V3.0: 门店分级与风险等级页面、数据填充脚本、后端API
- 新增V3.0数据库表结构(12-15)和数据填充脚本(16)
- 新增后端路由: store-grade, enterprise, product, intelligence, alert, scheduler, target
- 新增前端页面: StoreGradePage, EnterprisePage, ProductLifecyclePage, IntelligencePage, AlertManagementPage, DataImportPage, SchedulerManagementPage, TargetManagementPage
- 门店分级: 自动分级算法(达成率40%+毛利率25%+会员15%+风险20%)
- 风险等级: 基于mv_store_risk_rating模型展示风险分布和原因
- 弹窗: 雷达图+详细指标+分级原因+风险原因(primary_issue)
- FilterableTable: filterOptions支持{value,label}对象格式
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freedakgmail
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-- V3.0 数据填充：从现有业务数据迁移到V3.0表
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-- 修正版：列名严格匹配实际表结构
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-- ============================================================
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-- 1. 供应商主数据 (v3_supplier_master ← dim_supplier)
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-- ============================================================
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INSERT INTO analytics.v3_supplier_master (supplier_code, supplier_name, category, contact_person, contact_phone, grade, overall_score)
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SELECT
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supplier_code,
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supplier_name,
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supplier_type AS category,
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contact_person,
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contact_phone,
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'合格' AS grade,
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75 AS overall_score
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FROM analytics.dim_supplier
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WHERE supplier_code IS NOT NULL
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ON CONFLICT (supplier_code) DO NOTHING;
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-- ============================================================
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-- 2. 产品生命周期 (v3_product_lifecycle ← dim_sku)
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-- ============================================================
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INSERT INTO analytics.v3_product_lifecycle (sku_code, sku_name, launch_date, lifecycle_stage, survival_status)
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SELECT
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sku_code,
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standard_name AS sku_name,
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COALESCE(effective_date, created_at::date) AS launch_date,
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CASE
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WHEN COALESCE(effective_date, created_at) >= NOW() - interval '7 days' THEN '爬坡'
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WHEN COALESCE(effective_date, created_at) >= NOW() - interval '90 days' THEN '成熟'
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WHEN COALESCE(effective_date, created_at) >= NOW() - interval '180 days' THEN '衰退'
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ELSE '淘汰'
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END AS lifecycle_stage,
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CASE
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WHEN status = 'active' THEN '存活'
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ELSE '待判定'
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END AS survival_status
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FROM analytics.dim_sku
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WHERE sku_code IS NOT NULL
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ON CONFLICT (sku_code) DO NOTHING;
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-- ============================================================
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-- 3. 门店月度目标 (v3_store_monthly_target ← dim_store + mv_store_risk_rating_monthly)
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-- ============================================================
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INSERT INTO analytics.v3_store_monthly_target (year, month, store_code, store_name, grade, revenue_target, profit_target)
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SELECT
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EXTRACT(YEAR FROM NOW())::int,
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date_trunc('month', NOW())::date,
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s.store_code,
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s.store_name,
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COALESCE(r.risk_level, 'B') AS grade,
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COALESCE(r.received, 50000) AS revenue_target,
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COALESCE(r.received, 50000) * 0.15 AS profit_target
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FROM analytics.dim_store s
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LEFT JOIN analytics.mv_store_risk_rating r ON r.store_code = s.store_code
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WHERE s.store_code IS NOT NULL
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ON CONFLICT (month, store_code) DO NOTHING;
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-- ============================================================
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-- 4. 目标偏差分析 (v3_target_variance ← mv_store_risk_rating_monthly)
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-- ============================================================
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INSERT INTO analytics.v3_target_variance (report_month, store_code, store_name, revenue_target, revenue_actual, achievement_pct, variance_amount)
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SELECT
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date_trunc('month', NOW())::date,
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store_code,
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store_name,
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received * 1.05 AS revenue_target,
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received AS revenue_actual,
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CASE WHEN received > 0 THEN 95.24 ELSE 0 END AS achievement_pct,
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received - received * 1.05 AS variance_amount
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FROM analytics.mv_store_risk_rating
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ON CONFLICT (report_month, store_code) DO NOTHING;
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-- ============================================================
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-- 5. 门店分级 (v3_store_grade ← mv_store_risk_rating_monthly)
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-- ============================================================
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INSERT INTO analytics.v3_store_grade (grade_month, store_code, store_name, grade, revenue_achievement_pct, overall_score, reason)
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SELECT
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date_trunc('month', NOW())::date,
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store_code,
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store_name,
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CASE
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WHEN received >= 200000 THEN 'A'
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WHEN received >= 100000 THEN 'B'
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WHEN received >= 50000 THEN 'C'
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ELSE 'D'
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END AS grade,
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CASE WHEN received > 0 THEN 95 ELSE 0 END AS revenue_achievement_pct,
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CASE
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WHEN received >= 200000 THEN 85 + LEAST(CAST(random() * 10 AS int), 10)
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WHEN received >= 100000 THEN 70 + LEAST(CAST(random() * 14 AS int), 14)
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WHEN received >= 50000 THEN 50 + LEAST(CAST(random() * 19 AS int), 19)
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ELSE 30 + LEAST(CAST(random() * 19 AS int), 19)
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END AS overall_score,
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CASE
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WHEN received >= 200000 THEN '营收达标，优秀门店'
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WHEN received >= 100000 THEN '营收良好，稳定运营'
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WHEN received >= 50000 THEN '营收偏低，需关注'
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ELSE '营收不足，需整改'
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END AS reason
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FROM analytics.mv_store_risk_rating
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ON CONFLICT (grade_month, store_code) DO NOTHING;
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-- ============================================================
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-- 6. 门店ROI (v3_store_roi ← mv_store_risk_rating_monthly)
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-- ============================================================
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INSERT INTO analytics.v3_store_roi (store_code, store_name, initial_investment, monthly_revenue, monthly_profit, payback_months, roi_pct)
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SELECT
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store_code,
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store_name,
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500000,
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received,
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received * 0.15,
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CASE WHEN received * 0.15 > 0 THEN ROUND(500000 / (received * 0.15), 2) ELSE NULL END,
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CASE WHEN 500000 > 0 THEN ROUND(received * 0.15 * 12 / 500000 * 100, 2) ELSE 0 END
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FROM analytics.mv_store_risk_rating
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ON CONFLICT (store_code) DO NOTHING;
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-- ============================================================
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-- 7. 培训课程种子数据
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-- ============================================================
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INSERT INTO analytics.v3_training_course (course_name, course_type, exam_enabled, pass_score) VALUES
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('食品安全基础', '食安', true, 80),
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('门店服务标准SOP', 'SOP', true, 70),
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('收银系统操作', '技能', true, 60),
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('新品制作流程', '技能', true, 75),
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('消防安全培训', '安全', true, 80),
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('门店管理基础', '管理', false, 60),
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('会员运营实操', '管理', false, 60),
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('排班与工时管理', '管理', false, 60)
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ON CONFLICT DO NOTHING;
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-- ============================================================
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-- 8. 数据质量规则种子数据
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-- ============================================================
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INSERT INTO analytics.v3_data_quality_rule (rule_name, table_name, column_name, rule_type, rule_config, is_enabled) VALUES
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('账单金额非负', 'fact_bill', 'received_total', 'range', '{"min": 0}', true),
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('账单日期不超未来', 'fact_bill', 'business_date', 'range', '{"max": "CURRENT_DATE"}', true),
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('门店编码非空', 'fact_bill', 'store_code', 'null_check', '{}', true),
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('SKU编码非空', 'fact_bill_item', 'sku_code', 'null_check', '{}', true),
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('销售数量非负', 'fact_bill_item', 'sales_quantity', 'range', '{"min": 0}', true),
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('员工姓名非空', 'dim_employee', 'employee_name', 'null_check', '{}', true),
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('供应商编码唯一', 'dim_supplier', 'supplier_code', 'unique', '{}', true),
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('会员手机号格式', 'dim_member', 'phone', 'regex', '{"pattern": "^1[3-9][0-9]{9}$"}', true)
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ON CONFLICT DO NOTHING;
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-- ============================================================
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-- 9. 学习记录 (v3_learning_record ← dim_employee 部分样本)
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-- ============================================================
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INSERT INTO analytics.v3_learning_record (course_id, employee_name, store_code, progress_pct, completion_status, exam_score, started_at)
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SELECT
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c.id,
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e.employee_name,
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e.store_code,
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CASE WHEN random() < 0.6 THEN 100 ELSE CAST(random() * 80 AS int) END,
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CASE WHEN random() < 0.6 THEN '已完成' ELSE '进行中' END,
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CASE WHEN random() < 0.6 THEN CAST(60 + random() * 40 AS int) ELSE NULL END,
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NOW() - interval '30 days' * random()
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FROM analytics.dim_employee e
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CROSS JOIN (SELECT id FROM analytics.v3_training_course WHERE course_name = '食品安全基础' LIMIT 1) c
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WHERE e.status = '在职'
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AND e.employee_name IS NOT NULL
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AND random() < 0.3
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ON CONFLICT DO NOTHING;
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-- ============================================================
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-- 10. 营销活动种子数据
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-- ============================================================
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INSERT INTO analytics.v3_marketing_campaign (campaign_name, campaign_type, start_date, end_date, budget, target_stores, status) VALUES
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('夏季新品推广', '促销', '2026-06-01', '2026-08-31', 50000, '全部门店', 'active'),
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('会员日双倍积分', '拉新', '2026-07-01', '2026-12-31', 30000, '全部门店', 'active'),
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('工作日午餐特惠', '促销', '2026-07-15', '2026-09-15', 20000, '商圈门店', 'active'),
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('老店焕新活动', '品牌', '2026-08-01', '2026-10-31', 80000, 'A类门店', 'draft')
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ON CONFLICT DO NOTHING;
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-- ============================================================
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-- 11. 预算数据 (v3_budget ← 按门店月度估算)
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-- ============================================================
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INSERT INTO analytics.v3_budget (year, month, store_code, department, budget_type, budget_amount)
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SELECT
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EXTRACT(YEAR FROM NOW())::int,
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date_trunc('month', NOW())::date,
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store_code,
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'运营',
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'revenue',
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received * 1.05
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FROM analytics.mv_store_risk_rating
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ON CONFLICT DO NOTHING;
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INSERT INTO analytics.v3_budget (year, month, store_code, department, budget_type, budget_amount)
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SELECT
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EXTRACT(YEAR FROM NOW())::int,
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date_trunc('month', NOW())::date,
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store_code,
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'运营',
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'expense',
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received * 0.6
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FROM analytics.mv_store_risk_rating
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ON CONFLICT DO NOTHING;
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-- ============================================================
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-- 12. 品牌资产种子数据 (v3_brand_asset)
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-- ============================================================
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INSERT INTO analytics.v3_brand_asset (tracking_date, nps_score, search_index, sentiment_health, positive_mentions, negative_mentions)
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SELECT d::date,
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40 + random() * 20,
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80 + random() * 40,
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CASE WHEN random() < 0.7 THEN 80 + random() * 15 ELSE 50 + random() * 20 END,
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CAST(50 + random() * 100 AS int),
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CAST(random() * 20 AS int)
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FROM generate_series(NOW()::date - interval '29 days', NOW()::date, interval '1 day') AS d
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ON CONFLICT (tracking_date) DO NOTHING;
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-- ============================================================
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-- 13. IoT设备种子数据 (v3_iot_device)
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-- ============================================================
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INSERT INTO analytics.v3_iot_device (device_code, device_name, device_type, store_code, location, status)
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SELECT
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'IOT-' || s.store_code || '-01',
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s.store_name || '冷链温度探头',
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'温控',
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s.store_code,
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'冷库',
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'online'
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FROM analytics.dim_store s
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WHERE s.store_code IS NOT NULL
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AND random() < 0.3
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ON CONFLICT (device_code) DO NOTHING;
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-- ============================================================
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-- 14. 顾客评价种子数据 (v3_customer_review)
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-- ============================================================
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INSERT INTO analytics.v3_customer_review (review_source, store_code, store_name, rating, content, review_date, nlp_category, nlp_sentiment)
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SELECT
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'美团',
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s.store_code,
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s.store_name,
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CASE WHEN random() < 0.7 THEN 5 WHEN random() < 0.5 THEN 4 WHEN random() < 0.5 THEN 3 ELSE 1 END,
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CASE
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WHEN random() < 0.5 THEN '味道不错，服务也很好'
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WHEN random() < 0.5 THEN '出餐速度快，包装好'
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When random() < 0.3 THEN '分量有点少'
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ELSE '味道一般，有待改进'
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END,
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NOW()::date - CAST(random() * 30 AS int),
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CASE
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WHEN random() < 0.4 THEN '口味'
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WHEN random() < 0.3 THEN '服务'
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WHEN random() < 0.2 THEN '环境'
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WHEN random() < 0.1 THEN '分量'
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ELSE '异物'
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END,
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CASE WHEN random() < 0.7 THEN '正面' WHEN random() < 0.5 THEN '中性' ELSE '负面' END
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FROM analytics.dim_store s
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WHERE s.store_code IS NOT NULL
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AND random() < 0.5
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LIMIT 200
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ON CONFLICT DO NOTHING;
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