:59:26",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "未命名工的方式作流001",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
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-      "toolType": "api",
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-      "tableMetadataIds": null,
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-      "inputJsonSchema": null,
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:116] - API 响应原始数据: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 17:59:26",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "未命名工的方式作流001",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
-      "datasourceId": null,
-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": null,
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [
-  {
-    "id": "2002300699812753409",
-    "createBy": "yy8z9",
-    "createTime": "2025-12-20 16:51:02",
-    "updateBy": "yy8z9",
-    "updateTime": "2025-12-20 17:59:26",
-    "serviceId": "2002300699804364801",
-    "uniqueName": "未命名工的方式作流001",
-    "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-    "description": "3123123123",
-    "visualizable": 0,
-    "toolPrompt": null,
-    "toolType": "api",
-    "datasourceId": null,
-    "sqlTemplate": null,
-    "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-    "resultType": null,
-    "sourceType": null,
-    "trainingTaskId": null,
-    "tableMetadataIds": null,
-    "executionCount": 0,
-    "visualizationConfigs": null,
-    "inputJsonSchema": null,
-    "outputJsonSchema": null,
-    "lastExecutionTime": null
-  }
-]
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2002300699510763521, 配置数量: 1
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置
-2025-12-20 18:03:07 - lzwcai_workflow_to_mcp.main - INFO - [main.py:290] - 开始运行 MCP Server (stdio 模式)
-2025-12-20 18:03:07 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor
-2025-12-20 18:03:07 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0')
-2025-12-20 18:03:08 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x000001552178D700>
-2025-12-20 18:03:08 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2025-12-20 18:03:08 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest
-2025-12-20 18:03:08 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1
-2025-12-20 18:03:08 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:179] - 处理工具配置: name=weimingminggongdefangshizuoliu001_91bc43ee, description=3123123123...
-2025-12-20 18:03:08 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:183] - 工具 weimingminggongdefangshizuoliu001_91bc43ee 的 inputSchema: {"type": "object", "properties": {"query": {"description": "问题", "type": "string", "format": "paragraph", "maxLength": 3000}}, "required": ["query"]}
-2025-12-20 18:03:08 - lzwcai_workflow_to_mcp.main - INFO - [main.py:193] - ListTools 响应: 返回 1 个工具
-2025-12-20 18:03:08 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-20 18:03:12 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x00000155218D4A70>
-2025-12-20 18:03:12 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2025-12-20 18:03:12 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - INFO - [main.py:203] - 收到 CallTool 请求: name=weimingminggongdefangshizuoliu001_91bc43ee, arguments={"query": "你好啊"}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:216] - 找到工具配置: {
-  "id": "2002300699812753409",
-  "createBy": "yy8z9",
-  "createTime": "2025-12-20 16:51:02",
-  "updateBy": "yy8z9",
-  "updateTime": "2025-12-20 17:59:26",
-  "serviceId": "2002300699804364801",
-  "uniqueName": "未命名工的方式作流001",
-  "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-  "description": "3123123123",
-  "visualizable": 0,
-  "toolPrompt": null,
-  "toolType": "api",
-  "datasourceId": null,
-  "sqlTemplate": null,
-  "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-  "resultType": null,
-  "sourceType": null,
-  "trainingTaskId": null,
-  "tableMetadataIds": null,
-  "executionCount": 0,
-  "visualizationConfigs": null,
-  "inputJsonSchema": null,
-  "outputJsonSchema": null,
-  "lastExecutionTime": null
-}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - INFO - [main.py:220] - 使用工作流ID: 2002300699510763521
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - INFO - [main.py:228] - 执行工作流请求数据: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:150] - [执行工作流] URL: http://192.168.2.236:8088/open/workflow/execute
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:151] - [执行工作流] Token: wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:152] - [执行工作流] Headers: {"X-API-Key": "Bearer wf_buh230o9iaea4n6aefsddcexa7p27ydl", "Content-Type": "application/json", "Accept": "*/*"}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:153] - [执行工作流] 请求参数: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:154] - [执行工作流] 超时时间: 300.0s
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'POST']>
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'POST']>
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'POST']>
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 401, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Trace-Id', b'40f82dcb-2055-46b4-a9ae-1bbee5591306'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json;charset=UTF-8'), (b'Content-Length', b'61'), (b'Date', b'Sat, 20 Dec 2025 10:03:10 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-20 18:03:12 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/open/workflow/execute "HTTP/1.1 401 "
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'POST']>
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-20 18:03:12 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:164] - [API响应] HTTP 401
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:165] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-trace-id': '40f82dcb-2055-46b4-a9ae-1bbee5591306', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json;charset=UTF-8', 'content-length': '61', 'date': 'Sat, 20 Dec 2025 10:03:10 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - ERROR - [api_client.py:182] - [API错误] 执行工作流API请求失败 (HTTP 401): http://192.168.2.236:8088/open/workflow/execute
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - ERROR - [api_client.py:183] - [API错误] 响应内容: {"msg":"API Key无效","code":401,"errorCode":"UNAUTHORIZED"}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - ERROR - [main.py:236] - 工作流执行失败: 执行工作流API请求失败 (HTTP 401): http://192.168.2.236:8088/open/workflow/execute
-Traceback (most recent call last):
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\utils\api_client.py", line 167, in execute_workflow
-    response.raise_for_status()
-  File "D:\anaconda3\Lib\site-packages\httpx\_models.py", line 829, in raise_for_status
-    raise HTTPStatusError(message, request=request, response=self)
-httpx.HTTPStatusError: Client error '401 ' for url 'http://192.168.2.236:8088/open/workflow/execute'
-For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/401
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\main.py", line 232, in handle_call_tool
-    result = execute_workflow(request_data)
-             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\utils\api_client.py", line 268, in execute_workflow
-    return default_client.execute_workflow(request_data)
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\utils\api_client.py", line 184, in execute_workflow
-    raise Exception(error_msg)
-Exception: 执行工作流API请求失败 (HTTP 401): http://192.168.2.236:8088/open/workflow/execute
-2025-12-20 18:03:12 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-20 18:04:18 - root - INFO - [logger_config.py:117] - 日志系统初始化完成 - 日志目录: E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\logs
-2025-12-20 18:04:18 - mcp.server.lowlevel.server - DEBUG - [server.py:154] - Initializing server 'workflow_mcp_server'
-2025-12-20 18:04:18 - mcp.server.lowlevel.server - DEBUG - [server.py:380] - Registering handler for ListToolsRequest
-2025-12-20 18:04:18 - mcp.server.lowlevel.server - DEBUG - [server.py:441] - Registering handler for CallToolRequest
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:272] - ==================================================
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:273] - Workflow MCP Server 启动
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:274] - ==================================================
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:279] - 命令行参数: {'mode': 'api', 'json_path': None, 'workflow_id': None}
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:282] - 使用模式: api
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:103] - ApiLoader 初始化，工作流ID: 2002300699510763521
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:111] - 开始从 API 加载工作流配置，工作流ID: 2002300699510763521
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:56] - [客户端初始化] base_url=http://192.168.2.236:8088
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:57] - [客户端初始化] token=wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:58] - [客户端初始化] execute_timeout=300.0s
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:89] - [API请求] GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:90] - [API请求] Headers: {'X-API-Key': 'wf_buh230o9iaea4n6aefsddcexa7p27ydl'}
-2025-12-20 18:04:18 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.started host='192.168.2.236' port=8088 local_address=None timeout=30.0 socket_options=None
-2025-12-20 18:04:18 - httpcore.connection - DEBUG - [_trace.py:45] - connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x0000025DD0ED1F70>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'GET']>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'GET']>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'GET']>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Sat, 20 Dec 2025 10:04:16 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-20 18:04:18 - httpx - INFO - [_client.py:1025] - HTTP Request: GET http://192.168.2.236:8088/system/workflowManage/getByWorkflowId/2002300699510763521 "HTTP/1.1 200 "
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'GET']>
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-20 18:04:18 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:97] - [API响应] HTTP 200
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:98] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'date': 'Sat, 20 Dec 2025 10:04:16 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:103] - [API响应] 获取工作流配置成功: workflow_id=2002300699510763521
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:104] - [API响应] Body: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 17:59:26",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "未命名工的方式作流001",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
-      "datasourceId": null,
-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": null,
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:116] - API 响应原始数据: {
-  "msg": "查询成功",
-  "code": 200,
-  "data": [
-    {
-      "id": "2002300699812753409",
-      "createBy": "yy8z9",
-      "createTime": "2025-12-20 16:51:02",
-      "updateBy": "yy8z9",
-      "updateTime": "2025-12-20 17:59:26",
-      "serviceId": "2002300699804364801",
-      "uniqueName": "未命名工的方式作流001",
-      "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-      "description": "3123123123",
-      "visualizable": 0,
-      "toolPrompt": null,
-      "toolType": "api",
-      "datasourceId": null,
-      "sqlTemplate": null,
-      "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-      "resultType": null,
-      "sourceType": null,
-      "trainingTaskId": null,
-      "tableMetadataIds": null,
-      "executionCount": 0,
-      "visualizationConfigs": null,
-      "inputJsonSchema": null,
-      "outputJsonSchema": null,
-      "lastExecutionTime": null
-    }
-  ]
-}
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:124] - API 响应 data 字段: [
-  {
-    "id": "2002300699812753409",
-    "createBy": "yy8z9",
-    "createTime": "2025-12-20 16:51:02",
-    "updateBy": "yy8z9",
-    "updateTime": "2025-12-20 17:59:26",
-    "serviceId": "2002300699804364801",
-    "uniqueName": "未命名工的方式作流001",
-    "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-    "description": "3123123123",
-    "visualizable": 0,
-    "toolPrompt": null,
-    "toolType": "api",
-    "datasourceId": null,
-    "sqlTemplate": null,
-    "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-    "resultType": null,
-    "sourceType": null,
-    "trainingTaskId": null,
-    "tableMetadataIds": null,
-    "executionCount": 0,
-    "visualizationConfigs": null,
-    "inputJsonSchema": null,
-    "outputJsonSchema": null,
-    "lastExecutionTime": null
-  }
-]
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:135] - 从 API 加载工作流配置成功，工作流ID: 2002300699510763521, 配置数量: 1
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:165] - 已加载 1 个工具配置
-2025-12-20 18:04:18 - lzwcai_workflow_to_mcp.main - INFO - [main.py:290] - 开始运行 MCP Server (stdio 模式)
-2025-12-20 18:04:18 - asyncio - DEBUG - [proactor_events.py:634] - Using proactor: IocpProactor
-2025-12-20 18:04:18 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: root=InitializedNotification(method='notifications/initialized', params=None, jsonrpc='2.0')
-2025-12-20 18:04:19 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x0000025DCF92FF50>
-2025-12-20 18:04:19 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type ListToolsRequest
-2025-12-20 18:04:19 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type ListToolsRequest
-2025-12-20 18:04:19 - lzwcai_workflow_to_mcp.main - INFO - [main.py:171] - 收到 ListTools 请求，当前配置数量: 1
-2025-12-20 18:04:19 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:179] - 处理工具配置: name=weimingminggongdefangshizuoliu001_91bc43ee, description=3123123123...
-2025-12-20 18:04:19 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:183] - 工具 weimingminggongdefangshizuoliu001_91bc43ee 的 inputSchema: {"type": "object", "properties": {"query": {"description": "问题", "type": "string", "format": "paragraph", "maxLength": 3000}}, "required": ["query"]}
-2025-12-20 18:04:19 - lzwcai_workflow_to_mcp.main - INFO - [main.py:193] - ListTools 响应: 返回 1 个工具
-2025-12-20 18:04:19 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
-2025-12-20 18:04:22 - mcp.server.lowlevel.server - DEBUG - [server.py:582] - Received message: <mcp.shared.session.RequestResponder object at 0x0000025DD0D21340>
-2025-12-20 18:04:22 - mcp.server.lowlevel.server - INFO - [server.py:619] - Processing request of type CallToolRequest
-2025-12-20 18:04:22 - mcp.server.lowlevel.server - DEBUG - [server.py:621] - Dispatching request of type CallToolRequest
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.main - INFO - [main.py:203] - 收到 CallTool 请求: name=weimingminggongdefangshizuoliu001_91bc43ee, arguments={"query": "你好啊"}
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:216] - 找到工具配置: {
-  "id": "2002300699812753409",
-  "createBy": "yy8z9",
-  "createTime": "2025-12-20 16:51:02",
-  "updateBy": "yy8z9",
-  "updateTime": "2025-12-20 17:59:26",
-  "serviceId": "2002300699804364801",
-  "uniqueName": "未命名工的方式作流001",
-  "name": "weimingminggongdefangshizuoliu001_91bc43ee",
-  "description": "3123123123",
-  "visualizable": 0,
-  "toolPrompt": null,
-  "toolType": "api",
-  "datasourceId": null,
-  "sqlTemplate": null,
-  "sqlParams": "[{\"type\":\"paragraph\",\"name\":\"query\",\"displayName\":\"问题\",\"maxLength\":3000,\"defaultValue\":\"\",\"required\":true}]",
-  "resultType": null,
-  "sourceType": null,
-  "trainingTaskId": null,
-  "tableMetadataIds": null,
-  "executionCount": 0,
-  "visualizationConfigs": null,
-  "inputJsonSchema": null,
-  "outputJsonSchema": null,
-  "lastExecutionTime": null
-}
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.main - INFO - [main.py:220] - 使用工作流ID: 2002300699510763521
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.main - INFO - [main.py:228] - 执行工作流请求数据: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:150] - [执行工作流] URL: http://192.168.2.236:8088/open/workflow/execute
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:151] - [执行工作流] Token: wf_buh230o9iaea4n6aefsddcexa7p27ydl
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:152] - [执行工作流] Headers: {"X-API-Key": "wf_buh230o9iaea4n6aefsddcexa7p27ydl", "Content-Type": "application/json", "Accept": "*/*"}
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:153] - [执行工作流] 请求参数: {
-  "workflowId": "2002300699510763521",
-  "inputs": {
-    "query": "你好啊"
-  }
-}
-2025-12-20 18:04:22 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:154] - [执行工作流] 超时时间: 300.0s
-2025-12-20 18:04:22 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.started request=<Request [b'POST']>
-2025-12-20 18:04:22 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_headers.complete
-2025-12-20 18:04:22 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.started request=<Request [b'POST']>
-2025-12-20 18:04:22 - httpcore.http11 - DEBUG - [_trace.py:45] - send_request_body.complete
-2025-12-20 18:04:22 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.started request=<Request [b'POST']>
-2025-12-20 18:04:24 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'', [(b'Vary', b'Origin'), (b'Vary', b'Access-Control-Request-Method'), (b'Vary', b'Access-Control-Request-Headers'), (b'X-Trace-Id', b'62311551-e7ab-4d5d-adef-f59b1969a4cf'), (b'X-RateLimit-Limit', b'100'), (b'X-RateLimit-Remaining', b'99'), (b'X-Content-Type-Options', b'nosniff'), (b'X-XSS-Protection', b'1; mode=block'), (b'X-Frame-Options', b'SAMEORIGIN'), (b'Content-Type', b'application/json;charset=UTF-8'), (b'Transfer-Encoding', b'chunked'), (b'Date', b'Sat, 20 Dec 2025 10:04:22 GMT'), (b'Keep-Alive', b'timeout=60'), (b'Connection', b'keep-alive')])
-2025-12-20 18:04:24 - httpx - INFO - [_client.py:1025] - HTTP Request: POST http://192.168.2.236:8088/open/workflow/execute "HTTP/1.1 200 "
-2025-12-20 18:04:24 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.started request=<Request [b'POST']>
-2025-12-20 18:04:24 - httpcore.http11 - DEBUG - [_trace.py:45] - receive_response_body.complete
-2025-12-20 18:04:24 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.started
-2025-12-20 18:04:24 - httpcore.http11 - DEBUG - [_trace.py:45] - response_closed.complete
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:164] - [API响应] HTTP 200
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:165] - [API响应] Headers: {'vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers', 'x-trace-id': '62311551-e7ab-4d5d-adef-f59b1969a4cf', 'x-ratelimit-limit': '100', 'x-ratelimit-remaining': '99', 'x-content-type-options': 'nosniff', 'x-xss-protection': '1; mode=block', 'x-frame-options': 'SAMEORIGIN', 'content-type': 'application/json;charset=UTF-8', 'transfer-encoding': 'chunked', 'date': 'Sat, 20 Dec 2025 10:04:22 GMT', 'keep-alive': 'timeout=60', 'connection': 'keep-alive'}
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.utils.api_client - INFO - [api_client.py:170] - [API响应] 执行工作流成功
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.utils.api_client - DEBUG - [api_client.py:171] - [API响应] Body: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "e6dcd942-1592-45bb-8af7-d7d25df338ed",
-    "workflow_id": "3dc9d954-c270-4bbf-aa3d-5945c6acb2f5",
-    "status": "success",
-    "duration": 2.206175,
-    "nodes_executed": 3,
-    "errors_count": 0,
-    "final_output": {
-      "da9400b0-4754-4842-8a5e-dc20e2868cc6": {
-        "query": "你好啊"
-      },
-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好呀！✨ 很高兴见到你！今天过得怎么样呀？希望你度过了愉快的一天。我今天刚整理完知识库，准备了很多有趣的话题和实用的信息，希望能和你一起探讨、学习。不知道你今天想聊些什么呢？无论是有趣的故事、需要解决的问题，还是纯粹想聊聊日常，我都很乐意陪伴你聊天哦！😊"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好呀！✨ 很高兴见到你！今天过得怎么样呀？希望你度过了愉快的一天。我今天刚整理完知识库，准备了很多有趣的话题和实用的信息，希望能和你一起探讨、学习。不知道你今天想聊些什么呢？无论是有趣的故事、需要解决的问题，还是纯粹想聊聊日常，我都很乐意陪伴你聊天哦！😊"
-      }
-    },
-    "error": null,
-    "log_id": "logs/3dc9d954-c270-4bbf-aa3d-5945c6acb2f5/e6dcd942-1592-45bb-8af7-d7d25df338ed.json"
-  }
-}
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.main - INFO - [main.py:233] - 工作流执行成功: 2002300699510763521
-2025-12-20 18:04:24 - lzwcai_workflow_to_mcp.main - DEBUG - [main.py:234] - 工作流执行结果: {
-  "msg": "操作成功",
-  "code": 200,
-  "data": {
-    "success": true,
-    "execution_id": "e6dcd942-1592-45bb-8af7-d7d25df338ed",
-    "workflow_id": "3dc9d954-c270-4bbf-aa3d-5945c6acb2f5",
-    "status": "success",
-    "duration": 2.206175,
-    "nodes_executed": 3,
-    "errors_count": 0,
-    "final_output": {
-      "da9400b0-4754-4842-8a5e-dc20e2868cc6": {
-        "query": "你好啊"
-      },
-      "a480a344-7fa9-439b-8db6-79b136a79736": {
-        "text": "你好呀！✨ 很高兴见到你！今天过得怎么样呀？希望你度过了愉快的一天。我今天刚整理完知识库，准备了很多有趣的话题和实用的信息，希望能和你一起探讨、学习。不知道你今天想聊些什么呢？无论是有趣的故事、需要解决的问题，还是纯粹想聊聊日常，我都很乐意陪伴你聊天哦！😊"
-      },
-      "f54a21ba-e3a3-461e-9a6b-e952087f4a5b": {
-        "q": "你好呀！✨ 很高兴见到你！今天过得怎么样呀？希望你度过了愉快的一天。我今天刚整理完知识库，准备了很多有趣的话题和实用的信息，希望能和你一起探讨、学习。不知道你今天想聊些什么呢？无论是有趣的故事、需要解决的问题，还是纯粹想聊聊日常，我都很乐意陪伴你聊天哦！😊"
-      }
-    },
-    "error": null,
-    "log_id": "logs/3dc9d954-c270-4bbf-aa3d-5945c6acb2f5/e6dcd942-1592-45bb-8af7-d7d25df338ed.json"
-  }
-}
-2025-12-20 18:04:24 - mcp.server.lowlevel.server - DEBUG - [server.py:662] - Response sent
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_error.log b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_error.log
deleted file mode 100644
index a0a8755..0000000
--- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/logs/lzwcai_workflow_to_mcp_error.log
+++ /dev/null
@@ -1,23 +0,0 @@
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - ERROR - [api_client.py:182] - [API错误] 执行工作流API请求失败 (HTTP 401): http://192.168.2.236:8088/open/workflow/execute
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.utils.api_client - ERROR - [api_client.py:183] - [API错误] 响应内容: {"msg":"API Key无效","code":401,"errorCode":"UNAUTHORIZED"}
-2025-12-20 18:03:12 - lzwcai_workflow_to_mcp.main - ERROR - [main.py:236] - 工作流执行失败: 执行工作流API请求失败 (HTTP 401): http://192.168.2.236:8088/open/workflow/execute
-Traceback (most recent call last):
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\utils\api_client.py", line 167, in execute_workflow
-    response.raise_for_status()
-  File "D:\anaconda3\Lib\site-packages\httpx\_models.py", line 829, in raise_for_status
-    raise HTTPStatusError(message, request=request, response=self)
-httpx.HTTPStatusError: Client error '401 ' for url 'http://192.168.2.236:8088/open/workflow/execute'
-For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/401
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\main.py", line 232, in handle_call_tool
-    result = execute_workflow(request_data)
-             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\utils\api_client.py", line 268, in execute_workflow
-    return default_client.execute_workflow(request_data)
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "E:\yh-ai\project\lzwcai-szyg\lzwcai-mcp-server-package\lzwcai_workflow_to_mcp\lzwcai_workflow_to_mcp\utils\api_client.py", line 184, in execute_workflow
-    raise Exception(error_msg)
-Exception: 执行工作流API请求失败 (HTTP 401): http://192.168.2.236:8088/open/workflow/execute
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__init__.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__init__.py
index 53da55d..892d416 100644
--- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__init__.py
+++ b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__init__.py
@@ -10,7 +10,10 @@ from .api_client import (
     process_workflow_response,
     get_default_client,
     DEFAULT_TIMEOUT,
-    WORKFLOW_EXECUTE_TIMEOUT
+    WORKFLOW_EXECUTE_TIMEOUT,
+    DEFAULT_POLLING_INTERVAL,
+    DEFAULT_MAX_POLL_COUNT,
+    DEFAULT_MAX_ERROR_COUNT,
 )
 from .env_config import get_workflow_id, get_backend_base_url, get_env_config, set_env_variable
 
@@ -26,6 +29,9 @@ __all__ = [
     'get_default_client',
     'DEFAULT_TIMEOUT',
     'WORKFLOW_EXECUTE_TIMEOUT',
+    'DEFAULT_POLLING_INTERVAL',
+    'DEFAULT_MAX_POLL_COUNT',
+    'DEFAULT_MAX_ERROR_COUNT',
     'get_workflow_id',
     'get_backend_base_url',
     'get_env_config',
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__pycache__/__init__.cpython-312.pyc b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__pycache__/__init__.cpython-312.pyc
index a0546a9..2000b8d 100644
Binary files a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__pycache__/__init__.cpython-312.pyc and b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__pycache__/__init__.cpython-312.pyc differ
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__pycache__/api_client.cpython-312.pyc b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__pycache__/api_client.cpython-312.pyc
index eea50fb..d25d8eb 100644
Binary files a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__pycache__/api_client.cpython-312.pyc and b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/__pycache__/api_client.cpython-312.pyc differ
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/api_client.py b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/api_client.py
index 8658ef0..cedb2a2 100644
--- a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/api_client.py
+++ b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/utils/api_client.py
@@ -5,7 +5,9 @@
 
 import httpx
 import json
-from typing import Dict, Any, Optional, List
+import os
+import time
+from typing import Dict, Any, Optional, List, Tuple
 
 try:
     from .env_config import get_backend_base_url, get_workflow_execute_key
@@ -18,7 +20,64 @@ logger = get_logger(__name__)
 
 # 默认超时配置（秒）
 DEFAULT_TIMEOUT = 30.0  # 普通请求超时
-WORKFLOW_EXECUTE_TIMEOUT = 300.0  # 工作流执行超时（5分钟）
+WORKFLOW_EXECUTE_TIMEOUT = 600.0  # 工作流执行超时（10分钟，参考 workflow-execution-api.md）
+
+# 异步轮询默认配置（参考 workflow-execution-api.md）
+DEFAULT_POLLING_INTERVAL = 1.0
+DEFAULT_MAX_POLL_COUNT = 120
+DEFAULT_MAX_ERROR_COUNT = 3
+
+
+def _get_polling_config() -> Tuple[float, int, int]:
+    """
+    获取轮询配置（允许用环境变量覆盖）
+
+    Env:
+      - workflowPollingIntervalSeconds: float, 默认 1
+      - workflowMaxPollCount: int, 默认 120
+      - workflowMaxErrorCount: int, 默认 3
+    """
+    try:
+        interval = float(os.environ.get("workflowPollingIntervalSeconds", str(DEFAULT_POLLING_INTERVAL)))
+    except ValueError:
+        interval = DEFAULT_POLLING_INTERVAL
+
+    try:
+        max_polls = int(os.environ.get("workflowMaxPollCount", str(DEFAULT_MAX_POLL_COUNT)))
+    except ValueError:
+        max_polls = DEFAULT_MAX_POLL_COUNT
+
+    try:
+        max_errors = int(os.environ.get("workflowMaxErrorCount", str(DEFAULT_MAX_ERROR_COUNT)))
+    except ValueError:
+        max_errors = DEFAULT_MAX_ERROR_COUNT
+
+    # 避免被错误配置成 0 或负数
+    interval = interval if interval > 0 else DEFAULT_POLLING_INTERVAL
+    max_polls = max_polls if max_polls > 0 else DEFAULT_MAX_POLL_COUNT
+    max_errors = max_errors if max_errors > 0 else DEFAULT_MAX_ERROR_COUNT
+
+    return interval, max_polls, max_errors
+
+
+def _extract_async_ids(execute_response: Dict[str, Any]) -> Optional[Tuple[str, str, str]]:
+    """
+    从执行接口响应中提取 (status, execution_id, workflow_id)
+    兼容 snake_case / camelCase。
+    """
+    if not isinstance(execute_response, dict):
+        return None
+    data = execute_response.get("data")
+    if not isinstance(data, dict):
+        return None
+
+    status = data.get("status")
+    execution_id = data.get("execution_id") or data.get("executionId")
+    workflow_id = data.get("workflow_id") or data.get("workflowId")
+
+    if status and execution_id and workflow_id:
+        return str(status), str(execution_id), str(workflow_id)
+    return None
 
 
 class WorkflowAPIClient:
@@ -170,7 +229,20 @@ class WorkflowAPIClient:
             logger.info(f"[API响应] 执行工作流成功")
             logger.debug(f"[API响应] Body: {json.dumps(data, ensure_ascii=False, indent=2)}")
             
-            return data
+            # 兼容：如果服务端返回 async 的 execution_id/workflow_id，则按文档轮询状态接口直到结束
+            async_info = _extract_async_ids(data)
+            if async_info is None:
+                return data
+
+            status, execution_id, workflow_id = async_info
+            if status not in ("pending", "running"):
+                # 有些服务端可能直接返回 success/failed 但仍带 execution_id
+                return data
+
+            logger.info(
+                f"[执行工作流] 检测到异步执行: status={status}, execution_id={execution_id}, workflow_id={workflow_id}"
+            )
+            return self.poll_workflow_result(execution_id=execution_id, workflow_id=workflow_id)
             
         except httpx.TimeoutException:
             error_msg = f"执行工作流API请求超时: {url}"
@@ -193,6 +265,88 @@ class WorkflowAPIClient:
             logger.error(f"[API错误] {error_msg}", exc_info=True)
             raise Exception(error_msg)
     
+    def get_workflow_execute_status(self, execution_id: str, workflow_id: str) -> Dict[str, Any]:
+        """
+        查询异步执行状态
+
+        GET /open/workflow/execute/status/{executionId}?workflowId={workflowId}
+        """
+        url = f"{self.base_url}/open/workflow/status/{execution_id}"
+        try:
+            logger.info(f"[API请求] GET {url}")
+            response = self.client.get(
+                url,
+                headers=self._get_headers(),
+                params={"workflowId": workflow_id},
+            )
+            logger.info(f"[API响应] HTTP {response.status_code}")
+            response.raise_for_status()
+            data = response.json()
+            logger.debug(f"[API响应] Body: {json.dumps(data, ensure_ascii=False, indent=2)}")
+            return data
+        except httpx.TimeoutException:
+            error_msg = f"查询工作流执行状态超时: {url}"
+            logger.error(f"[API错误] {error_msg}")
+            raise Exception(error_msg)
+        except httpx.HTTPStatusError as e:
+            error_msg = f"查询工作流执行状态失败 (HTTP {e.response.status_code}): {url}"
+            logger.error(f"[API错误] {error_msg}")
+            logger.error(f"[API错误] 响应内容: {e.response.text}")
+            raise Exception(error_msg)
+        except httpx.RequestError as e:
+            error_msg = f"查询工作流执行状态请求异常: {url}, 错误: {str(e)}"
+            logger.error(f"[API错误] {error_msg}")
+            raise Exception(error_msg)
+        except Exception as e:
+            error_msg = f"处理查询执行状态响应时出错: {str(e)}"
+            logger.error(f"[API错误] {error_msg}", exc_info=True)
+            raise Exception(error_msg)
+
+    def poll_workflow_result(self, execution_id: str, workflow_id: str) -> Dict[str, Any]:
+        """轮询直到工作流执行完成（success/failed）或超时"""
+        interval, max_polls, max_errors = _get_polling_config()
+        error_count = 0
+
+        logger.info(
+            f"[轮询] 开始轮询执行状态: execution_id={execution_id}, workflow_id={workflow_id}, "
+            f"interval={interval}s, max_polls={max_polls}, max_errors={max_errors}"
+        )
+
+        for i in range(max_polls):
+            try:
+                resp = self.get_workflow_execute_status(execution_id=execution_id, workflow_id=workflow_id)
+                if resp.get("code") != 200:
+                    error_count += 1
+                    logger.warning(f"[轮询] 状态接口返回非200(code={resp.get('code')}), error_count={error_count}")
+                    if error_count >= max_errors:
+                        raise Exception(f"轮询连续错误次数过多: code={resp.get('code')}, msg={resp.get('msg')}")
+                    time.sleep(interval)
+                    continue
+
+                error_count = 0
+                data = resp.get("data") or {}
+                status = data.get("status")
+
+                logger.info(f"[轮询] 第{i+1}/{max_polls}次: status={status}")
+
+                if status in ("pending", "running"):
+                    time.sleep(interval)
+                    continue
+                if status in ("success", "failed"):
+                    return resp
+
+                # 兜底：未知状态
+                raise Exception(f"未知执行状态: {status}")
+
+            except Exception as e:
+                error_count += 1
+                logger.warning(f"[轮询] 轮询异常: {e}, error_count={error_count}")
+                if error_count >= max_errors:
+                    raise
+                time.sleep(interval)
+
+        raise Exception("轮询超时：超过最大轮询次数仍未完成")
+
     def close(self):
         """关闭HTTP客户端"""
         if self._client is not None:
diff --git a/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/workflow-execution-api.md b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/workflow-execution-api.md
new file mode 100644
index 0000000..c042932
--- /dev/null
+++ b/lzwcai_workflow_to_mcp/lzwcai_workflow_to_mcp/workflow-execution-api.md
@@ -0,0 +1,321 @@
+# 工作流执行接口文档
+
+## 概述
+工作流执行分为**同步执行**和**异步执行**两种模式，通过 `async` 参数控制。
+
+---
+
+## 1. 调试执行接口
+
+### 接口信息
+- **URL**: `POST /open/workflow/execute`
+- **超时**: 600000ms (10分钟)
+
+### 请求参数
+```json
+{
+  "workflow": {
+    "nodes": [...],
+    "edges": [...]
+  },
+  "inputs": {
+    "param1": "value1",
+    "param2": "value2"
+  },
+  "saveLog": false,
+  "async": true
+}
+```
+
+| 参数 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| workflow | Object | 是 | 工作流配置（节点和边） |
+| inputs | Object | 否 | 开始节点的输入参数 |
+| saveLog | Boolean | 否 | 是否保存执行日志 |
+| async | Boolean | 否 | 是否异步执行（默认 false） |
+
+---
+
+## 2. 响应处理
+
+### 2.1 同步执行响应 (async=false)
+直接返回完整执行结果：
+
+```json
+{
+  "code": 200,
+  "data": {
+    "success": true,
+    "status": "success",
+    "duration": 2.5,
+    "nodes_executed": 5,
+    "errors_count": 0,
+    "final_output": {...},
+    "execution_history": [
+      {
+        "node_id": "node_1",
+        "node_type": "start",
+        "status": "success",
+        "inputs": {...},
+        "outputs": {...},
+        "error": null,
+        "duration": 0.1,
+        "timestamp": "2024-01-20T10:00:00"
+      }
+    ]
+  }
+}
+```
+
+### 2.2 异步执行响应 (async=true)
+返回执行 ID，需要轮询查询状态：
+
+```json
+{
+  "code": 200,
+  "data": {
+    "status": "pending",
+    "execution_id": "exec_123456",
+    "workflow_id": "wf_789"
+  }
+}
+```
+
+---
+
+## 3. 异步执行状态查询
+
+### 接口信息
+- **URL**: `GET /open/workflow/status/{executionId}?workflowId={workflowId}`
+
+### 请求参数
+| 参数 | 位置 | 类型 | 必填 | 说明 |
+|------|------|------|------|------|
+| executionId | Path | String | 是 | 执行ID |
+| workflowId | Query | String | 是 | 工作流ID |
+
+### 响应数据
+```json
+{
+  "code": 200,
+  "data": {
+    "status": "success",
+    "success": true,
+    "execution_id": "exec_123456",
+    "workflow_id": "wf_789",
+    "duration": 2.5,
+    "nodes_executed": 5,
+    "errors_count": 0,
+    "final_output": {...},
+    "execution_history": [...]
+  }
+}
+```
+
+### 状态值说明
+| 状态 | 说明 | 处理方式 |
+|------|------|----------|
+| pending | 等待执行 | 继续轮询 |
+| running | 执行中 | 继续轮询 |
+| success | 执行成功 | 停止轮询，显示结果 |
+| failed | 执行失败 | 停止轮询，显示错误 |
+
+---
+
+## 4. 轮询策略
+
+### 前端实现参数
+```javascript
+const POLLING_INTERVAL = 1000;      // 轮询间隔：1秒
+const MAX_POLL_COUNT = 120;         // 最大轮询次数：2分钟
+const MAX_ERROR_COUNT = 3;          // 最大连续错误次数
+```
+
+### 轮询逻辑
+1. 提交异步执行请求，获取 `execution_id` 和 `workflow_id`
+2. 每隔 1 秒调用状态查询接口
+3. 根据返回的 `status` 判断：
+   - `pending/running`: 继续轮询
+   - `success/failed`: 停止轮询，处理结果
+4. 超过 120 次轮询或连续 3 次错误，停止轮询
+
+---
+
+## 5. Python 实现示例
+
+### 5.1 同步执行
+```python
+import requests
+
+def execute_workflow_sync(workflow_config, inputs=None):
+    """同步执行工作流"""
+    url = "http://your-api/api/system/workflowManage/debug"
+    
+    payload = {
+        "workflow": workflow_config,
+        "inputs": inputs or {},
+        "saveLog": False,
+        "async": False
+    }
+    
+    response = requests.post(url, json=payload, timeout=600)
+    result = response.json()
+    
+    if result["code"] == 200:
+        return result["data"]
+    else:
+        raise Exception(f"执行失败: {result.get('msg')}")
+```
+
+### 5.2 异步执行 + 轮询
+```python
+import requests
+import time
+
+def execute_workflow_async(workflow_config, inputs=None):
+    """异步执行工作流并轮询结果"""
+    # 1. 提交异步执行
+    url = "http://your-api/api/system/workflowManage/debug"
+    payload = {
+        "workflow": workflow_config,
+        "inputs": inputs or {},
+        "saveLog": False,
+        "async": True
+    }
+    
+    response = requests.post(url, json=payload, timeout=600)
+    result = response.json()
+    
+    if result["code"] != 200:
+        raise Exception(f"提交失败: {result.get('msg')}")
+    
+    execution_id = result["data"]["execution_id"]
+    workflow_id = result["data"]["workflow_id"]
+    
+    # 2. 轮询执行状态
+    status_url = f"http://your-api/api/system/workflowManage/status/{execution_id}"
+    params = {"workflowId": workflow_id}
+    
+    max_polls = 120
+    poll_interval = 1
+    error_count = 0
+    
+    for i in range(max_polls):
+        try:
+            response = requests.get(status_url, params=params)
+            result = response.json()
+            
+            if result["code"] == 200:
+                error_count = 0  # 重置错误计数
+                data = result["data"]
+                status = data["status"]
+                
+                if status == "success":
+                    return data
+                elif status == "failed":
+                    raise Exception(f"执行失败: {data.get('error')}")
+                elif status in ["pending", "running"]:
+                    time.sleep(poll_interval)
+                    continue
+                else:
+                    raise Exception(f"未知状态: {status}")
+            else:
+                error_count += 1
+                if error_count >= 3:
+                    raise Exception("连续错误次数过多")
+                time.sleep(poll_interval)
+                
+        except requests.RequestException as e:
+            error_count += 1
+            if error_count >= 3:
+                raise Exception(f"网络异常: {str(e)}")
+            time.sleep(poll_interval)
+    
+    raise Exception("执行超时")
+```
+
+### 5.3 完整使用示例
+```python
+# 工作流配置
+workflow_config = {
+    "nodes": [
+        {
+            "id": "start_1",
+            "type": "start",
+            "data": {
+                "config": {
+                    "inputs": [
+                        {"name": "text", "type": "string", "required": True}
+                    ]
+                }
+            }
+        },
+        # ... 其他节点
+    ],
+    "edges": [
+        {"source": "start_1", "target": "node_2"}
+    ]
+}
+
+# 输入参数
+inputs = {
+    "text": "Hello World"
+}
+
+# 同步执行
+try:
+    result = execute_workflow_sync(workflow_config, inputs)
+    print("执行成功:", result)
+except Exception as e:
+    print("执行失败:", e)
+
+# 异步执行
+try:
+    result = execute_workflow_async(workflow_config, inputs)
+    print("执行成功:", result)
+except Exception as e:
+    print("执行失败:", e)
+```
+
+---
+
+## 6. 响应数据结构
+
+### execution_history 节点执行记录
+```python
+{
+    "node_id": "node_1",           # 节点ID
+    "node_type": "llm",            # 节点类型
+    "status": "success",           # 执行状态
+    "inputs": {...},               # 输入参数
+    "outputs": {...},              # 输出结果
+    "error": None,                 # 错误信息
+    "duration": 1.2,               # 执行耗时(秒)
+    "timestamp": "2024-01-20..."   # 时间戳
+}
+```
+
+### 节点类型枚举
+- `start`: 开始节点
+- `end`: 结束节点
+- `llm`: 大模型节点
+- `code`: 代码节点
+- `condition`: 条件分支
+- `loop`: 循环节点
+- `http`: HTTP请求
+- `knowledge`: 知识库
+- `agent`: 智能体
+- `tool`: 工具节点
+- `variable`: 变量节点
+- `template`: 模板节点
+- `subflow`: 子流程
+
+---
+
+## 7. 注意事项
+
+1. **超时设置**: 调试接口超时时间为 10 分钟，适合长时间运行的工作流
+2. **异步推荐**: 对于复杂工作流，建议使用异步模式避免超时
+3. **轮询频率**: 建议 1 秒轮询一次，避免过于频繁
+4. **错误处理**: 需要处理网络异常、超时、连续错误等情况
+5. **日志保存**: `saveLog` 参数控制是否保存执行日志到数据库
diff --git a/lzwcai_workflow_to_mcp/main.py b/lzwcai_workflow_to_mcp/main.py
index 80107e5..4077d21 100644
--- a/lzwcai_workflow_to_mcp/main.py
+++ b/lzwcai_workflow_to_mcp/main.py
@@ -7,8 +7,8 @@ import os
 
 if __name__ == "__main__":
     # 设置环境变量
-    os.environ["workflowId"] = "2002300699510763521"
-    os.environ["workflowExecuteKey"] = "wf_buh230o9iaea4n6aefsddcexa7p27ydl"
+    os.environ["workflowId"] = "2005892514011795457"
+    os.environ["workflowExecuteKey"] = "wf_ce270212b2ee45ab9c81714a7c243c56"
     os.environ["backendBaseUrl"] = "http://192.168.2.236:8088"
     
     # Import and run the actual MCP server
diff --git a/lzwcai_workflow_to_mcp/pyproject.toml b/lzwcai_workflow_to_mcp/pyproject.toml
index 753605b..9b6cab5 100644
--- a/lzwcai_workflow_to_mcp/pyproject.toml
+++ b/lzwcai_workflow_to_mcp/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "lzwcai-workflow-to-mcp"
-version = "0.1.1"
+version = "0.1.2"
 description = "MCP server for executing business SQL queries with dynamic tool generation"
 readme = "README.md"
 requires-python = ">=3.13"