fix: 利润机会池改为门店级计算，修复整体平均掩盖问题门店

问题：利润机会池6项中有4项返回0，总额仅15.3万
原因：用整体平均费率判断是否超标，整体已达标则机会=0，
      掩盖了个别门店严重超标的问题

修复：
1. 食材成本差异回收：改用 mv_store_theoretical_actual_cost_monthly
   （门店级理论vs实际成本），只对正差异（超耗）门店求和 × 30%
   修复前 0 → 修复后 1,898,587
2. 标准店人工优化：改为门店级，只对费率>24%的门店计算机会
   修复前 0 → 修复后 1,561,133（58家超标）
3. 标准店能源优化：改为门店级，只对费率>5%的门店计算机会
   修复前 0 → 修复后 466,609（49家超标）
4. 平台佣金优化：改为门店级，只对佣金率>20%的门店计算机会
   修复前 0 → 修复后 3,430（1家超标）
5. 高优惠门店治理和SKU复杂度压缩逻辑不变

总额：152,995 → 4,082,755（约408万）

Generated with [Devin](https://devin.ai)

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>

server/src/routes/data.ts

@@ -1144,6 +1144,7 @@ router.get('/overview/store-profit-ranking', async (req: AuthRequest, res) => {
 })
 
 // 利润机会池
+// 门店级计算：只对超标门店计算机会金额，避免整体平均掩盖问题门店
 router.get('/overview/profit-opportunity', async (req: AuthRequest, res) => {
   try {
     const month = parseMonth(req)
@@ -1154,64 +1155,67 @@ router.get('/overview/profit-opportunity', async (req: AuthRequest, res) => {
       sku_abc AS (
         SELECT * FROM analytics.mv_dish_sku_abc_monthly WHERE month_start = $1
       ),
-      standard_stores AS (
-        SELECT r.store_code, r.store_name, r.received,
-               COALESCE(e.actual_food_cost, 0) AS actual_food_cost,
-               COALESCE(e.operating_expense, 0) AS operating_expense,
-               COALESCE(e.wage_expense, 0) AS wage_expense,
-               COALESCE(e.utility_expense, 0) AS utility_expense,
-               r.theoretical_margin_pct, r.discount_rate_pct
-        FROM risk r
-        LEFT JOIN analytics.mv_store_operating_expense_monthly e
-          ON r.store_code = e.sales_store_code AND e.report_month = $1::date
-        WHERE r.received IS NOT NULL AND r.received > 0
-      ),
+      -- 食材成本差异：改用 mv_store_theoretical_actual_cost_monthly（门店级理论vs实际成本）
       cost_diff AS (
         SELECT
-          round(sum(actual_food_cost)::numeric, 2) AS actual_cost,
-          round(sum(received * (1 - COALESCE(theoretical_margin_pct, 0) / 100))::numeric, 2) AS theoretical_cost
-        FROM standard_stores
+          count(*) FILTER (WHERE food_cost_variance > 0) AS over_cost_stores,
+          round(sum(food_cost_variance) FILTER (WHERE food_cost_variance > 0)::numeric, 2) AS total_positive_variance,
+          round(sum(theoretical_cost)::numeric, 2) AS total_theoretical_cost,
+          round(sum(actual_food_cost)::numeric, 2) AS total_actual_cost,
+          round(sum(sales_received)::numeric, 2) AS total_received
+        FROM analytics.mv_store_theoretical_actual_cost_monthly
+        WHERE month_start = $1
       ),
       cost_diff_stores AS (
-        SELECT store_name, received,
-          round(actual_food_cost::numeric, 2) AS actual_food_cost,
-          round((received * (1 - COALESCE(theoretical_margin_pct, 0) / 100))::numeric, 2) AS theoretical_cost,
-          round((actual_food_cost - received * (1 - COALESCE(theoretical_margin_pct, 0) / 100))::numeric, 2) AS diff_amount,
-          round((actual_food_cost / nullif(received, 0) * 100)::numeric, 2) AS actual_cost_rate,
-          round((received * (1 - COALESCE(theoretical_margin_pct, 0) / 100) / nullif(received, 0) * 100)::numeric, 2) AS theoretical_cost_rate
-        FROM standard_stores
-        WHERE actual_food_cost IS NOT NULL
-        ORDER BY (actual_food_cost - received * (1 - COALESCE(theoretical_margin_pct, 0) / 100)) DESC
+        SELECT store_name, sales_received AS received,
+          round(food_cost_variance::numeric, 2) AS diff_amount,
+          round(actual_food_cost_rate_pct::numeric, 2) AS actual_cost_rate,
+          round(theoretical_cost_rate_pct::numeric, 2) AS theoretical_cost_rate
+        FROM analytics.mv_store_theoretical_actual_cost_monthly
+        WHERE month_start = $1 AND food_cost_variance > 0
+        ORDER BY food_cost_variance DESC
         LIMIT 5
       ),
+      -- 人工优化：门店级，只对费率>24%的门店计算机会
       labor AS (
         SELECT
+          count(*) FILTER (WHERE wage_rate_pct > 24) AS over_wage_stores,
+          round(sum(wage_expense) FILTER (WHERE wage_rate_pct > 24)::numeric, 2) AS over_wage_total,
+          round(sum(wage_expense * GREATEST(1 - 24.0 / nullif(wage_rate_pct, 0), 0)) FILTER (WHERE wage_rate_pct > 24)::numeric, 2) AS opportunity,
           round(sum(wage_expense)::numeric, 2) AS total_wage,
           round(sum(received)::numeric, 2) AS total_received
-        FROM standard_stores
+        FROM analytics.mv_store_operating_expense_monthly
+        WHERE report_month = $1::date AND wage_expense IS NOT NULL AND wage_expense > 0 AND received > 0
       ),
       labor_stores AS (
-        SELECT store_name, received,
+        SELECT sales_store_name AS store_name, received,
           round(wage_expense::numeric, 2) AS wage,
-          round((wage_expense / nullif(received, 0) * 100)::numeric, 2) AS wage_rate
-        FROM standard_stores
-        WHERE wage_expense IS NOT NULL
-        ORDER BY wage_expense / nullif(received, 0) DESC
+          round(wage_rate_pct::numeric, 2) AS wage_rate
+        FROM analytics.mv_store_operating_expense_monthly
+        WHERE report_month = $1::date AND wage_expense IS NOT NULL AND wage_expense > 0 AND received > 0
+          AND wage_rate_pct > 24
+        ORDER BY wage_rate_pct DESC
         LIMIT 5
       ),
+      -- 能源优化：门店级，只对费率>5%的门店计算机会
       energy AS (
         SELECT
+          count(*) FILTER (WHERE utility_rate_pct > 5) AS over_utility_stores,
+          round(sum(utility_expense) FILTER (WHERE utility_rate_pct > 5)::numeric, 2) AS over_utility_total,
+          round(sum(utility_expense * GREATEST(1 - 5.0 / nullif(utility_rate_pct, 0), 0)) FILTER (WHERE utility_rate_pct > 5)::numeric, 2) AS opportunity,
           round(sum(utility_expense)::numeric, 2) AS total_utility,
           round(sum(received)::numeric, 2) AS total_received
-        FROM standard_stores
+        FROM analytics.mv_store_operating_expense_monthly
+        WHERE report_month = $1::date AND utility_expense IS NOT NULL AND utility_expense > 0 AND received > 0
       ),
       energy_stores AS (
-        SELECT store_name, received,
+        SELECT sales_store_name AS store_name, received,
           round(utility_expense::numeric, 2) AS utility,
-          round((utility_expense / nullif(received, 0) * 100)::numeric, 2) AS utility_rate
-        FROM standard_stores
-        WHERE utility_expense IS NOT NULL
-        ORDER BY utility_expense / nullif(received, 0) DESC
+          round(utility_rate_pct::numeric, 2) AS utility_rate
+        FROM analytics.mv_store_operating_expense_monthly
+        WHERE report_month = $1::date AND utility_expense IS NOT NULL AND utility_expense > 0 AND received > 0
+          AND utility_rate_pct > 5
+        ORDER BY utility_rate_pct DESC
         LIMIT 5
       ),
       discount AS (
@@ -1230,19 +1234,22 @@ router.get('/overview/profit-opportunity', async (req: AuthRequest, res) => {
         ORDER BY discount_rate_pct DESC
         LIMIT 5
       ),
+      -- 平台佣金优化：门店级，只对佣金率>20%的门店计算机会
       platform AS (
         SELECT
+          count(*) FILTER (WHERE delivery_commission_expense > 0 AND delivery_commission_expense / nullif(received, 0) * 100 > 20) AS over_commission_stores,
           round(sum(delivery_commission_expense)::numeric, 2) AS total_commission,
-          round(sum(received)::numeric, 2) AS platform_received
+          round(sum(received)::numeric, 2) AS platform_received,
+          round(sum(delivery_commission_expense * GREATEST(1 - 20.0 / nullif(delivery_commission_expense / nullif(received, 0) * 100, 0), 0)) FILTER (WHERE delivery_commission_expense > 0 AND delivery_commission_expense / nullif(received, 0) * 100 > 20)::numeric, 2) AS opportunity
         FROM analytics.mv_store_operating_expense_monthly
-        WHERE report_month = $1::date AND delivery_commission_expense IS NOT NULL
+        WHERE report_month = $1::date AND delivery_commission_expense IS NOT NULL AND received > 0
       ),
       platform_stores AS (
         SELECT e.sales_store_code AS store_code, e.received,
           round(e.delivery_commission_expense::numeric, 2) AS commission,
           round((e.delivery_commission_expense / nullif(e.received, 0) * 100)::numeric, 2) AS commission_rate
         FROM analytics.mv_store_operating_expense_monthly e
-        WHERE e.report_month = $1::date AND e.delivery_commission_expense IS NOT NULL
+        WHERE e.report_month = $1::date AND e.delivery_commission_expense IS NOT NULL AND e.received > 0
         ORDER BY e.delivery_commission_expense / nullif(e.received, 0) DESC
         LIMIT 5
       ),
@@ -1264,47 +1271,47 @@ router.get('/overview/profit-opportunity', async (req: AuthRequest, res) => {
           'items', json_build_array(
             json_build_object(
               'category', '食材成本差异回收',
-              'baseline', (SELECT round((actual_cost - theoretical_cost)::numeric, 2) FROM cost_diff),
+              'baseline', (SELECT total_positive_variance FROM cost_diff),
               'target_pct', 30,
-              'opportunity', GREATEST(round((SELECT (actual_cost - theoretical_cost) FROM cost_diff) * 0.30, 2), 0),
+              'opportunity', round((SELECT total_positive_variance FROM cost_diff) * 0.30, 2),
               'confidence', '中高',
               'owner', '商品/供应链/门店',
               'evidence', '采购价差、用量差、盘点差、报损差',
               'detail', (SELECT
-                '实际食材成本' || (SELECT actual_cost FROM cost_diff) || '元 vs 理论成本' || (SELECT theoretical_cost FROM cost_diff) || '元，差异' || round((SELECT actual_cost - theoretical_cost FROM cost_diff)::numeric, 2) || '元（成本率' || round((SELECT actual_cost FROM cost_diff) / nullif((SELECT sum(received) FROM standard_stores), 0) * 100, 2) || '% vs 理论' || round((SELECT theoretical_cost FROM cost_diff) / nullif((SELECT sum(received) FROM standard_stores), 0) * 100, 2) || '%）。\n\n' ||
-                '差异TOP5门店（需优先排查）：\n' ||
+                '超耗门店' || (SELECT over_cost_stores FROM cost_diff) || '家，实际食材成本' || (SELECT total_actual_cost FROM cost_diff) || '元 vs 理论成本' || (SELECT total_theoretical_cost FROM cost_diff) || '元，正差异合计' || (SELECT total_positive_variance FROM cost_diff) || '元。\n\n' ||
+                '超耗TOP5门店（需优先排查）：\n' ||
                 string_agg(store_name || '：差异' || diff_amount || '元（实际成本率' || actual_cost_rate || '% vs 理论' || theoretical_cost_rate || '%，实收' || received || '元）', '；\n') ||
-                '\n\n行动指向：\n1）上述5家门店实际成本率均远超理论值，需逐店排查采购单价与BOM标准价差异；\n2）盘点差——核查月末盘点与系统库存一致性，差异>3%需复盘；\n3）报损差——对比报损记录与行业基准，报损率>2%的门店需检查存储和加工流程；\n4）30天目标：将TOP5门店成本率降低5个百分点，预计回收' || round((SELECT (actual_cost - theoretical_cost) FROM cost_diff) * 0.30, 2) || '元。'
+                '\n\n行动指向：\n1）上述5家门店实际成本率均远超理论值，需逐店排查采购单价与BOM标准价差异；\n2）盘点差——核查月末盘点与系统库存一致性，差异>3%需复盘；\n3）报损差——对比报损记录与行业基准，报损率>2%的门店需检查存储和加工流程；\n4）30天目标：回收正差异的30%，预计节约' || round((SELECT total_positive_variance FROM cost_diff) * 0.30, 2) || '元。'
                 FROM cost_diff_stores)
             ),
             json_build_object(
               'category', '标准店人工优化',
-              'baseline', (SELECT round(total_wage / total_received * 100, 2) FROM labor),
+              'baseline', (SELECT round(total_wage / nullif(total_received, 0) * 100, 2) FROM labor),
               'target_pct', 100,
-              'opportunity', GREATEST(round((SELECT total_wage * (1 - 24.0 / nullif(total_wage / total_received * 100, 0)) FROM labor), 2), 0),
+              'opportunity', (SELECT opportunity FROM labor),
               'confidence', '中',
               'owner', '运营/人力',
               'evidence', '工时、工资、餐段销售与服务质量',
               'detail', (SELECT
-                '标准店人工合计' || (SELECT total_wage FROM labor) || '元，费率' || round((SELECT total_wage FROM labor) / nullif((SELECT total_received FROM labor), 0) * 100, 2) || '%，目标降至24%。\n\n' ||
-                '人工费率TOP5门店（需重点督导）：\n' ||
-                string_agg(store_name || '：人工' || wage || '元（费率' || wage_rate || '%，实收' || received || '元）', '；\n') ||
-                '\n\n行动指向：\n1）上述门店人工费率远超24%目标，需核查排班与实际打卡工时，识别冗余工时；\n2）低峰时段用小时工替代月薪员工，预计可降费率2-3个百分点；\n3）对费率>30%的门店启动人效专项督导，要求店长提交排班优化方案；\n4）30天目标：TOP5门店人工费率平均降低2个百分点。'
+                '人工费率超24%的门店共' || (SELECT over_wage_stores FROM labor) || '家，超标门店人工合计' || (SELECT over_wage_total FROM labor) || '元，理论可优化' || (SELECT opportunity FROM labor) || '元。\n\n' ||
+                '人工费率TOP5超标门店（需重点督导）：\n' ||
+                COALESCE(string_agg(store_name || '：人工' || wage || '元（费率' || wage_rate || '%，实收' || received || '元）', '；\n'), '无超标门店') ||
+                '\n\n行动指向：\n1）上述门店人工费率远超24%目标，需核查排班与实际打卡工时，识别冗余工时；\n2）低峰时段用小时工替代月薪员工，预计可降费率2-3个百分点；\n3）对费率>30%的门店启动人效专项督导，要求店长提交排班优化方案；\n4）30天目标：将超标门店人工费率降至24%以内。'
                 FROM labor_stores)
             ),
             json_build_object(
               'category', '标准店能源优化',
-              'baseline', (SELECT round(total_utility / total_received * 100, 2) FROM energy),
+              'baseline', (SELECT round(total_utility / nullif(total_received, 0) * 100, 2) FROM energy),
               'target_pct', 100,
-              'opportunity', GREATEST(round((SELECT total_utility * (1 - 5.0 / nullif(total_utility / total_received * 100, 0)) FROM energy), 2), 0),
+              'opportunity', (SELECT opportunity FROM energy),
               'confidence', '中',
               'owner', '工程/门店',
               'evidence', '账单/抄表、面积、营业时长',
               'detail', (SELECT
-                '标准店水电合计' || (SELECT total_utility FROM energy) || '元，费率' || round((SELECT total_utility FROM energy) / nullif((SELECT total_received FROM energy), 0) * 100, 2) || '%，目标降至5%。\n\n' ||
-                '水电费率TOP5门店（需排查设备）：\n' ||
-                string_agg(store_name || '：水电' || utility || '元（费率' || utility_rate || '%，实收' || received || '元）', '；\n') ||
-                '\n\n行动指向：\n1）上述门店水电费率远超5%目标，需排查是否存在设备老化、管道泄漏或空调空转；\n2）对比近3个月水电账单，波动>20%的门店需现场检查；\n3）缩短非营业时段的照明和空调，预计可降费率0.3-0.5个百分点；\n4）30天目标：TOP5门店水电费率平均降低0.5个百分点。'
+                '水电费率超5%的门店共' || (SELECT over_utility_stores FROM energy) || '家，超标门店水电合计' || (SELECT over_utility_total FROM energy) || '元，理论可优化' || (SELECT opportunity FROM energy) || '元。\n\n' ||
+                '水电费率TOP5超标门店（需排查设备）：\n' ||
+                COALESCE(string_agg(store_name || '：水电' || utility || '元（费率' || utility_rate || '%，实收' || received || '元）', '；\n'), '无超标门店') ||
+                '\n\n行动指向：\n1）上述门店水电费率远超5%目标，需排查是否存在设备老化、管道泄漏或空调空转；\n2）对比近3个月水电账单，波动>20%的门店需现场检查；\n3）缩短非营业时段的照明和空调，预计可降费率0.3-0.5个百分点；\n4）30天目标：将超标门店水电费率降至5%以内。'
                 FROM energy_stores)
             ),
             json_build_object(
@@ -1326,15 +1333,15 @@ router.get('/overview/profit-opportunity', async (req: AuthRequest, res) => {
               'category', '平台佣金优化',
               'baseline', (SELECT round(total_commission / nullif(platform_received, 0) * 100, 2) FROM platform),
               'target_pct', 100,
-              'opportunity', GREATEST(round((SELECT total_commission * (1 - 20.0 / nullif(total_commission / nullif(platform_received, 0) * 100, 0)) FROM platform), 2), 0),
+              'opportunity', (SELECT opportunity FROM platform),
               'confidence', '中低',
               'owner', '外卖/采购',
               'evidence', '平台结算单与订单对账',
               'detail', (SELECT
-                '平台佣金合计' || (SELECT total_commission FROM platform) || '元，佣金率' || round((SELECT total_commission FROM platform) / nullif((SELECT platform_received FROM platform), 0) * 100, 2) || '%。\n\n' ||
+                '平台佣金合计' || (SELECT total_commission FROM platform) || '元，整体佣金率' || round((SELECT total_commission FROM platform) / nullif((SELECT platform_received FROM platform), 0) * 100, 2) || '%，佣金率超20%的门店' || (SELECT over_commission_stores FROM platform) || '家。\n\n' ||
                 '佣金费率TOP5门店（需对账核查）：\n' ||
                 COALESCE(string_agg(store_code || '：佣金' || commission || '元（费率' || commission_rate || '%，实收' || received || '元）', '；\n'), '无数据') ||
-                '\n\n行动指向：\n1）当前整体佣金率6.08%已低于20%目标，暂无大幅优化空间；\n2）上述门店佣金费率偏高，需逐月核对平台结算单与订单明细，识别多扣佣金；\n3）平台活动费与佣金应分离核算，避免活动费被计入佣金；\n4）提升自配送比例，降低对平台配送依赖；\n5）30天目标：完成TOP5门店平台结算单对账。'
+                '\n\n行动指向：\n1）佣金率超20%的门店需逐月核对平台结算单与订单明细，识别多扣佣金；\n2）平台活动费与佣金应分离核算，避免活动费被计入佣金；\n3）提升自配送比例，降低对平台配送依赖；\n4）30天目标：完成TOP5门店平台结算单对账。'
                 FROM platform_stores)
             ),
             json_build_object(