feat: 月度模式全面参数化 - 移除硬编码日期，前后端按月动态查询
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freedakgmail
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-- ============================================================
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-- Phase 5: SKU/菜品参数化函数
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-- 替换 _april 后缀的SKU/菜品视图为参数化函数
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-- 依赖: fn_dish_sales(p_month) 已在 Phase 2 中创建
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-- ============================================================
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-- 1. fn_dish_sku_summary(p_month) — 替换 dish_sku_summary_april
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CREATE OR REPLACE FUNCTION analytics.fn_dish_sku_summary(p_month date)
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RETURNS TABLE (
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dish_name text,
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category_level1 text,
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category_level2 text,
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detail_rows bigint,
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bill_count bigint,
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store_count bigint,
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sales_quantity numeric,
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gross_amount numeric,
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received_amount numeric,
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discount_amount numeric,
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discount_rate_pct numeric,
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realized_unit_price numeric,
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revenue_share_pct numeric
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) AS $$
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SELECT
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s.dish_name,
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min(s.category_level1) AS category_level1,
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min(s.category_level2) AS category_level2,
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count(*) AS detail_rows,
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count(DISTINCT ROW(s.store_code, s.bill_no)) AS bill_count,
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count(DISTINCT s.store_code) AS store_count,
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sum(s.sales_quantity) AS sales_quantity,
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sum(s.gross_amount) AS gross_amount,
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sum(s.received_amount) AS received_amount,
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sum(s.dish_discount_amount) AS discount_amount,
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round(sum(s.dish_discount_amount) / NULLIF(sum(s.gross_amount), 0) * 100, 2) AS discount_rate_pct,
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round(sum(s.received_amount) / NULLIF(sum(s.sales_quantity), 0), 2) AS realized_unit_price,
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round(sum(s.received_amount) / NULLIF(sum(sum(s.received_amount)) OVER (), 0) * 100, 4) AS revenue_share_pct
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FROM analytics.fn_dish_sales(p_month) s
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WHERE s.dish_name IS NOT NULL
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GROUP BY s.dish_name
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$$ LANGUAGE SQL STABLE;
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-- 2. fn_dish_sku_abc(p_month) — 替换 v_dish_sku_abc_april
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CREATE OR REPLACE FUNCTION analytics.fn_dish_sku_abc(p_month date)
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RETURNS TABLE (
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dish_name text,
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category_level1 text,
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category_level2 text,
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detail_rows bigint,
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bill_count bigint,
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store_count bigint,
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sales_quantity numeric,
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gross_amount numeric,
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received_amount numeric,
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discount_amount numeric,
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discount_rate_pct numeric,
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realized_unit_price numeric,
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revenue_share_pct numeric,
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cumulative_revenue_share numeric,
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median_quantity numeric,
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median_revenue numeric,
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abc_class text,
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sales_quadrant text
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) AS $$
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WITH medians AS (
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SELECT
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percentile_cont(0.5) WITHIN GROUP (ORDER BY s.sales_quantity::double precision) AS median_quantity,
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percentile_cont(0.5) WITHIN GROUP (ORDER BY s.received_amount::double precision) AS median_revenue
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FROM analytics.fn_dish_sku_summary(p_month) s
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), ranked AS (
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SELECT
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s.dish_name,
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s.category_level1,
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s.category_level2,
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s.detail_rows,
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s.bill_count,
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s.store_count,
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s.sales_quantity,
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s.gross_amount,
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s.received_amount,
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s.discount_amount,
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s.discount_rate_pct,
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s.realized_unit_price,
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s.revenue_share_pct,
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sum(s.received_amount) OVER (ORDER BY s.received_amount DESC, s.dish_name ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
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/ NULLIF(sum(s.received_amount) OVER (), 0) AS cumulative_revenue_share,
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m.median_quantity,
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m.median_revenue
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FROM analytics.fn_dish_sku_summary(p_month) s
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CROSS JOIN medians m
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)
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SELECT
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ranked.dish_name,
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ranked.category_level1,
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ranked.category_level2,
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ranked.detail_rows,
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ranked.bill_count,
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ranked.store_count,
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ranked.sales_quantity,
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ranked.gross_amount,
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ranked.received_amount,
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ranked.discount_amount,
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ranked.discount_rate_pct,
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ranked.realized_unit_price,
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ranked.revenue_share_pct,
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ranked.cumulative_revenue_share,
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ranked.median_quantity,
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ranked.median_revenue,
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CASE
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WHEN ranked.cumulative_revenue_share <= 0.70 THEN 'A-核心'
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WHEN ranked.cumulative_revenue_share <= 0.90 THEN 'B-成长'
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ELSE 'C-长尾'
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END AS abc_class,
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CASE
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WHEN ranked.sales_quantity::double precision >= ranked.median_quantity AND ranked.received_amount::double precision >= ranked.median_revenue THEN '明星菜品'
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WHEN ranked.sales_quantity::double precision >= ranked.median_quantity AND ranked.received_amount::double precision < ranked.median_revenue THEN '引流菜品'
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WHEN ranked.sales_quantity::double precision < ranked.median_quantity AND ranked.received_amount::double precision >= ranked.median_revenue THEN '潜力菜品'
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ELSE '淘汰观察品'
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END AS sales_quadrant
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FROM ranked
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$$ LANGUAGE SQL STABLE;
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-- 3. fn_dish_category_summary(p_month) — 替换 dish_category_summary_april
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CREATE OR REPLACE FUNCTION analytics.fn_dish_category_summary(p_month date)
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RETURNS TABLE (
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category_level1 text,
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category_level2 text,
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sku_count bigint,
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bill_count bigint,
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store_count bigint,
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sales_quantity numeric,
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gross_amount numeric,
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received_amount numeric,
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discount_amount numeric,
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discount_rate_pct numeric,
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revenue_share_pct numeric
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) AS $$
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SELECT
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s.category_level1,
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s.category_level2,
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count(DISTINCT s.dish_name) AS sku_count,
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count(DISTINCT ROW(s.store_code, s.bill_no)) AS bill_count,
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count(DISTINCT s.store_code) AS store_count,
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sum(s.sales_quantity) AS sales_quantity,
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sum(s.gross_amount) AS gross_amount,
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sum(s.received_amount) AS received_amount,
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sum(s.dish_discount_amount) AS discount_amount,
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round(sum(s.dish_discount_amount) / NULLIF(sum(s.gross_amount), 0) * 100, 2) AS discount_rate_pct,
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round(sum(s.received_amount) / NULLIF(sum(sum(s.received_amount)) OVER (), 0) * 100, 2) AS revenue_share_pct
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FROM analytics.fn_dish_sales(p_month) s
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GROUP BY s.category_level1, s.category_level2
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$$ LANGUAGE SQL STABLE;
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-- 4. fn_dish_member_sku(p_month) — 替换 dish_member_sku_april
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CREATE OR REPLACE FUNCTION analytics.fn_dish_member_sku(p_month date)
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RETURNS TABLE (
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member_id text,
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dish_name text,
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order_count bigint,
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purchase_days bigint,
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store_count bigint,
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sales_quantity numeric,
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received_amount numeric
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) AS $$
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SELECT
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s.member_id,
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s.dish_name,
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count(DISTINCT ROW(s.store_code, s.bill_no)) AS order_count,
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count(DISTINCT s.business_date) AS purchase_days,
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count(DISTINCT s.store_code) AS store_count,
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sum(s.sales_quantity) AS sales_quantity,
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sum(s.received_amount) AS received_amount
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FROM analytics.fn_dish_sales(p_month) s
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WHERE s.member_id IS NOT NULL AND s.dish_name IS NOT NULL AND s.sales_quantity > 0
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GROUP BY s.member_id, s.dish_name
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$$ LANGUAGE SQL STABLE;
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-- 5. fn_dish_store_sku(p_month) — 替换 dish_store_sku_april
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CREATE OR REPLACE FUNCTION analytics.fn_dish_store_sku(p_month date)
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RETURNS TABLE (
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store_code text,
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store_name text,
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dish_name text,
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category_level1 text,
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category_level2 text,
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bill_count bigint,
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sales_quantity numeric,
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gross_amount numeric,
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received_amount numeric,
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discount_amount numeric,
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discount_rate_pct numeric
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) AS $$
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SELECT
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s.store_code,
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min(s.store_name) AS store_name,
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s.dish_name,
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min(s.category_level1) AS category_level1,
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min(s.category_level2) AS category_level2,
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count(DISTINCT s.bill_no) AS bill_count,
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sum(s.sales_quantity) AS sales_quantity,
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sum(s.gross_amount) AS gross_amount,
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sum(s.received_amount) AS received_amount,
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sum(s.dish_discount_amount) AS discount_amount,
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round(sum(s.dish_discount_amount) / NULLIF(sum(s.gross_amount), 0) * 100, 2) AS discount_rate_pct
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FROM analytics.fn_dish_sales(p_month) s
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WHERE s.dish_name IS NOT NULL
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GROUP BY s.store_code, s.dish_name
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$$ LANGUAGE SQL STABLE;
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-- 6. fn_dish_pair_summary(p_month) — 替换 dish_pair_summary_april (普通表)
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-- 注意: 此表由外部脚本填充，函数版本从 dish_sales_details 实时计算
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CREATE OR REPLACE FUNCTION analytics.fn_dish_pair_summary(p_month date)
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RETURNS TABLE (
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dish_a text,
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dish_b text,
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pair_count bigint
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) AS $$
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WITH pairs AS (
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SELECT
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LEAST(a.dish_name, b.dish_name) AS dish_a,
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GREATEST(a.dish_name, b.dish_name) AS dish_b,
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count(DISTINCT a.bill_no) AS pair_count
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FROM dish_sales_details a
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JOIN dish_sales_details b
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ON a.store_code = b.store_code
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AND a.bill_no = b.bill_no
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AND a.dish_name < b.dish_name
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WHERE a.opened_at >= p_month
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AND a.opened_at < p_month + INTERVAL '1 month'
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AND b.opened_at >= p_month
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AND b.opened_at < p_month + INTERVAL '1 month'
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AND a.dish_name IS NOT NULL
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AND b.dish_name IS NOT NULL
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GROUP BY LEAST(a.dish_name, b.dish_name), GREATEST(a.dish_name, b.dish_name)
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)
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SELECT dish_a, dish_b, pair_count
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FROM pairs
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ORDER BY pair_count DESC
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$$ LANGUAGE SQL STABLE;
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-- 7. fn_c_sku_governance(p_month) — 替换 v_c_sku_governance_april
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CREATE OR REPLACE FUNCTION analytics.fn_c_sku_governance(p_month date)
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RETURNS TABLE (
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dish_name text,
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category_level1 text,
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category_level2 text,
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detail_rows bigint,
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bill_count bigint,
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store_count bigint,
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sales_quantity numeric,
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gross_amount numeric,
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received_amount numeric,
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discount_amount numeric,
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discount_rate_pct numeric,
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realized_unit_price numeric,
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revenue_share_pct numeric,
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cumulative_revenue_share numeric,
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median_quantity numeric,
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median_revenue numeric,
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abc_class text,
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sales_quadrant text,
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item_types text,
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is_combo_header boolean,
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is_combo_component boolean,
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is_single_item boolean,
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dish_code text,
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cost_source_group_count bigint,
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cost_report_sales_amount numeric,
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theoretical_cost numeric,
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actual_cost numeric,
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cost_variance_amount numeric,
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cost_report_theoretical_cost_rate_pct numeric,
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cost_report_actual_cost_rate_pct numeric,
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material_count bigint,
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exclusive_material_count bigint,
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governance_group text,
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additional_risk text
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) AS $$
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WITH item_types AS (
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SELECT
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d.dish_name,
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string_agg(DISTINCT COALESCE(d.item_type, '未分类'), '、' ORDER BY (COALESCE(d.item_type, '未分类'))) AS item_types,
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bool_or(d.item_type = '套餐') AS is_combo_header,
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bool_or(d.item_type = '套餐明细菜') AS is_combo_component,
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bool_or(d.item_type = '单点') AS is_single_item
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FROM dish_sales_details d
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WHERE d.opened_at >= p_month AND d.opened_at < p_month + INTERVAL '1 month'
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GROUP BY d.dish_name
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), material_usage AS (
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SELECT
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m.material_name,
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count(DISTINCT m.dish_name) AS used_by_dish_count
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FROM analytics.v_dish_cost_analysis_latest_material_detail m
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GROUP BY m.material_name
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), material_profile AS (
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SELECT
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d.dish_name,
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count(DISTINCT d.material_name) AS material_count,
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count(DISTINCT d.material_name) FILTER (WHERE u.used_by_dish_count = 1) AS exclusive_material_count
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FROM analytics.v_dish_cost_analysis_latest_material_detail d
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JOIN material_usage u USING (material_name)
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GROUP BY d.dish_name
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)
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SELECT
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c.dish_name,
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c.category_level1,
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c.category_level2,
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c.detail_rows,
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c.bill_count,
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c.store_count,
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c.sales_quantity,
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c.gross_amount,
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c.received_amount,
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c.discount_amount,
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c.discount_rate_pct,
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c.realized_unit_price,
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c.revenue_share_pct,
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c.cumulative_revenue_share,
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c.median_quantity,
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c.median_revenue,
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c.abc_class,
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c.sales_quadrant,
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t.item_types,
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t.is_combo_header,
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t.is_combo_component,
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t.is_single_item,
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cost.dish_code,
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cost.source_group_count AS cost_source_group_count,
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cost.sales_amount AS cost_report_sales_amount,
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cost.theoretical_cost,
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cost.actual_cost,
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cost.cost_variance_amount,
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cost.theoretical_cost_rate_pct AS cost_report_theoretical_cost_rate_pct,
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cost.actual_cost_rate_pct AS cost_report_actual_cost_rate_pct,
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COALESCE(mp.material_count, 0) AS material_count,
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COALESCE(mp.exclusive_material_count, 0) AS exclusive_material_count,
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CASE
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WHEN c.received_amount <= 0 AND COALESCE(t.is_combo_header, false) THEN 'T1-套餐/技术项目治理'
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WHEN c.received_amount <= 0 THEN 'T2-零收入单点核查'
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WHEN c.store_count = 1 AND c.bill_count < 30 AND c.received_amount < 1000 THEN 'S1-首批停用评审'
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WHEN c.store_count <= 3 AND c.bill_count < 60 AND c.received_amount < 3000 THEN 'S2-区域低效评审'
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WHEN c.store_count > 3 AND c.bill_count < 30 AND c.received_amount < 1000 THEN 'S3-铺店不动销评审'
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WHEN c.sales_quadrant IN ('明星菜品', '潜力菜品') THEN 'K1-保留并优化'
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ELSE 'K2-继续观察'
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END AS governance_group,
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CASE
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WHEN COALESCE(mp.exclusive_material_count, 0) > 0 AND c.received_amount < 3000 THEN '高：低收入且占用独有原料'
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WHEN cost.cost_variance_amount > 0 AND cost.actual_cost > (cost.theoretical_cost * 1.2) THEN '高：成本报表显示明显超理论'
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WHEN c.discount_rate_pct >= 35 THEN '中：高折扣依赖'
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ELSE '常规'
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END AS additional_risk
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FROM analytics.fn_dish_sku_abc(p_month) c
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LEFT JOIN item_types t USING (dish_name)
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LEFT JOIN analytics.v_dish_cost_analysis_latest_dish_rollup cost USING (dish_name)
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LEFT JOIN material_profile mp USING (dish_name)
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WHERE c.abc_class = 'C-长尾'
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$$ LANGUAGE SQL STABLE;
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