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 d9aed5a..bcf2a5f 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 WHERE EXTRACT(YEAR FROM so.order_date_utc) = 2025 ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 30",
+    "sqlTemplate": "WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = 'CLOSED'), logistics_delay_stats AS (SELECT customer_id, AVG(GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))) AS avg_logistics_delay_days, SUM(CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 5 THEN 1 ELSE 0 END) AS delay_count FROM fact_sales_shipment GROUP BY customer_id), quality_issue_stats AS (SELECT COALESCE(ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0), 2), 0) AS defect_rate_pct FROM fact_quality_inspection), scrap_stats AS (SELECT COALESCE((SELECT COUNT(*) FROM fact_scrap) * 100.0 / NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0), 0) AS scrap_rate_pct), active_work_order_risk AS (SELECT COUNT(*) AS active_wo_count, COALESCE(SUM(CASE WHEN planned_qty > 0 AND (completed_qty / planned_qty) < 0.5 AND EXTRACT(DAY FROM NOW() - event_time_utc) > 10 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN ('OPEN', 'STARTED')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 80 THEN 'RED' WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 15) * 2) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 4) + LEAST(25, gm.defect_rate_pct * 2) + LEAST(20, gm.lagging_wo_count * 8)) >= 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level, CASE WHEN gm.lagging_wo_count >= 5 THEN 'PRODUCTION_SEVERELY_DELAYED' WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 10 THEN 'HIGH_LOGISTICS_DELAY_RISK' WHEN gm.avg_production_days > 30 THEN 'LONG_PRODUCTION_CYCLE' WHEN gm.defect_rate_pct > 20 THEN 'QUALITY_ISSUES' ELSE 'NORMAL' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id LEFT JOIN global_metrics gm ON 1=1 LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 50",
     "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 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",
+    "sqlTemplate": "WITH work_order_base AS (SELECT wo.work_order_id, wo.work_order_number, wo.product_id, wo.status, wo.planned_qty, wo.completed_qty, wo.event_time_utc::timestamp AS start_time, wo.last_updated_utc::timestamp AS last_update, wo.source_system FROM fact_work_order wo), product_info AS (SELECT product_id, product_name, product_category FROM dim_product WHERE is_current = 't'), labor_summary AS (SELECT work_order_number, COUNT(DISTINCT worker_name) AS worker_count, SUM(report_qty) AS total_report_qty, SUM(duration_minutes) AS total_minutes, MAX(event_time_utc::timestamp) AS last_report_time FROM fact_labor_report GROUP BY work_order_number), quality_summary AS (SELECT work_order_number, SUM(pass_qty) AS pass_qty, SUM(fail_qty) AS fail_qty FROM fact_quality_inspection GROUP BY work_order_number), work_order_progress AS (SELECT wb.work_order_id, wb.work_order_number, p.product_name, p.product_category, wb.status AS raw_status, CASE wb.status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, wb.planned_qty, wb.completed_qty, CASE WHEN wb.planned_qty > 0 THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1) ELSE 0 END AS completion_rate, GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty, wb.start_time, wb.last_update, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours, COALESCE(ls.worker_count, 0) AS worker_count, COALESCE(ls.total_report_qty, 0) AS total_report_qty, COALESCE(ls.total_minutes, 0) AS total_work_minutes, ls.last_report_time, COALESCE(qs.pass_qty, 0) AS qc_pass_qty, COALESCE(qs.fail_qty, 0) AS qc_fail_qty FROM work_order_base wb LEFT JOIN product_info p ON wb.product_id = p.product_id LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number), anomaly_detection AS (SELECT *, CASE WHEN raw_status = 'STARTED' AND elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN raw_status = 'STARTED' AND elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' ELSE NULL END AS progress_anomaly, CASE WHEN qc_pass_qty + qc_fail_qty > 0 AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 20 THEN 'QUALITY_ISSUE' ELSE NULL END AS quality_anomaly, CASE WHEN raw_status = 'STARTED' AND last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' WHEN raw_status = 'STARTED' AND last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' ELSE NULL END AS labor_anomaly, CASE WHEN total_work_minutes > 0 AND total_report_qty / (total_work_minutes / 60.0) < 2 THEN 'LOW_EFFICIENCY' ELSE NULL END AS efficiency_anomaly FROM work_order_progress) SELECT work_order_number, product_name, product_category, status_name, planned_qty, completed_qty, remaining_qty, completion_rate, worker_count, ROUND(total_work_minutes / 60.0, 1) AS total_work_hours, elapsed_hours, COALESCE(progress_anomaly, '') || CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(quality_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(labor_anomaly, '') || CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END || COALESCE(efficiency_anomaly, '') AS anomaly_flags, CASE WHEN progress_anomaly = 'SEVERELY_DELAYED' OR quality_anomaly IS NOT NULL THEN 'HIGH' WHEN progress_anomaly = 'DELAYED' OR labor_anomaly IS NOT NULL THEN 'MEDIUM' WHEN efficiency_anomaly IS NOT NULL THEN 'LOW' ELSE 'NONE' END AS risk_level FROM anomaly_detection ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END, CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END, completion_rate ASC ; WITH status_summary AS (SELECT CASE status WHEN 'OPEN' THEN 'PENDING' WHEN 'STARTED' THEN 'IN_PROGRESS' WHEN 'CLOSED' THEN 'COMPLETED' ELSE 'UNKNOWN' END AS status_name, status AS raw_status, COUNT(*) AS order_count, SUM(planned_qty) AS total_planned, SUM(completed_qty) AS total_completed FROM fact_work_order GROUP BY status) SELECT status_name, order_count, ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS pct, ROUND(total_planned, 0) AS total_planned, ROUND(total_completed, 0) AS total_completed, CASE WHEN total_planned > 0 THEN ROUND(total_completed * 100.0 / total_planned, 1) ELSE 0 END AS completion_rate FROM status_summary ORDER BY CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END ; WITH work_order_anomaly AS (SELECT wo.work_order_number, p.product_name, wo.status, wo.planned_qty, wo.completed_qty, CASE WHEN wo.planned_qty > 0 THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1) ELSE 0 END AS completion_rate, ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours, (SELECT MAX(lr.event_time_utc::timestamp) FROM fact_labor_report lr WHERE lr.work_order_number = wo.work_order_number) AS last_report_time FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' WHERE wo.status = 'STARTED') SELECT work_order_number, product_name, ROUND(planned_qty, 0) AS planned_qty, ROUND(completed_qty, 0) AS completed_qty, completion_rate, elapsed_hours, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'SEVERELY_DELAYED' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'DELAYED' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'NO_LABOR_RECORD' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'LABOR_STALLED' ELSE 'NORMAL' END AS anomaly_type, CASE WHEN elapsed_hours > 96 AND completion_rate < 20 THEN 'IMMEDIATE_FOLLOWUP' WHEN elapsed_hours > 72 AND completion_rate < 40 THEN 'MONITOR_PROGRESS' WHEN last_report_time IS NULL AND elapsed_hours > 72 THEN 'CONFIRM_START' WHEN last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 72 THEN 'CHECK_LABOR' ELSE '-' END AS action FROM work_order_anomaly WHERE elapsed_hours > 36 OR last_report_time IS NULL OR (last_report_time IS NOT NULL AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 36) ORDER BY CASE WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1 WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2 ELSE 3 END, completion_rate ASC ; SELECT p.product_category, COUNT(*) AS order_count, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS pending, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS in_progress, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS completed, ROUND(SUM(wo.planned_qty), 0) AS total_planned, ROUND(SUM(wo.completed_qty), 0) AS total_completed, CASE WHEN SUM(wo.planned_qty) > 0 THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1) ELSE 0 END AS completion_rate FROM fact_work_order wo LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category ORDER BY order_count DESC LIMIT 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 > 72), today_stats AS (SELECT COUNT(*) AS today_completed FROM fact_work_order WHERE status = 'CLOSED' AND last_updated_utc::date = CURRENT_DATE) SELECT 'total_orders' AS metric, cs.total_orders::text AS value, '-' AS status FROM current_stats cs UNION ALL SELECT 'pending', cs.open_orders::text, CASE WHEN cs.open_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM current_stats cs UNION ALL SELECT 'in_progress', cs.started_orders::text, 'RUNNING' FROM current_stats cs UNION ALL SELECT 'completed', cs.closed_orders::text, 'NORMAL' FROM current_stats cs UNION ALL SELECT 'completion_rate', ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%', CASE WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 60 THEN 'GOOD' WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 30 THEN 'NORMAL' ELSE 'LOW' END FROM current_stats cs UNION ALL SELECT 'anomaly_count', ans.anomaly_count::text, CASE WHEN ans.anomaly_count > 15 THEN 'ATTENTION' ELSE 'NORMAL' END FROM anomaly_stats ans UNION ALL SELECT 'today_completed', ts.today_completed::text, '-' FROM today_stats ts",
     "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 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",
+    "sqlTemplate": "WITH supplier_delivery AS (SELECT po.supplier_id, COUNT(*) AS order_count, COUNT(pr.purchase_receipt_id) AS receipt_count, AVG(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS avg_delivery_days, MAX(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS max_delivery_days, STDDEV(CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp) ELSE NULL END) AS stddev_delivery_days FROM fact_purchase_order po LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id GROUP BY po.supplier_id), supplier_quality AS (SELECT pr.supplier_id, COUNT(pr.purchase_receipt_id) AS total_receipts, SUM(pr.receipt_qty_total) AS total_qty, SUM(pr.amount) AS total_amount FROM fact_purchase_receipt pr GROUP BY pr.supplier_id), supplier_returns AS (SELECT pret.supplier_id, COUNT(*) AS return_count, SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count FROM fact_purchase_return pret GROUP BY pret.supplier_id), supplier_quality_rate AS (SELECT sq.supplier_id, sq.total_receipts, sq.total_qty, sq.total_amount, COALESCE(sr.return_count, 0) AS return_count, COALESCE(sr.damage_count, 0) AS damage_count, CASE WHEN sq.total_receipts > 0 THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts ELSE 100 END AS quality_rate FROM supplier_quality sq LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id), supplier_risk AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COALESCE(sd.order_count, 0) AS order_count, COALESCE(sd.receipt_count, 0) AS receipt_count, ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days, ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days, ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility, COALESCE(sqr.total_receipts, 0) AS total_receipts, COALESCE(sqr.return_count, 0) AS return_count, ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate, CASE WHEN COALESCE(sd.avg_delivery_days, 0) > 120 THEN 40 WHEN COALESCE(sd.avg_delivery_days, 0) > 90 THEN 30 WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 20 ELSE 10 END + CASE WHEN COALESCE(sd.stddev_delivery_days, 0) > 40 THEN 30 WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 20 ELSE 10 END AS delivery_risk_score, CASE WHEN COALESCE(sqr.quality_rate, 100) < 60 THEN 50 WHEN COALESCE(sqr.quality_rate, 100) < 75 THEN 30 WHEN COALESCE(sqr.quality_rate, 100) < 88 THEN 15 ELSE 5 END AS quality_risk_score FROM dim_supplier s LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id WHERE s.is_current = 't'), supplier_risk_level AS (SELECT *, delivery_risk_score + quality_risk_score AS total_risk_score, CASE WHEN delivery_risk_score + quality_risk_score >= 90 THEN 'HIGH' WHEN delivery_risk_score + quality_risk_score >= 60 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level, CASE WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY' WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE' WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE' ELSE 'NORMAL' END AS risk_pattern FROM supplier_risk) SELECT supplier_name, supplier_category, order_count, receipt_count, avg_delivery_days, max_delivery_days, delivery_volatility, total_receipts, return_count, quality_rate, delivery_risk_score, quality_risk_score, total_risk_score, risk_level, risk_pattern FROM supplier_risk_level ORDER BY total_risk_score DESC, supplier_name LIMIT 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 > 90 AND sri.return_count > 3 THEN 'HIGH' WHEN sri.avg_delivery_days > 75 OR sri.return_count > 2 THEN 'MEDIUM' ELSE 'LOW' END AS risk_level FROM fact_purchase_order po JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id) SELECT purchase_order_number, order_date, supplier_name, supplier_category, supplier_avg_days, quality_rate, days_since_order, risk_level, CASE WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP' WHEN risk_level = 'MEDIUM' THEN 'MONITOR' ELSE 'NORMAL' END AS action_required FROM order_risk WHERE risk_level IN ('HIGH', 'MEDIUM') ORDER BY CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END, days_since_order DESC LIMIT 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 > 20 THEN 'HIGH' WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 12 THEN 'MEDIUM' ELSE 'LOW' END AS category_risk_level FROM supplier_stats ORDER BY return_count DESC LIMIT 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 > 20 THEN 1 ELSE 0 END) AS high_risk_suppliers, SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 12 AND 20 THEN 1 ELSE 0 END) AS medium_risk_suppliers, SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 12 THEN 1 ELSE 0 END) AS low_risk_suppliers, COUNT(*) AS total_suppliers FROM risk_summary), order_stats AS (SELECT COUNT(*) AS pending_orders, COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders FROM fact_purchase_order), return_stats AS (SELECT COUNT(*) AS total_returns, COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns FROM fact_purchase_return) SELECT 'high_risk_suppliers' AS metric_name, rc.high_risk_suppliers::text AS metric_value, CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status FROM risk_counts rc UNION ALL SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text, CASE WHEN rc.medium_risk_suppliers > 8 THEN 'MONITOR' ELSE 'NORMAL' END FROM risk_counts rc UNION ALL SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL' FROM risk_counts rc UNION ALL SELECT 'pending_orders', os.pending_orders::text, CASE WHEN os.pending_orders > 100 THEN 'BACKLOG' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'overdue_orders_30d', os.overdue_orders::text, CASE WHEN os.overdue_orders > 20 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END FROM order_stats os UNION ALL SELECT 'recent_returns_30d', rs.recent_returns::text, CASE WHEN rs.recent_returns > 15 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END FROM return_stats rs ; WITH supplier_metrics AS (SELECT s.supplier_id, s.supplier_name, s.supplier_category, COUNT(DISTINCT po.purchase_order_id) AS order_count, COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count, COUNT(DISTINCT pret.purchase_return_id) AS return_count, SUM(pr.amount) AS total_amount FROM dim_supplier s LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id WHERE s.is_current = 't' GROUP BY s.supplier_id, s.supplier_name, s.supplier_category), ranked_suppliers AS (SELECT *, CASE WHEN receipt_count > 0 THEN ROUND(return_count * 100.0 / receipt_count, 1) ELSE 0 END AS return_rate, ROW_NUMBER() OVER (ORDER BY CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC, return_count DESC) AS risk_rank FROM supplier_metrics) SELECT risk_rank, supplier_name, supplier_category, order_count, receipt_count, return_count, return_rate, ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount, CASE WHEN return_rate > 20 THEN 'HIGH_RISK' WHEN return_rate > 12 THEN 'MEDIUM_RISK' WHEN return_count > 0 THEN 'LOW_RISK' ELSE 'EXCELLENT' END AS risk_assessment FROM ranked_suppliers ORDER BY risk_rank LIMIT 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 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",
+    "sqlTemplate": "WITH labor_stats AS (SELECT p.product_category AS department, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) AS total_work_minutes, SUM(lr.report_qty) AS total_output_qty, COUNT(DISTINCT lr.work_order_number) AS work_order_count FROM fact_labor_report lr INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), work_order_stats AS (SELECT p.product_category AS department, COUNT(*) AS total_work_orders, SUM(wo.planned_qty) AS total_planned_qty, SUM(wo.completed_qty) AS total_completed_qty, SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order wo INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), quality_stats AS (SELECT p.product_category AS department, SUM(qi.pass_qty) AS total_pass_qty, SUM(qi.fail_qty) AS total_fail_qty, COUNT(*) AS inspection_count FROM fact_quality_inspection qi INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't' GROUP BY p.product_category), sales_stats AS (SELECT AVG(deal_amount) AS avg_order_amount, SUM(deal_amount) AS total_sales_amount, COUNT(*) AS order_count FROM fact_sales_order), scrap_stats AS (SELECT COUNT(*) AS total_scrap_count FROM fact_scrap) SELECT ls.department, ls.worker_count, ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours, ls.total_output_qty, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0), 2) AS efficiency_index, ws.total_planned_qty AS planned_qty, ws.total_completed_qty AS completed_qty, ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value, ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker, qs.total_pass_qty AS qc_pass_qty, qs.total_fail_qty AS qc_fail_qty, ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate, ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate, ROUND((ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 + (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 + (qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25, 2) AS performance_score, CASE WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 20 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 30 THEN 'RED' WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 12 OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50 THEN 'YELLOW' ELSE 'GREEN' END AS warning_level FROM labor_stats ls LEFT JOIN work_order_stats ws ON ls.department = ws.department LEFT JOIN quality_stats qs ON ls.department = qs.department CROSS JOIN sales_stats ss CROSS JOIN scrap_stats scr ORDER BY performance_score DESC LIMIT 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 >= 20 THEN 'HIGH' WHEN COALESCE(SUM(qi.fail_qty), 0) / NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 12 THEN 'MEDIUM' ELSE 'LOW' END AS loss_level FROM dim_product p LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id WHERE p.is_current = 't' GROUP BY p.product_category, p.product_name, p.product_code ORDER BY defect_rate DESC NULLS LAST LIMIT 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 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",
+    "sqlTemplate": "WITH sales_summary AS (SELECT COUNT(*) AS total_orders, SUM(deal_amount) AS total_sales_amount, AVG(deal_amount) AS avg_order_amount, SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders, SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders, SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders, SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount, SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount FROM fact_sales_order), production_summary AS (SELECT COUNT(*) AS total_work_orders, SUM(planned_qty) AS total_planned_qty, SUM(completed_qty) AS total_completed_qty, SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders, SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders, SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders FROM fact_work_order), ar_summary AS (SELECT COUNT(*) AS receipt_count, SUM(amount) AS total_receipt_amount, AVG(amount) AS avg_receipt_amount FROM fact_ar_receipt), ap_summary AS (SELECT COUNT(*) AS payment_count, SUM(amount) AS total_payment_amount, AVG(amount) AS avg_payment_amount FROM fact_ap_payment), invoice_summary AS (SELECT COUNT(*) AS invoice_count, SUM(invoice_amount) AS total_invoice_amount, AVG(invoice_amount) AS avg_invoice_amount FROM fact_invoice), return_summary AS (SELECT COUNT(*) AS return_count, SUM(amount) AS total_return_amount, AVG(amount) AS avg_return_amount FROM fact_sales_return), shipment_summary AS (SELECT COUNT(*) AS shipment_count, SUM(amount) AS total_shipment_amount, AVG(amount) AS avg_shipment_amount FROM fact_sales_shipment), purchase_summary AS (SELECT COUNT(*) AS purchase_order_count FROM fact_purchase_order), quality_summary AS (SELECT COUNT(*) AS inspection_count, SUM(pass_qty) AS total_pass_qty, SUM(fail_qty) AS total_fail_qty FROM fact_quality_inspection), labor_summary AS (SELECT COUNT(DISTINCT worker_name) AS worker_count, SUM(duration_minutes) AS total_work_minutes, SUM(report_qty) AS total_output_qty FROM fact_labor_report), scrap_summary AS (SELECT COUNT(*) AS scrap_count FROM fact_scrap) SELECT 'Decision Brief' AS report_title, CURRENT_DATE AS report_date, ss.total_orders AS sales_order_count, ROUND(ss.total_sales_amount, 2) AS total_sales_amount, ROUND(ss.avg_order_amount, 2) AS avg_order_amount, ss.paid_orders AS paid_order_count, ss.partial_orders AS partial_paid_count, ss.unpaid_orders AS unpaid_order_count, ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate, ROUND(ss.unpaid_amount, 2) AS receivable_amount, ps.total_work_orders AS work_order_count, ps.closed_orders AS completed_work_orders, ps.started_orders AS in_progress_work_orders, ps.open_orders AS pending_work_orders, ROUND(ps.total_planned_qty, 0) AS planned_qty, ROUND(ps.total_completed_qty, 0) AS completed_qty, ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate, ls.worker_count AS active_worker_count, ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours, ROUND(ls.total_output_qty, 0) AS total_output, ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker, ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour, qs.inspection_count AS qc_batch_count, ROUND(qs.total_pass_qty, 0) AS pass_qty, ROUND(qs.total_fail_qty, 0) AS fail_qty, ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate, ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate, scr.scrap_count AS scrap_record_count, ar.receipt_count AS ar_receipt_count, ROUND(ar.total_receipt_amount, 2) AS total_ar_amount, ap.payment_count AS ap_payment_count, ROUND(ap.total_payment_amount, 2) AS total_ap_amount, ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow, inv.invoice_count, ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount, sh.shipment_count, ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount, pur.purchase_order_count, ret.return_count, ROUND(ret.total_return_amount, 2) AS total_return_amount, ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate, CASE WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 88 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 65 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 12 THEN 'EXCELLENT' WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 80 AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 40 AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 15 THEN 'GOOD' ELSE 'WARNING' END AS health_status FROM sales_summary ss CROSS JOIN production_summary ps CROSS JOIN ar_summary ar CROSS JOIN ap_summary ap CROSS JOIN invoice_summary inv CROSS JOIN return_summary ret CROSS JOIN shipment_summary sh CROSS JOIN purchase_summary pur CROSS JOIN quality_summary qs CROSS JOIN labor_summary ls CROSS JOIN scrap_summary scr ; 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, 200000 AS threshold, CASE WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 200000 THEN 'EXCEEDED' ELSE 'NORMAL' END AS status FROM fact_sales_order UNION ALL SELECT 'warning_metrics', 'defect_rate_pct', ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2), 12, CASE WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 12 THEN 'EXCEEDED' ELSE 'NORMAL' END FROM fact_quality_inspection UNION ALL SELECT 'warning_metrics', 'production_completion_rate', ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2), 40, CASE WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 40 THEN 'LOW' ELSE 'NORMAL' END FROM fact_work_order UNION ALL SELECT 'warning_metrics', 'return_rate_pct', ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2), 12, CASE WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 / NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 12 THEN 'EXCEEDED' ELSE 'NORMAL' END ; 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 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",
+    "sqlTemplate": "WITH daily_metrics AS (SELECT DATE(lr.event_time_utc::timestamp) AS metric_date, COUNT(DISTINCT lr.worker_name) AS worker_count, SUM(lr.duration_minutes) / 60.0 AS total_hours, SUM(lr.report_qty) AS total_output, CASE WHEN SUM(lr.duration_minutes) > 0 THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0) ELSE 0 END AS hourly_efficiency FROM fact_labor_report lr GROUP BY DATE(lr.event_time_utc::timestamp)), daily_quality AS (SELECT DATE(qi.event_time_utc::timestamp) AS metric_date, SUM(qi.pass_qty) AS pass_qty, SUM(qi.fail_qty) AS fail_qty, CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0 THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty)) ELSE 0 END AS defect_rate FROM fact_quality_inspection qi GROUP BY DATE(qi.event_time_utc::timestamp)), daily_production AS (SELECT DATE(wo.event_time_utc::timestamp) AS metric_date, SUM(wo.planned_qty) AS planned_qty, SUM(wo.completed_qty) AS completed_qty, CASE WHEN SUM(wo.planned_qty) > 0 THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty) ELSE 0 END AS completion_rate FROM fact_work_order wo GROUP BY DATE(wo.event_time_utc::timestamp)), combined_daily AS (SELECT COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date, COALESCE(dm.worker_count, 0) AS worker_count, COALESCE(dm.total_hours, 0) AS total_hours, COALESCE(dm.total_output, 0) AS total_output, COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency, COALESCE(dq.defect_rate, 0) AS defect_rate, COALESCE(dp.completion_rate, 0) AS completion_rate FROM daily_metrics dm FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL), numbered_data AS (SELECT *, ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq FROM combined_daily), moving_avg_step1 AS (SELECT metric_date, day_seq, worker_count, total_output, hourly_efficiency, defect_rate, completion_rate, AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency, AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output, AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate, AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate, AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x FROM numbered_data), moving_avg AS (SELECT *, LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency, LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output, LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate FROM moving_avg_step1), slope_calc AS (SELECT *, (ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency, (ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output, (ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect FROM moving_avg), trend_analysis AS (SELECT metric_date, hourly_efficiency, total_output, defect_rate, completion_rate, ROUND(ma7_efficiency, 2) AS ma7_efficiency, ROUND(ma7_output, 2) AS ma7_output, ROUND(ma7_defect_rate, 2) AS ma7_defect_rate, ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency, ROUND(COALESCE(slope_output, 0), 4) AS slope_output, ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect, prev_ma7_efficiency, prev_ma7_output, prev_ma7_defect_rate, CASE WHEN COALESCE(slope_efficiency, 0) > 0.5 THEN 'RISING' WHEN COALESCE(slope_efficiency, 0) < -0.5 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN COALESCE(slope_output, 0) > 5 THEN 'RISING' WHEN COALESCE(slope_output, 0) < -5 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN COALESCE(slope_defect, 0) > 0.5 THEN 'RISING' WHEN COALESCE(slope_defect, 0) < -0.5 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, CASE WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.4 THEN 'ANOMALY' ELSE 'NORMAL' END AS efficiency_status, CASE WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.4 THEN 'ANOMALY' ELSE 'NORMAL' END AS output_status, CASE WHEN defect_rate > ma7_defect_rate * 1.8 AND defect_rate > 8 THEN 'ANOMALY' ELSE 'NORMAL' END AS defect_status FROM slope_calc) SELECT metric_date, ROUND(hourly_efficiency, 2) AS hourly_output, ma7_efficiency AS efficiency_ma7, slope_efficiency, efficiency_trend, efficiency_status, CASE WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT' WHEN prev_ma7_efficiency IS NOT NULL AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS efficiency_turning_point, ROUND(total_output, 0) AS daily_output, ma7_output AS output_ma7, slope_output, output_trend, output_status, CASE WHEN prev_ma7_output IS NOT NULL AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT' WHEN prev_ma7_output IS NOT NULL AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS output_turning_point, ROUND(defect_rate, 2) AS defect_rate, ma7_defect_rate AS defect_rate_ma7, slope_defect, defect_trend, defect_status, CASE WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT' WHEN prev_ma7_defect_rate IS NOT NULL AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT' ELSE 'NONE' END AS defect_turning_point FROM trend_analysis ORDER BY metric_date DESC LIMIT 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 * 1.15 THEN 'RISING' WHEN hourly_efficiency < prev_efficiency * 0.85 THEN 'FALLING' ELSE 'STABLE' END AS efficiency_trend, CASE WHEN prev_output IS NULL THEN 'NONE' WHEN total_output > prev_output * 1.15 THEN 'RISING' WHEN total_output < prev_output * 0.85 THEN 'FALLING' ELSE 'STABLE' END AS output_trend, CASE WHEN prev_defect_rate IS NULL THEN 'NONE' WHEN defect_rate > prev_defect_rate * 1.3 THEN 'RISING' WHEN defect_rate < prev_defect_rate * 0.7 THEN 'FALLING' ELSE 'STABLE' END AS defect_trend, ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct FROM weekly_with_lag ORDER BY week_start DESC LIMIT 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 * 1.08 THEN 'RISING' WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.92 THEN 'FALLING' ELSE 'STABLE' END AS trend, CASE WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.25 THEN 'ANOMALY' ELSE 'NORMAL' END AS warning FROM period_stats ps UNION ALL SELECT 'daily_output', ROUND(ps.avg_output_7d, 0), ROUND(ps.avg_output_prev7d, 0), ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1), CASE WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.08 THEN 'RISING' WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.92 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.25 THEN 'ANOMALY' ELSE 'NORMAL' END FROM period_stats ps UNION ALL SELECT 'defect_rate_pct', ROUND(qs.avg_defect_7d, 2), ROUND(qs.avg_defect_prev7d, 2), ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1), CASE WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.15 THEN 'RISING' WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.85 THEN 'FALLING' ELSE 'STABLE' END, CASE WHEN qs.avg_defect_7d > 12 THEN 'THRESHOLD_EXCEEDED' WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.4 THEN 'ANOMALY_RISING' ELSE 'NORMAL' END FROM quality_stats qs ; 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/test.py b/lzwcai_mcpskills_mfg_data_agent/test.py
new file mode 100644
index 0000000..25ca3b2
--- /dev/null
+++ b/lzwcai_mcpskills_mfg_data_agent/test.py
@@ -0,0 +1,111 @@
+import requests
+import json
+
+def main(**kwargs) -> dict:
+    """
+    函数节点的入口函数
+
+    Args:
+        **kwargs: 从前端配置的参数传入，可通过变量引用获取工作流上下文
+
+    Returns:
+        dict: 返回结果将作为节点输出，可被后续节点引用
+    """
+    # 从 kwargs 获取参数，如果没有提供则使用默认值
+    url = kwargs.get('url', 'http://192.167.30.2:8088/datasource/sqlExecutionLog/testBatchSqlWithSchema')
+    
+    # 请求头（从kwargs获取或使用默认值）
+    authorization_token = kwargs.get('authorization_token', 'Bearer eyJhbGciOiJIUzUxMiJ9.eyJ0b2tlbl90eXBlIjoiTE9HSU4iLCJsb2dpbl91c2VyX2tleSI6IjAxNzZiNjEyLTc2YWItNDdhMS1iYTRiLTdjNWU2ZTMxNDlmZCJ9.w7aOfDJDHtA4bwNKIvUVK2cf1yO_2F27d_eYuos-p1-XGrSQOX0D4ny0b8Js36MhXwBnF4GDcy8V1VobEN6zBA')
+    headers = {
+        'Authorization': authorization_token
+    }
+    
+    # 请求体参数（从kwargs获取或使用默认值）
+    payload_id = kwargs.get('id', '2006300000000000001')
+    business_name = kwargs.get('businessName', 'OrderDelayWarningAnalysis')
+    business_description = kwargs.get('businessDescription', '订单延迟预警分析：依据历史订单的生产周期、物流延误、设备故障等特征，输出延迟概率与红/黄/绿预警等级')
+    datasource_id = kwargs.get('datasourceId', '57')
+    sql_template = kwargs.get('sqlTemplate', 'WITH production_cycle_stats AS (SELECT COALESCE(AVG(GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))), 0) AS avg_production_days FROM fact_work_order WHERE status = \'CLOSED\' AND last_updated_utc &gt;= 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) &gt; 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 &gt; 0 AND (completed_qty / planned_qty)  7 THEN 1 ELSE 0 END), 0) AS lagging_wo_count FROM fact_work_order WHERE status IN (\'OPEN\', \'STARTED\')), global_metrics AS (SELECT pcs.avg_production_days, qis.defect_rate_pct, ss.scrap_rate_pct, awr.active_wo_count, awr.lagging_wo_count FROM production_cycle_stats pcs, quality_issue_stats qis, scrap_stats ss, active_work_order_risk awr) SELECT so.sales_order_number AS order_number, c.customer_name, so.order_date_utc::DATE AS order_date, so.deal_amount AS order_amount, so.payment_status, ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days, ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days, COALESCE(lds.delay_count, 0)::INT AS historical_delay_count, ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct, ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct, gm.active_wo_count::INT AS active_work_order_count, gm.lagging_wo_count::INT AS lagging_work_order_count, ROUND(LEAST(100, GREATEST(0, LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)))::NUMERIC, 1) AS delay_probability_pct, CASE WHEN (LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) + LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) + LEAST(25, gm.defect_rate_pct * 2.5) + LEAST(20, gm.lagging_wo_count * 10)) &gt;= 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)) &gt;= 30 THEN \'YELLOW\' ELSE \'GREEN\' END AS warning_level, CASE WHEN gm.lagging_wo_count &gt;= 2 THEN \'PRODUCTION_SEVERELY_DELAYED\' WHEN COALESCE(lds.avg_logistics_delay_days, 0) &gt; 5 THEN \'HIGH_LOGISTICS_DELAY_RISK\' WHEN gm.avg_production_days &gt; 15 THEN \'LONG_PRODUCTION_CYCLE\' WHEN gm.defect_rate_pct &gt; 10 THEN \'QUALITY_ISSUES\' ELSE \'NORMAL\' END AS primary_risk_factor FROM fact_sales_order so LEFT JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = \'t\' CROSS JOIN global_metrics gm LEFT JOIN logistics_delay_stats lds ON so.customer_id = lds.customer_id WHERE EXTRACT(YEAR FROM so.order_date_utc) = 2025 ORDER BY delay_probability_pct DESC, so.order_date_utc DESC LIMIT 30')
+    parameters = kwargs.get('parameters', {})
+    
+    # 请求体（从kwargs获取或使用默认值）
+    payload = {
+        "id": payload_id,
+        "businessName": business_name,
+        "businessDescription": business_description,
+        "datasourceId": datasource_id,
+        "sqlTemplate": sql_template,
+        "parameters": parameters
+    }
+    
+    # 超时时间（从kwargs获取或使用默认值）
+    timeout = kwargs.get('timeout', 30)
+    
+    try:
+        # 发送POST请求
+        response = requests.post(
+            url=url,
+            headers=headers,
+            json=payload,
+            timeout=timeout
+        )
+        
+        # 检查HTTP响应状态码
+        response.raise_for_status()
+        
+        # 解析响应结果
+        result = response.json()
+        
+        # 返回接口调用结果
+        return {
+            "status": "success",
+            "status_code": response.status_code,
+            "result": result
+        }
+        
+    except requests.exceptions.Timeout:
+        return {
+            "status": "error",
+            "error_type": "timeout",
+            "message": f"接口调用超时（{timeout}秒）",
+            "result": None
+        }
+        
+    except requests.exceptions.ConnectionError:
+        return {
+            "status": "error",
+            "error_type": "connection_error",
+            "message": "无法连接到目标服务器",
+            "result": None
+        }
+        
+    except requests.exceptions.HTTPError as e:
+        return {
+            "status": "error",
+            "error_type": "http_error",
+            "status_code": response.status_code if 'response' in locals() else None,
+            "message": f"HTTP请求错误: {str(e)}",
+            "response_text": response.text if 'response' in locals() else "",
+            "result": None
+        }
+        
+    except json.JSONDecodeError:
+        return {
+            "status": "error",
+            "error_type": "json_decode_error",
+            "message": "接口返回的不是有效的JSON格式",
+            "response_text": response.text if 'response' in locals() else "",
+            "result": None
+        }
+        
+    except Exception as e:
+        return {
+            "status": "error",
+            "error_type": "unknown_error",
+            "message": f"接口调用失败: {str(e)}",
+            "result": None
+        }
+
+if __name__ == "__main__":
+    result = main()
+    print(json.dumps(result, ensure_ascii=False, indent=2))        
\ No newline at end of file
diff --git a/lzwcai_mcpskills_template/README.md b/lzwcai_mcpskills_template/README.md
new file mode 100644
index 0000000..7ffaa1f
--- /dev/null
+++ b/lzwcai_mcpskills_template/README.md
@@ -0,0 +1,65 @@
+# lzwcai-mcpskills-template
+
+MCP Server 模板项目，用于快速创建新的 MCP 服务。
+
+## 功能
+
+- 提供 MCP Server 基础框架
+- 支持动态工具注册
+- 内置日志系统
+- 支持环境变量配置
+
+## 安装
+
+```bash
+pip install lzwcai-mcpskills-template
+```
+
+## 使用
+
+```bash
+# 设置环境变量
+export API_KEY="your-api-key"
+export BASE_URL="http://your-api-server"
+
+# 运行
+lzwcai-mcpskills-template
+```
+
+## 环境变量
+
+| 变量名 | 说明 | 默认值 |
+|--------|------|--------|
+| API_KEY | API密钥 | - |
+| BASE_URL | 后端API地址 | http://localhost:8080 |
+
+## 项目结构
+
+```
+lzwcai_mcpskills_template/
+├── main.py              # 入口文件
+├── pyproject.toml       # 项目配置
+├── README.md            # 说明文档
+└── lzwcai_mcpskills_template/ # 核心代码
+    ├── main.py          # MCP Server 主逻辑
+    ├── schema_converter.py  # Schema 转换器
+    └── utils/           # 工具模块
+        ├── __init__.py
+        ├── api_client.py    # API 客户端
+        ├── env_config.py    # 环境变量配置
+        ├── json_helper.py   # JSON 工具
+        └── logger_config.py # 日志配置
+```
+
+## 开发
+
+基于此模板创建新项目：
+
+1. 复制整个目录
+2. 修改 `pyproject.toml` 中的项目名称
+3. 修改 `lzwcai_mcpskills_template` 目录名
+4. 在 `main.py` 中实现你的工具逻辑
+
+## License
+
+MIT
diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/.python-version b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/.python-version
new file mode 100644
index 0000000..24ee5b1
--- /dev/null
+++ b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/.python-version
@@ -0,0 +1 @@
+3.13
diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/main.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/main.py
new file mode 100644
index 0000000..41c33ce
--- /dev/null
+++ b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/main.py
@@ -0,0 +1,281 @@
+#!/usr/bin/env python3
+"""
+MCP Server 模板
+支持从本地 JSON 或 API 动态加载工具配置
+
+使用方式:
+- local: 从本地 JSON 文件加载工具配置
+- api: 从远程 API 加载工具配置
+"""
+import json
+import os
+import logging
+import argparse
+import anyio
+
+import mcp.types as types
+from mcp.server import NotificationOptions, Server
+from mcp.server.models import InitializationOptions
+from mcp.server.stdio import stdio_server
+
+try:
+    from .schema_converter import convert_sql_params_to_input_schema
+    from .utils.api_client import call_api
+    from .utils.env_config import get_api_key, get_base_url
+    from .utils.logger_config import setup_system_logging, get_logger
+except ImportError:
+    from schema_converter import convert_sql_params_to_input_schema
+    from utils.api_client import call_api
+    from utils.env_config import get_api_key, get_base_url
+    from utils.logger_config import setup_system_logging, get_logger
+
+# 初始化日志系统
+setup_system_logging(app_name="lzwcai_mcpskills_template", log_level=logging.DEBUG)
+logger = get_logger(__name__)
+
+# 初始化 MCP Server
+server = Server("mcpskills_template_server")
+
+# 全局配置
+_tools_config: list[dict] = []
+_config: dict = {}
+
+
+def parse_arguments():
+    """解析命令行参数"""
+    parser = argparse.ArgumentParser(description="MCP Skills Template Server")
+    parser.add_argument(
+        "--mode",
+        type=str,
+        choices=["local", "api"],
+        default="local",
+        help="数据加载模式: local(本地JSON，默认) 或 api(远程API)"
+    )
+    parser.add_argument(
+        "--json-path",
+        type=str,
+        default=None,
+        help="本地 JSON 文件路径 (local 模式)"
+    )
+    return parser.parse_args()
+
+
+class DataLoader:
+    """数据加载器基类"""
+    
+    def load(self) -> list[dict]:
+        raise NotImplementedError
+
+
+class LocalLoader(DataLoader):
+    """本地 JSON 文件加载器"""
+    
+    def __init__(self, json_path: str = None):
+        if json_path is None:
+            json_path = os.path.join(os.path.dirname(__file__), "tools_config.json")
+        self.json_path = json_path
+    
+    def load(self) -> list[dict]:
+        try:
+            with open(self.json_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                logger.info(f"从本地加载 {len(data)} 条配置: {self.json_path}")
+                return data
+        except FileNotFoundError:
+            logger.error(f"配置文件不存在: {self.json_path}")
+            return []
+        except json.JSONDecodeError as e:
+            logger.error(f"JSON 解析错误: {e}")
+            return []
+
+
+class ApiLoader(DataLoader):
+    """API 远程加载器"""
+    
+    def __init__(self):
+        self.base_url = get_base_url()
+        self.api_key = get_api_key()
+        logger.debug(f"ApiLoader 初始化，base_url: {self.base_url}")
+    
+    def load(self) -> list[dict]:
+        try:
+            # TODO: 根据实际 API 修改此处
+            logger.info(f"开始从 API 加载工具配置")
+            response = call_api("/api/tools", method="GET")
+            
+            if response.get("code") != 200:
+                logger.error(f"获取工具配置失败: {response.get('msg')}")
+                return []
+            
+            data = response.get("data", [])
+            if isinstance(data, dict):
+                data = [data]
+            
+            logger.info(f"从 API 加载工具配置成功，数量: {len(data)}")
+            return data
+            
+        except Exception as e:
+            logger.error(f"API 请求失败: {e}", exc_info=True)
+            return []
+
+
+def create_loader(mode: str, **kwargs) -> DataLoader:
+    """创建数据加载器"""
+    if mode == "local":
+        return LocalLoader(json_path=kwargs.get("json_path"))
+    elif mode == "api":
+        return ApiLoader()
+    else:
+        raise ValueError(f"不支持的模式: {mode}")
+
+
+def init_tools(loader: DataLoader):
+    """初始化工具配置"""
+    global _tools_config
+    _tools_config = loader.load()
+    logger.info(f"已加载 {len(_tools_config)} 个工具配置")
+
+
+@server.list_tools()
+async def handle_list_tools() -> list[types.Tool]:
+    """列出所有可用工具"""
+    logger.info(f"收到 ListTools 请求，当前配置数量: {len(_tools_config)}")
+    tools = []
+    
+    for tool in _tools_config:
+        name = tool.get("name", "")
+        description = tool.get("description") or tool.get("toolPrompt", "")
+        sql_params = tool.get("sqlParams", "[]")
+        
+        # 转换参数为 inputSchema
+        input_schema = convert_sql_params_to_input_schema(sql_params)
+        
+        tools.append(
+            types.Tool(
+                name=name,
+                description=description,
+                inputSchema=input_schema
+            )
+        )
+    
+    logger.info(f"ListTools 响应: 返回 {len(tools)} 个工具")
+    return tools
+
+
+@server.call_tool()
+async def handle_call_tool(
+    name: str, 
+    arguments: dict | None
+) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
+    """调用工具"""
+    logger.info(f"收到 CallTool 请求: name={name}, arguments={json.dumps(arguments, ensure_ascii=False) if arguments else 'None'}")
+    
+    # 查找对应的工具配置
+    tool_config = None
+    for tool in _tools_config:
+        if tool.get("name") == name:
+            tool_config = tool
+            break
+    
+    if tool_config is None:
+        logger.error(f"未找到工具配置: {name}")
+        raise ValueError(f"未知工具: {name}")
+    
+    # TODO: 在这里实现你的工具逻辑
+    # 示例：简单返回参数
+    try:
+        result = execute_tool(name, arguments or {}, tool_config)
+        logger.info(f"工具执行成功: {name}")
+    except Exception as e:
+        logger.error(f"工具执行失败: {e}", exc_info=True)
+        result = {"error": str(e), "tool_name": name}
+    
+    return [
+        types.TextContent(
+            type="text",
+            text=json.dumps(result, ensure_ascii=False, indent=2)
+        )
+    ]
+
+
+def execute_tool(name: str, arguments: dict, config: dict) -> dict:
+    """
+    执行工具逻辑
+    
+    TODO: 根据实际需求修改此函数
+    """
+    # 示例实现
+    if name == "example_hello_world":
+        return {"message": f"Hello, {arguments.get('name', 'World')}!"}
+    
+    elif name == "example_calculator":
+        a = float(arguments.get("a", 0))
+        b = float(arguments.get("b", 0))
+        op = arguments.get("operation", "+")
+        
+        if op == "+":
+            result = a + b
+        elif op == "-":
+            result = a - b
+        elif op == "*":
+            result = a * b
+        elif op == "/":
+            result = a / b if b != 0 else "除数不能为0"
+        else:
+            result = "未知运算符"
+        
+        return {"result": result, "expression": f"{a} {op} {b}"}
+    
+    # 默认返回参数
+    return {
+        "tool_name": name,
+        "arguments": arguments,
+        "message": "工具执行成功（默认实现）"
+    }
+
+
+async def run_server():
+    """运行 MCP Server (stdio 模式)"""
+    async with stdio_server() as streams:
+        await server.run(
+            streams[0],
+            streams[1],
+            InitializationOptions(
+                server_name="mcpskills_template_server",
+                server_version="0.1.0",
+                capabilities=server.get_capabilities(
+                    notification_options=NotificationOptions(),
+                    experimental_capabilities={},
+                ),
+            ),
+        )
+
+
+def main():
+    """主入口"""
+    global _config
+    
+    logger.info("=" * 50)
+    logger.info("MCP Skills Template Server 启动")
+    logger.info("=" * 50)
+    
+    # 解析命令行参数
+    args = parse_arguments()
+    _config = vars(args)
+    logger.info(f"命令行参数: {_config}")
+    
+    # 创建加载器并初始化工具
+    logger.info(f"使用模式: {args.mode}")
+    loader = create_loader(
+        mode=args.mode,
+        json_path=args.json_path
+    )
+    init_tools(loader)
+    
+    logger.info("开始运行 MCP Server (stdio 模式)")
+    # 运行服务器
+    anyio.run(run_server)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/schema_converter.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/schema_converter.py
new file mode 100644
index 0000000..5f9eb7e
--- /dev/null
+++ b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/schema_converter.py
@@ -0,0 +1,168 @@
+"""
+Schema 转换器
+将 sqlParams 数组格式转换为 MCP 工具需要的 JSON Schema 格式
+
+支持的类型:
+- string: 文本输入
+- paragraph: 段落/多行文本
+- select: 下拉选项
+- number: 数字输入
+"""
+import json
+from typing import Any
+
+
+def convert_param_to_schema_property(param: dict) -> tuple[str, dict, bool]:
+    """
+    将单个参数转换为 JSON Schema property
+    
+    Args:
+        param: 参数配置，格式如:
+            {
+                "type": "string",
+                "name": "param_name",
+                "displayName": "参数显示名",
+                "maxLength": 200,
+                "defaultValue": "",
+                "required": true,
+                "options": ["选项1", "选项2"]  # 仅 select 类型
+            }
+    
+    Returns:
+        tuple: (property_name, property_schema, is_required)
+    """
+    param_type = param.get("type", "string")
+    param_name = param.get("name", "")
+    display_name = param.get("displayName", param_name)
+    default_value = param.get("defaultValue", "")
+    max_length = param.get("maxLength")
+    is_required = param.get("required", False)
+    options = param.get("options", [])
+    
+    property_schema = {
+        "description": display_name
+    }
+    
+    if param_type == "string":
+        property_schema["type"] = "string"
+        if max_length:
+            property_schema["maxLength"] = max_length
+            
+    elif param_type == "paragraph":
+        property_schema["type"] = "string"
+        property_schema["format"] = "paragraph"
+        if max_length:
+            property_schema["maxLength"] = max_length
+            
+    elif param_type == "select":
+        property_schema["type"] = "string"
+        if options:
+            property_schema["enum"] = options
+            
+    elif param_type == "number":
+        property_schema["type"] = "number"
+        
+    else:
+        # 默认当作 string 处理
+        property_schema["type"] = "string"
+    
+    # 添加默认值
+    if default_value not in (None, ""):
+        if param_type == "number":
+            try:
+                property_schema["default"] = int(default_value) if str(default_value).isdigit() else float(default_value)
+            except (ValueError, TypeError):
+                property_schema["default"] = default_value
+        else:
+            property_schema["default"] = default_value
+    
+    return param_name, property_schema, is_required
+
+
+def convert_sql_params_to_input_schema(sql_params: str | list) -> dict:
+    """
+    将 sqlParams 转换为 MCP 工具的 inputSchema
+    
+    Args:
+        sql_params: sqlParams 字段值，可以是 JSON 字符串或已解析的列表
+            格式: [{"type": "string", "name": "xxx", ...}, ...]
+    
+    Returns:
+        dict: MCP 工具的 inputSchema，格式如:
+            {
+                "type": "object",
+                "properties": {...},
+                "required": [...]
+            }
+    """
+    # 解析 JSON 字符串
+    if isinstance(sql_params, str):
+        try:
+            params_list = json.loads(sql_params)
+        except json.JSONDecodeError:
+            return {"type": "object", "properties": {}, "required": []}
+    else:
+        params_list = sql_params
+    
+    if not isinstance(params_list, list):
+        return {"type": "object", "properties": {}, "required": []}
+    
+    input_schema = {
+        "type": "object",
+        "properties": {},
+        "required": []
+    }
+    
+    for param in params_list:
+        if not isinstance(param, dict):
+            continue
+            
+        name, schema, is_required = convert_param_to_schema_property(param)
+        
+        if name:
+            input_schema["properties"][name] = schema
+            if is_required:
+                input_schema["required"].append(name)
+    
+    return input_schema
+
+
+def convert_tool_config_to_mcp_tool(config: dict) -> dict:
+    """
+    将单个工具配置转换为 MCP Tool 配置
+    
+    Args:
+        config: 工具配置对象
+    
+    Returns:
+        dict: MCP Tool 配置
+    """
+    name = config.get("name", "")
+    description = config.get("description") or config.get("toolPrompt", "")
+    sql_params = config.get("sqlParams", "[]")
+    
+    input_schema = convert_sql_params_to_input_schema(sql_params)
+    
+    return {
+        "name": name,
+        "description": description,
+        "inputSchema": input_schema,
+        "_raw": config  # 保留原始数据
+    }
+
+
+# 测试用
+if __name__ == "__main__":
+    # 测试单个参数转换
+    test_param = {
+        "type": "select",
+        "name": "operation",
+        "displayName": "运算符",
+        "required": True,
+        "options": ["+", "-", "*", "/"]
+    }
+    
+    name, schema, required = convert_param_to_schema_property(test_param)
+    print(f"参数名: {name}")
+    print(f"Schema: {json.dumps(schema, ensure_ascii=False, indent=2)}")
+    print(f"必填: {required}")
diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/tools_config.json b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/tools_config.json
new file mode 100644
index 0000000..f8ae9a4
--- /dev/null
+++ b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/tools_config.json
@@ -0,0 +1,16 @@
+[
+  {
+    "id": "example_tool_001",
+    "name": "example_hello_world",
+    "description": "示例工具 - Hello World",
+    "toolPrompt": "这是一个示例工具，用于演示 MCP 工具的基本结构",
+    "sqlParams": "[{\"type\":\"string\",\"name\":\"name\",\"displayName\":\"名称\",\"maxLength\":100,\"defaultValue\":\"World\",\"required\":true}]"
+  },
+  {
+    "id": "example_tool_002",
+    "name": "example_calculator",
+    "description": "示例工具 - 简单计算器",
+    "toolPrompt": "执行简单的数学计算",
+    "sqlParams": "[{\"type\":\"number\",\"name\":\"a\",\"displayName\":\"数字A\",\"required\":true},{\"type\":\"number\",\"name\":\"b\",\"displayName\":\"数字B\",\"required\":true},{\"type\":\"select\",\"name\":\"operation\",\"displayName\":\"运算符\",\"required\":true,\"options\":[\"+\",\"-\",\"*\",\"/\"]}]"
+  }
+]
\ No newline at end of file
diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/__init__.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/__init__.py
new file mode 100644
index 0000000..1b74368
--- /dev/null
+++ b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/__init__.py
@@ -0,0 +1,19 @@
+"""Utils package for lzwcai_mcpskills_template"""
+
+from .json_helper import load_json
+from .api_client import call_api, APIClient, get_default_client
+from .env_config import get_api_key, get_base_url, get_env_config, set_env_variable
+from .logger_config import setup_system_logging, get_logger
+
+__all__ = [
+    'load_json',
+    'call_api',
+    'APIClient',
+    'get_default_client',
+    'get_api_key',
+    'get_base_url',
+    'get_env_config',
+    'set_env_variable',
+    'setup_system_logging',
+    'get_logger'
+]
diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/api_client.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/api_client.py
new file mode 100644
index 0000000..cd5cabd
--- /dev/null
+++ b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/api_client.py
@@ -0,0 +1,197 @@
+"""
+API 调用客户端
+通用的 HTTP API 客户端封装
+"""
+
+import httpx
+import json
+from typing import Dict, Any, Optional
+
+try:
+    from .env_config import get_base_url, get_api_key
+    from .logger_config import get_logger
+except ImportError:
+    from env_config import get_base_url, get_api_key
+    from logger_config import get_logger
+
+logger = get_logger(__name__)
+
+# 默认超时配置（秒）
+DEFAULT_TIMEOUT = 30.0
+LONG_TIMEOUT = 300.0
+
+
+class APIClient:
+    """通用 API 客户端"""
+    
+    def __init__(
+        self, 
+        base_url: Optional[str] = None,
+        api_key: Optional[str] = None,
+        default_timeout: float = DEFAULT_TIMEOUT
+    ):
+        """
+        初始化 API 客户端
+        
+        Args:
+            base_url: API 基础 URL（默认从环境变量读取）
+            api_key: API 密钥（默认从环境变量读取）
+            default_timeout: 默认超时时间（秒）
+        """
+        self.base_url = (base_url or get_base_url()).rstrip('/')
+        self.api_key = api_key or get_api_key()
+        self.default_timeout = default_timeout
+        self._client: Optional[httpx.Client] = None
+        
+        logger.info(f"[客户端初始化] base_url={self.base_url}")
+    
+    @property
+    def client(self) -> httpx.Client:
+        """懒加载 HTTP 客户端"""
+        if self._client is None:
+            self._client = httpx.Client(timeout=self.default_timeout)
+        return self._client
+        
+    def _get_headers(self) -> Dict[str, str]:
+        """获取请求头"""
+        headers = {
+            'Content-Type': 'application/json',
+            'Accept': 'application/json'
+        }
+        if self.api_key:
+            headers['X-API-Key'] = self.api_key
+        return headers
+    
+    def request(
+        self, 
+        endpoint: str, 
+        method: str = "GET",
+        data: Optional[Dict[str, Any]] = None,
+        params: Optional[Dict[str, Any]] = None,
+        timeout: Optional[float] = None
+    ) -> Dict[str, Any]:
+        """
+        发送 HTTP 请求
+        
+        Args:
+            endpoint: API 端点路径
+            method: HTTP 方法
+            data: 请求体数据
+            params: URL 查询参数
+            timeout: 超时时间
+            
+        Returns:
+            API 响应数据
+        """
+        url = f"{self.base_url}{endpoint}"
+        timeout = timeout or self.default_timeout
+        
+        try:
+            logger.info(f"[API请求] {method} {url}")
+            
+            if method.upper() == "GET":
+                response = self.client.get(
+                    url,
+                    headers=self._get_headers(),
+                    params=params,
+                    timeout=timeout
+                )
+            elif method.upper() == "POST":
+                response = self.client.post(
+                    url,
+                    headers=self._get_headers(),
+                    json=data,
+                    params=params,
+                    timeout=timeout
+                )
+            elif method.upper() == "PUT":
+                response = self.client.put(
+                    url,
+                    headers=self._get_headers(),
+                    json=data,
+                    params=params,
+                    timeout=timeout
+                )
+            elif method.upper() == "DELETE":
+                response = self.client.delete(
+                    url,
+                    headers=self._get_headers(),
+                    params=params,
+                    timeout=timeout
+                )
+            else:
+                raise ValueError(f"不支持的 HTTP 方法: {method}")
+            
+            logger.info(f"[API响应] HTTP {response.status_code}")
+            response.raise_for_status()
+            
+            return response.json()
+            
+        except httpx.TimeoutException:
+            error_msg = f"API 请求超时: {url}"
+            logger.error(f"[API错误] {error_msg}")
+            raise Exception(error_msg)
+            
+        except httpx.HTTPStatusError as e:
+            error_msg = f"API 请求失败 (HTTP {e.response.status_code}): {url}"
+            logger.error(f"[API错误] {error_msg}")
+            raise Exception(error_msg)
+            
+        except Exception as e:
+            error_msg = f"API 请求异常: {str(e)}"
+            logger.error(f"[API错误] {error_msg}", exc_info=True)
+            raise Exception(error_msg)
+    
+    def close(self):
+        """关闭 HTTP 客户端"""
+        if self._client is not None:
+            self._client.close()
+            self._client = None
+    
+    def __enter__(self):
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
+        return False
+
+
+# 懒加载的默认客户端
+_default_client: Optional[APIClient] = None
+
+
+def get_default_client() -> APIClient:
+    """获取默认客户端（懒加载）"""
+    global _default_client
+    if _default_client is None:
+        _default_client = APIClient()
+    return _default_client
+
+
+def call_api(
+    endpoint: str,
+    method: str = "GET",
+    data: Optional[Dict[str, Any]] = None,
+    params: Optional[Dict[str, Any]] = None,
+    timeout: Optional[float] = None
+) -> Dict[str, Any]:
+    """
+    便捷函数：调用 API
+    
+    Args:
+        endpoint: API 端点路径
+        method: HTTP 方法
+        data: 请求体数据
+        params: URL 查询参数
+        timeout: 超时时间
+        
+    Returns:
+        API 响应数据
+    """
+    return get_default_client().request(
+        endpoint=endpoint,
+        method=method,
+        data=data,
+        params=params,
+        timeout=timeout
+    )
diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/env_config.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/env_config.py
new file mode 100644
index 0000000..fba59a9
--- /dev/null
+++ b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/env_config.py
@@ -0,0 +1,60 @@
+"""环境变量配置模块"""
+
+import os
+from typing import Optional
+
+
+def get_api_key(default: str = "") -> str:
+    """
+    获取 API 密钥环境变量
+    
+    Args:
+        default: 默认值
+        
+    Returns:
+        str: API 密钥
+        
+    Environment Variables:
+        API_KEY: API 密钥
+    """
+    return os.environ.get("API_KEY", default)
+
+
+def get_base_url(default: str = "http://localhost:8080") -> str:
+    """
+    获取后端 API 基础 URL 环境变量
+    
+    Args:
+        default: 默认值
+        
+    Returns:
+        str: 后端 API 基础 URL
+        
+    Environment Variables:
+        BASE_URL: 后端 API 基础 URL
+    """
+    return os.environ.get("BASE_URL", default)
+
+
+def get_env_config() -> dict:
+    """
+    获取所有环境配置
+    
+    Returns:
+        dict: 包含所有配置的字典
+    """
+    return {
+        "api_key": get_api_key(),
+        "base_url": get_base_url()
+    }
+
+
+def set_env_variable(key: str, value: str) -> None:
+    """
+    设置环境变量（仅在当前进程中有效）
+    
+    Args:
+        key: 环境变量名
+        value: 环境变量值
+    """
+    os.environ[key] = value
diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/json_helper.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/json_helper.py
new file mode 100644
index 0000000..00ac6cc
--- /dev/null
+++ b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/json_helper.py
@@ -0,0 +1,41 @@
+"""JSON 文件读取工具"""
+
+import json
+from pathlib import Path
+from typing import Any, Union
+
+
+def load_json(json_path: Union[str, Path]) -> Any:
+    """
+    读取 JSON 文件并返回其内容
+    
+    Args:
+        json_path: JSON 文件的路径
+        
+    Returns:
+        JSON 文件中解析后的数据
+        
+    Raises:
+        FileNotFoundError: 当文件不存在时
+        json.JSONDecodeError: 当 JSON 格式无效时
+    """
+    try:
+        path = Path(json_path)
+        
+        if not path.exists():
+            raise FileNotFoundError(f"JSON 文件不存在: {json_path}")
+        
+        if not path.is_file():
+            raise ValueError(f"路径不是一个文件: {json_path}")
+        
+        with open(path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+        
+        return data
+        
+    except json.JSONDecodeError as e:
+        raise json.JSONDecodeError(f"JSON 格式错误: {e.msg}", e.doc, e.pos)
+    except FileNotFoundError:
+        raise
+    except Exception as e:
+        raise Exception(f"读取 JSON 文件时发生错误: {str(e)}")
diff --git a/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/logger_config.py b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/logger_config.py
new file mode 100644
index 0000000..b423c64
--- /dev/null
+++ b/lzwcai_mcpskills_template/lzwcai_mcpskills_template/utils/logger_config.py
@@ -0,0 +1,130 @@
+# -*- coding: utf-8 -*-
+"""
+统一日志配置模块
+提供系统级别的日志配置和管理
+"""
+
+import os
+import sys
+import logging
+from logging.handlers import RotatingFileHandler, TimedRotatingFileHandler
+from pathlib import Path
+
+
+class LoggerConfig:
+    """日志配置管理类"""
+    
+    def __init__(self, logs_dir: str = None):
+        """初始化日志配置"""
+        if logs_dir:
+            self.logs_dir = Path(logs_dir)
+        else:
+            project_root = Path(__file__).parent.parent
+            self.logs_dir = project_root / "logs"
+        
+        self.logs_dir.mkdir(exist_ok=True)
+        
+        self.log_format = '%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s'
+        self.date_format = '%Y-%m-%d %H:%M:%S'
+        self.log_level = self._get_log_level_from_env()
+        self._initialized = False
+    
+    def _get_log_level_from_env(self) -> int:
+        log_level_str = os.getenv('LOG_LEVEL', 'INFO').upper()
+        level_mapping = {
+            'DEBUG': logging.DEBUG,
+            'INFO': logging.INFO,
+            'WARNING': logging.WARNING,
+            'WARN': logging.WARNING,
+            'ERROR': logging.ERROR,
+            'CRITICAL': logging.CRITICAL,
+            'FATAL': logging.CRITICAL
+        }
+        return level_mapping.get(log_level_str, logging.INFO)
+    
+    def setup_logging(self, 
+                     app_name: str = "lzwcai_mcpskills_template",
+                     log_level: int = logging.INFO,
+                     max_file_size: int = 10 * 1024 * 1024,
+                     backup_count: int = 5,
+                     console_output: bool = True) -> logging.Logger:
+        if self._initialized:
+            return logging.getLogger()
+        
+        root_logger = logging.getLogger()
+        root_logger.setLevel(log_level)
+        
+        for handler in root_logger.handlers[:]:
+            root_logger.removeHandler(handler)
+        
+        formatter = logging.Formatter(self.log_format, self.date_format)
+
+        # 1. 主日志文件 - 按大小滚动
+        main_log_file = self.logs_dir / f"{app_name}.log"
+        file_handler = RotatingFileHandler(
+            main_log_file,
+            maxBytes=max_file_size,
+            backupCount=backup_count,
+            encoding='utf-8'
+        )
+        file_handler.setLevel(log_level)
+        file_handler.setFormatter(formatter)
+        root_logger.addHandler(file_handler)
+        
+        # 2. 错误日志文件
+        error_log_file = self.logs_dir / f"{app_name}_error.log"
+        error_handler = RotatingFileHandler(
+            error_log_file,
+            maxBytes=max_file_size,
+            backupCount=backup_count,
+            encoding='utf-8'
+        )
+        error_handler.setLevel(logging.ERROR)
+        error_handler.setFormatter(formatter)
+        root_logger.addHandler(error_handler)
+        
+        # 3. 按日期滚动的日志文件
+        daily_log_file = self.logs_dir / f"{app_name}_daily.log"
+        daily_handler = TimedRotatingFileHandler(
+            daily_log_file,
+            when='midnight',
+            interval=1,
+            backupCount=30,
+            encoding='utf-8'
+        )
+        daily_handler.setLevel(log_level)
+        daily_handler.setFormatter(formatter)
+        daily_handler.suffix = "%Y-%m-%d"
+        root_logger.addHandler(daily_handler)
+        
+        # 4. 控制台输出 (MCP协议使用stdio时，必须将日志输出到stderr)
+        if console_output:
+            console_handler = logging.StreamHandler(sys.stderr)
+            console_handler.setLevel(log_level)
+            console_formatter = logging.Formatter(
+                '%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+                self.date_format
+            )
+            console_handler.setFormatter(console_formatter)
+            root_logger.addHandler(console_handler)
+        
+        self._initialized = True
+        root_logger.info(f"日志系统初始化完成 - 日志目录: {self.logs_dir}")
+        
+        return root_logger
+    
+    def get_module_logger(self, module_name: str) -> logging.Logger:
+        return logging.getLogger(module_name)
+
+
+# 全局日志配置实例
+logger_config = LoggerConfig()
+
+
+def setup_system_logging(app_name: str = "lzwcai_mcpskills_template", 
+                        log_level: int = logging.INFO) -> logging.Logger:
+    return logger_config.setup_logging(app_name, log_level)
+
+
+def get_logger(name: str) -> logging.Logger:
+    return logger_config.get_module_logger(name)
diff --git a/lzwcai_mcpskills_template/main.py b/lzwcai_mcpskills_template/main.py
new file mode 100644
index 0000000..c79f492
--- /dev/null
+++ b/lzwcai_mcpskills_template/main.py
@@ -0,0 +1,15 @@
+"""
+Entry point for lzwcai-mcpskills-template
+MCP Server 模板项目入口
+"""
+
+import os
+
+if __name__ == "__main__":
+    # 设置环境变量（根据实际需求修改）
+    os.environ["API_KEY"] = "your-api-key"
+    os.environ["BASE_URL"] = "http://localhost:8080"
+    
+    # Import and run the actual MCP server
+    from lzwcai_mcpskills_template.main import main
+    main()
diff --git a/lzwcai_mcpskills_template/pyproject.toml b/lzwcai_mcpskills_template/pyproject.toml
new file mode 100644
index 0000000..6bd929f
--- /dev/null
+++ b/lzwcai_mcpskills_template/pyproject.toml
@@ -0,0 +1,31 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "lzwcai_mcpskills_template"
+version = "0.1.0"
+description = "MCP Server 模板项目 - 用于快速创建新的 MCP 服务"
+readme = "README.md"
+requires-python = ">=3.13"
+license = {text = "MIT"}
+authors = [
+    {name = "lzwcai", email = "your-email@example.com"},
+]
+keywords = ["mcp", "template", "server"]
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.13",
+]
+dependencies = [
+    "httpx>=0.28.1",
+    "mcp[cli]>=1.10.1",
+]
+
+[project.scripts]
+lzwcai_mcpskills_template = "lzwcai_mcpskills_template.main:main"
+
+[tool.hatch.build.targets.wheel]
+packages = ["lzwcai_mcpskills_template"]