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文档: - 01-项目概述与架构: 技术架构、数据流、部署拓扑 - 02-数据接入与治理: 原始数据导入、物化视图、门店名映射 - 03-指标体系与API开发: 指标分层、SQL模式、常见陷阱 - 04-前端页面开发: 组件规范、页面模板、月份参数管理 - 05-部署与运维: 部署脚本、FRP隧道、PM2、备份 - 06-调试排查手册: 问题分类、8个实际案例、工具速查 - 07-通用方法论: 核心原则、实施阶段、快速复制Checklist 修复: - situational-awareness.ts: salary_month→salary_period, 日期格式改中文 - 客流-人力匹配: 改用attendance_records打卡数据解析在岗人数 - 客流数据除以30天对齐日均
187 lines
5.3 KiB
Markdown
187 lines
5.3 KiB
Markdown
# 03 · 指标体系与API开发
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## 1. 指标分层设计
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```
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L0: 原始数据层 (bill_records, salary_detail_records, attendance_records)
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↓
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L1: 预聚合层 (物化视图 mv_*)
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↓
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L2: API层 (后端路由，SQL查询 + 业务逻辑)
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↓
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L3: 展示层 (前端页面，图表 + 表格)
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```
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## 2. 后端API规范
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### 2.1 路由组织
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```
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server/src/routes/
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├── data.ts # 核心数据API（概览、门店、风险明细等）
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├── situational-awareness.ts # 态势感知（健康度、预警、关联分析）
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├── smart-scheduling.ts # 智能排班
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├── cost-analysis.ts # 成本分析
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├── store-expense.ts # 门店费用
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├── analytics-enhanced.ts # 增强分析
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├── admin.ts # 管理
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└── tts.ts # 语音
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```
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### 2.2 通用模式
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```typescript
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// 月份参数解析
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const month = parseMonth(req) // 从 query 参数获取，默认 '2026-04'
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// 分页
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const { page, pageSize, offset } = parsePagination(req)
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// 数据权限
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const scope = getDataScope(req) // 返回 store_names 数组或 null（全部）
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// 响应格式
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sendSuccess(res, data, meta?) // { success: true, data, meta }
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sendError(res, message) // { success: false, error: message }
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```
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### 2.3 SQL查询模式
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**安全参数化**：
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```typescript
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// 正确：参数化查询
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const result = await query(`SELECT * FROM mv_risk_anomaly WHERE month = to_char($1::date, 'YYYY-MM')`, [month])
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// 错误：字符串拼接
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const result = await query(`SELECT * FROM mv_risk_anomaly WHERE month = '${month}'`)
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```
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**动态条件构建**：
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```typescript
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const conditions: string[] = [`month = to_char($1::date, 'YYYY-MM')`]
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const params: any[] = [month]
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let paramIdx = 2
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if (storeName) {
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conditions.push(`store_name = $${paramIdx}`)
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params.push(storeName)
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paramIdx++
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}
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const whereClause = conditions.join(' AND ')
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```
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**聚合汇总+分页**：
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```typescript
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// 先查总数和汇总
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const countResult = await query(`SELECT count(*) AS total, sum(consumption) AS sum_consumption FROM mv_risk_anomaly WHERE ${whereClause}`, params)
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// 再查明细
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const result = await query(`SELECT * FROM mv_risk_anomaly WHERE ${whereClause} ORDER BY ${sortCol} ${order} LIMIT $${paramIdx} OFFSET $${paramIdx + 1}`, [...params, pageSize, offset])
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```
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## 3. 常见SQL陷阱与修复
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### 3.1 Schema前缀错误
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**症状**：`relation "analytics.mv_channel_daily" does not exist`
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**排查**：
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```sql
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SELECT schemaname, matviewname FROM pg_matviews WHERE matviewname LIKE '%channel%';
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```
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**修复**：确认实际schema，移除或修正前缀
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```typescript
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// 错误：FROM analytics.mv_channel_daily
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// 正确：FROM mv_channel_daily (实际在public schema)
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```
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### 3.2 列名不存在
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**症状**：`column "risk_score" does not exist`
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**排查**：
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```sql
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SELECT column_name FROM information_schema.columns WHERE table_name = 'mv_store_risk_rating_monthly';
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```
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**修复策略**：
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- 策略A：改用 `SELECT *`（当不需要特定列名时）
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- 策略B：用 `0 AS column_name` 替代不存在的列（当前端期望该字段时）
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- 策略C：修正为正确的列名
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### 3.3 日期格式不匹配
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**症状**：薪资相关查询返回0条
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**排查**：
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```sql
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SELECT DISTINCT salary_period FROM salary_detail_records ORDER BY 1 DESC LIMIT 5;
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-- 结果：2026年4月（非2026-04）
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```
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**修复**：
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```typescript
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// 错误：s.salary_month = to_char($1::date, 'YYYY-MM')
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// 正确：s.salary_period = to_char($1::date, 'YYYY"年"FMMM"月"')
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```
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### 3.4 物化视图stale
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**症状**：API返回数据与直接查原始表不一致
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**排查**：对比物化视图和原始表的count
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```sql
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SELECT count(*) FROM mv_risk_anomaly WHERE month = '2026-04';
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SELECT count(*) FROM bill_records WHERE to_char(c176::timestamp, 'YYYY-MM') = '2026-04' AND ...;
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```
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**修复**：重建物化视图
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```sql
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DROP MATERIALIZED VIEW mv_risk_anomaly;
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CREATE MATERIALIZED VIEW mv_risk_anomaly AS ...;
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CREATE INDEX idx_mv_risk_anomaly_month ON mv_risk_anomaly(month);
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```
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## 4. 考勤打卡数据解析
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### 4.1 department路径解析门店名
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```
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北京西部马华餐饮有限公司/西部马华品牌门店/胡庆鹏区/HQP2区/双安店/前厅/服务组
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```
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**提取逻辑**（从后往前找以"店"结尾的层级）：
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```typescript
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function extractStore(dept: string): string {
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const parts = dept.split('/')
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for (let i = parts.length - 1; i >= 0; i--) {
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if (parts[i].endsWith('店')) return parts[i]
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}
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return ''
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}
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```
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### 4.2 打卡时间解析
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格式：`08:43(考勤机：指纹)\n21:23(考勤机：指纹)`
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```typescript
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function parseClockTimes(raw: string): { start: number; end: number } | null {
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const times = raw.match(/(\d{1,2}):(\d{2})/g)
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if (!times || times.length < 2) return null
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const startHour = parseInt(times[0].match(/(\d{1,2}):/)[1])
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let endHour = parseInt(times[times.length - 1].match(/(\d{1,2}):/)[1])
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if (endHour < startHour) endHour = 23 // 跨天
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return { start: startHour, end: endHour }
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}
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```
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### 4.3 日均在岗人数估算
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```typescript
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// 每个员工每天打卡算1人在岗，按小时累计
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// 月度汇总后除以30天得到日均同时在岗人数
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staffSummary[store][hour] = Math.round(totalPersonHours[store][hour] / 30)
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```
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**关键点**：客流数据 `mv_bill_hourly.bills` 也是月度汇总，展示时需除以30天对齐。
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