youruo
c358aff781
数据补充（全部来自数据库，无人工填数）： - 流水列接入 4/12：采购进仓（jst_purchase×Item，po_date，Confirmed/Finished）、 采购退货（jst_purchaseout×Item，io_date）、销售出仓（jst_Orders×OrderItem，send_date， 与周转率发货口径一致）、销售退货（jst_returnAndRefund×Item，as_date，实收 r_qty）。 其余 8 列业务库确无单据表（已核实全库表清单），保持「—」待 DBA 建表 - 期初快照缺失时自动兜底：优先用 rpt.pm_stockMonitor（报表库 01:00 跑批 T-1 库存， 按 dbWarehouse 聚合 jsStock，精确）；监控表没有的虚拟仓（运营专用/京东仓）用 实时库存扣自期初时刻以来的带符号流水回推（近似），推导结果补写快照逐日滚动 缺陷修复： - 合计行仓库名显示 NaN：isTotal 渲染分支拦截了所有列，'合计' 被强转数字； 现仓库名原样显示，无数据来源的流水列合计也显示「—」而非误导性 0 - 合计行：某列全部仓库均无数据时合计置空，避免 0 误读 新增 4 项单元测试（流水符号约定、参数展开、期初双路径推导）。
364 lines
15 KiB
JavaScript
364 lines
15 KiB
JavaScript
const express = require('express');
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const cloudDb = require('../config/cloudDatabase');
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const snapshotDb = require('../config/dailyInventorySnapshotDb');
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const {
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DAILY_INVENTORY_BASELINE,
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FLOW_COLUMNS,
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} = require('../config/dailyInventoryBaseline');
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const { fetchFlows, AVAILABLE_FLOW_FIELDS } = require('../services/dailyInventoryFlows');
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const { deriveAndStoreOpeningSnapshot } = require('../services/dailyInventoryOpening');
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const router = express.Router();
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/** 库存衡量基准时刻：快照日次日 01:00（报表库每日凌晨 1 点跑批，标注 T-1） */
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const AS_OF_TIME = '01:00';
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/**
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* 「月度」发货窗口天数：滚动 30 天。
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* 库龄阈值：3 个月 = 90 天、12 个月 = 365 天（按日历天折算）。
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*/
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const SHIP_WINDOW_DAYS = 30;
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const AGE_3M_DAYS = 90;
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const AGE_12M_DAYS = 365;
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/** 结果缓存：单次查询要扫订单明细 + 库存明细两张大表，避免连续刷新重复打库 */
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const RESULT_CACHE_TTL_MS = 5 * 60 * 1000;
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let cachedResult = null;
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let cachedResultAt = 0;
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const toNumber = (v) => {
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if (v == null) return null;
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const n = Number(v);
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return Number.isFinite(n) ? n : null;
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};
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/** 快照日 + 次日 01:00 作为统一的库龄/分级基准 */
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function nextDayAsOf(bizDate) {
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if (!bizDate || !/^\d{4}-\d{2}-\d{2}$/.test(bizDate)) return null;
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const d = new Date(`${bizDate}T00:00:00Z`);
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d.setUTCDate(d.getUTCDate() + 1);
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return `${d.toISOString().slice(0, 10)} ${AS_OF_TIME}`;
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}
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/** 发货窗口：[asOf - 30 天, asOf)，滚动一个月 */
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function shipWindow(asOf) {
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const to = new Date(`${asOf.slice(0, 10)}T${AS_OF_TIME}:00Z`);
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const from = new Date(to.getTime() - SHIP_WINDOW_DAYS * 86400000);
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const fmt = (dt) => dt.toISOString().slice(0, 19).replace('T', ' ');
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return { shipFrom: fmt(from), shipTo: fmt(to) };
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}
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async function fetchLatestBizDate() {
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const rows = await cloudDb.query(
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`SELECT MAX(CONVERT(VARCHAR(10), date, 23)) AS d FROM rpt.pm_stockMonitor`
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);
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return rows[0]?.d || null;
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}
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/** 上一自然日（日历天，用于取「期初 = 昨日结存」） */
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function prevDay(bizDate) {
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if (!bizDate || !/^\d{4}-\d{2}-\d{2}$/.test(bizDate)) return null;
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const d = new Date(`${bizDate}T00:00:00Z`);
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d.setUTCDate(d.getUTCDate() - 1);
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return d.toISOString().slice(0, 10);
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}
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/** mssql 不支持数组参数，按位展开成 @p0,@p1... */
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function buildInClause(values, prefix) {
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const params = {};
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const clause = values
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.map((v, i) => {
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const key = `${prefix}${i}`;
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params[key] = v;
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return `@${key}`;
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})
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.join(',');
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return { clause, params };
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}
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/**
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* 分级规则（库龄 + 周转率双维度），口径与业务给定一致：
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* 库龄 < 3 个月 且 周转率 30%~199% → 滞销预警
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* 库龄 < 3 个月 且 周转率 ≥ 200% → 商品慢流
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* 库龄 > 3 个月 且 周转率 30%~99% → 商品慢流
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* 库龄 3~12 个月 且 周转率 ≥ 100% → 商品呆滞
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* 库龄 > 12 个月 且 周转率 100%~199% → 商品呆滞
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* 库龄 > 12 个月 且 周转率 ≥ 200% → 商品僵尸
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* 边界：库龄正好 90 天不计入「慢流」（业务描述为 > 3 个月），正好 365 天仍属「3~12 个月」。
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*/
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function buildLevelExpr() {
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const t = 'b.turnoverPct';
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return `
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CASE
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WHEN b.stockQty <= 0 OR ${t} IS NULL OR b.ageDays IS NULL THEN NULL
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WHEN b.ageDays < ${AGE_3M_DAYS} AND ${t} >= 30 AND ${t} < 200 THEN 'warn'
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WHEN b.ageDays < ${AGE_3M_DAYS} AND ${t} >= 200 THEN 'slow'
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WHEN b.ageDays > ${AGE_3M_DAYS} AND ${t} >= 30 AND ${t} < 100 THEN 'slow'
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WHEN b.ageDays >= ${AGE_3M_DAYS} AND b.ageDays <= ${AGE_12M_DAYS} AND ${t} >= 100 THEN 'stale'
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WHEN b.ageDays > ${AGE_12M_DAYS} AND ${t} >= 100 AND ${t} < 200 THEN 'stale'
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WHEN b.ageDays > ${AGE_12M_DAYS} AND ${t} >= 200 THEN 'zombie'
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ELSE NULL
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END`;
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}
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/**
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* 每个仓库的周转率与四级库存量（一次查询覆盖全部仓库）。
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*
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* 口径：
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* 库存周转天数（周转率）= 月度发货总件数 / 月度日均库存件数 × 100%
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* · 月度发货总件数 = 该仓近 30 天已发货订单件数（jst_OrderItem × jst_Orders，按 send_date，排除 Delete/Cancelled）
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* · 月度日均库存件数 = 该仓聚水潭库存 jst_inventoryInfo（无历史结存表，以当日库存近似月度日均）
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* 库龄(天) = DATEDIFF(基准时刻, SKU 建档时间 created)
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*
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* SKU 关联链路：jst_inventoryInfo.sku_id = jst_OrderItem.sku_id = pm_stockMonitor.sku_id（实测崔桥 1852/1852 命中）
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*/
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async function fetchGradeMetrics(wmsCoIds, bizDate, asOf) {
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if (!wmsCoIds.length || !bizDate || !asOf) return new Map();
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const invIn = buildInClause(wmsCoIds, 'w');
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const shipIn = buildInClause(wmsCoIds, 'v');
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const { shipFrom, shipTo } = shipWindow(asOf);
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const level = buildLevelExpr();
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const sqlText = `
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WITH inv AS (
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SELECT i.wms_co_id AS wmsCoId,
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i.sku_id AS skuId,
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SUM(CAST(ISNULL(i.qty, 0) AS DECIMAL(18, 2))) AS stockQty
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FROM rpt.jst_inventoryInfo i
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WHERE i.wms_co_id IN (${invIn.clause})
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GROUP BY i.wms_co_id, i.sku_id
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),
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created AS (
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SELECT m.sku_id AS skuId, MIN(m.created) AS created
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FROM rpt.pm_stockMonitor m
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WHERE CONVERT(VARCHAR(10), m.date, 23) = @bizDate
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GROUP BY m.sku_id
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),
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ship AS (
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SELECT o.wms_co_id AS wmsCoId,
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oi.sku_id AS skuId,
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SUM(CAST(ISNULL(oi.qty, 0) AS DECIMAL(18, 2))) AS shipQty
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FROM rpt.jst_OrderItem oi
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JOIN rpt.jst_Orders o ON o.o_id = oi.o_id
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WHERE o.wms_co_id IN (${shipIn.clause})
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AND o.send_date >= @shipFrom
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AND o.send_date < @shipTo
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AND ISNULL(o.status, '') NOT IN ('Delete', 'Cancelled')
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GROUP BY o.wms_co_id, oi.sku_id
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),
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base AS (
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SELECT v.wmsCoId,
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v.skuId,
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v.stockQty,
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ISNULL(s.shipQty, 0) AS shipQty,
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DATEDIFF(DAY, c.created, @asOf) AS ageDays,
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CASE WHEN v.stockQty <= 0 THEN NULL
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ELSE ISNULL(s.shipQty, 0) * 100.0 / v.stockQty END AS turnoverPct
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FROM inv v
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LEFT JOIN ship s ON s.wmsCoId = v.wmsCoId AND s.skuId = v.skuId
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LEFT JOIN created c ON c.skuId = v.skuId
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)
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SELECT b.wmsCoId,
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SUM(CASE WHEN ABS(b.stockQty) > 0.0001 THEN 1 ELSE 0 END) AS skuCount,
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SUM(b.stockQty) AS totalStock,
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SUM(b.shipQty) AS totalShip,
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SUM(CASE WHEN b.ageDays IS NULL OR b.turnoverPct IS NULL OR b.stockQty <= 0 THEN 1 ELSE 0 END) AS ungradedSku,
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CASE WHEN SUM(b.stockQty) > 0
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THEN CAST(SUM(b.shipQty) * 100.0 / SUM(b.stockQty) AS DECIMAL(18, 2))
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ELSE NULL END AS turnoverPct,
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SUM(CASE WHEN ${level} = 'warn' THEN b.stockQty ELSE 0 END) AS warnLevel,
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SUM(CASE WHEN ${level} = 'slow' THEN b.stockQty ELSE 0 END) AS slowLevel,
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SUM(CASE WHEN ${level} = 'stale' THEN b.stockQty ELSE 0 END) AS staleLevel,
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SUM(CASE WHEN ${level} = 'zombie' THEN b.stockQty ELSE 0 END) AS zombieLevel
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FROM base b
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GROUP BY b.wmsCoId`;
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const rows = await cloudDb.query(sqlText, {
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...invIn.params,
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...shipIn.params,
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bizDate,
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asOf,
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shipFrom,
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shipTo,
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}, { requestTimeout: 120000 });
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const map = new Map();
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rows.forEach((r) => {
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map.set(String(r.wmsCoId), {
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skuCount: toNumber(r.skuCount) || 0,
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gradeStock: toNumber(r.totalStock) || 0,
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shipQty: toNumber(r.totalShip) || 0,
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turnoverDays: toNumber(r.turnoverPct),
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warnLevel: toNumber(r.warnLevel) || 0,
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slowLevel: toNumber(r.slowLevel) || 0,
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staleLevel: toNumber(r.staleLevel) || 0,
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zombieLevel: toNumber(r.zombieLevel) || 0,
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ungradedSku: toNumber(r.ungradedSku) || 0,
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});
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});
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return map;
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}
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/** 实时库存表的数据时效（一次取全局即可） */
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async function fetchInventoryTakenAt() {
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const rows = await cloudDb.query(
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`SELECT MAX(modified) AS takenAt FROM rpt.jst_inventoryInfo`
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);
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return rows[0]?.takenAt || null;
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}
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/**
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* GET /api/daily-inventory
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* 每日进销存：26 个仓库 ×（期初 / 期末 / 周转率 / 四级库存量）
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*
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* 数据源分工（全部来自数据库，无硬编码业务数值）：
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* - 期初数量：本地快照表 daily_inventory_snapshot 中「上一业务日」的期末结存；
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* 快照缺失时按 pm_stockMonitor（精确）/ 实时库存扣流水（近似）推导并补写
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* - 期末数量：jst_inventoryInfo 实时库存（按 wms_co_id 单仓）
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* - 库存周转天数 / 四级库存量：近 30 天发货 × 实时库存 × SKU 建档日期
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* - 12 个流水列：4 列可从业务库单据表还原（采购进仓/采购退货/销售出仓/销售退货，
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* 见 services/dailyInventoryFlows.js），其余 8 列业务库无单据表，返回 null（前端显示「—」）
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*/
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router.get('/', async (req, res) => {
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try {
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const force = String(req.query?.refresh || '') === '1';
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const latestBizDate = await fetchLatestBizDate().catch(() => null);
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const cacheKey = latestBizDate || 'none';
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if (!force && cachedResult && cachedResult.latestBizDate === cacheKey
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&& Date.now() - cachedResultAt < RESULT_CACHE_TTL_MS) {
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return res.json({ success: true, data: cachedResult, cached: true });
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}
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const asOf = nextDayAsOf(latestBizDate);
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const openingDate = prevDay(latestBizDate);
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const wmsCoIds = DAILY_INVENTORY_BASELINE
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.map((b) => b.wmsCoId)
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.filter((v) => v != null);
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const [gradeMap, takenAt] = await Promise.all([
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fetchGradeMetrics(wmsCoIds, latestBizDate, asOf).catch((e) => {
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console.error('[daily-inventory] 分级计算失败:', e.message);
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return new Map();
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}),
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fetchInventoryTakenAt().catch(() => null),
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]);
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// 期初 = 上一业务日的期末快照；若定时任务漏跑，顺手把当前业务日的快照补齐
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let openingMap = await snapshotDb.fetchSnapshot(openingDate).catch(() => new Map());
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const todayMap = await snapshotDb.fetchSnapshot(latestBizDate).catch(() => new Map());
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const missing = DAILY_INVENTORY_BASELINE
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.filter((b) => b.wmsCoId != null && !todayMap.has(String(b.wmsCoId)))
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.map((b) => {
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const g = gradeMap.get(String(b.wmsCoId));
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return {
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wmsCoId: b.wmsCoId,
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warehouse: b.warehouse,
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dbWarehouse: b.dbWarehouse,
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closingQty: Number(g?.gradeStock ?? 0),
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skuCount: Number(g?.skuCount ?? 0),
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takenAt: takenAt ? new Date(takenAt) : null,
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};
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});
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if (missing.length) {
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await snapshotDb.upsertSnapshot(latestBizDate, missing).catch((e) => {
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console.error('[daily-inventory] 快照补写失败:', e.message);
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});
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}
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// 期初快照也缺失时，用 pm_stockMonitor（精确）/ 实时库存扣流水（近似）推导并补写
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if (openingDate && !openingMap.size) {
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await deriveAndStoreOpeningSnapshot({
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cloudDb,
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snapshotDb,
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baseline: DAILY_INVENTORY_BASELINE,
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openingDate,
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latestBizDate,
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takenAt,
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}).catch((e) => {
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console.error('[daily-inventory] 期初快照推导失败:', e.message);
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});
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openingMap = await snapshotDb.fetchSnapshot(openingDate).catch(() => new Map());
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}
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// 流水列：自期初时刻（最新业务日 01:00）以来的单据合计（带符号，流出为负）
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let flowMap = new Map();
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if (openingDate) {
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flowMap = await fetchFlows(wmsCoIds, `${latestBizDate} 01:00:00`).catch((e) => {
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console.error('[daily-inventory] 流水取数失败:', e.message);
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return new Map();
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});
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}
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const items = DAILY_INVENTORY_BASELINE.map((base) => {
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// 12 个流水列：4 列从业务库单据表取数，其余 8 列无单据来源保持 null（前端渲染为「—」）
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const flows = {};
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for (const col of FLOW_COLUMNS) flows[col.field] = null;
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const itemFlows = base.wmsCoId != null ? flowMap.get(String(base.wmsCoId)) : null;
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if (itemFlows) {
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for (const field of AVAILABLE_FLOW_FIELDS) {
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if (itemFlows[field] != null) flows[field] = Number(itemFlows[field].toFixed(2));
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}
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}
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const grade = base.wmsCoId != null ? gradeMap.get(String(base.wmsCoId)) : null;
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const opening = base.wmsCoId != null ? openingMap.get(String(base.wmsCoId)) : null;
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return {
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warehouse: base.warehouse,
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dbWarehouse: base.dbWarehouse,
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openingQty: opening ? opening.closingQty : null,
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openingQtyExtra: null,
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...flows,
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closingQty: grade?.gradeStock ?? null,
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closingSkuCount: grade?.skuCount ?? null,
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closingTakenAt: takenAt,
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shipQty30d: grade?.shipQty ?? null,
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turnoverDays: grade?.turnoverDays ?? null,
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gradeStock: grade?.gradeStock ?? null,
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warnLevel: grade?.warnLevel ?? null,
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slowLevel: grade?.slowLevel ?? null,
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staleLevel: grade?.staleLevel ?? null,
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zombieLevel: grade?.zombieLevel ?? null,
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ungradedSku: grade?.ungradedSku ?? null,
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hasGrade: Boolean(grade),
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};
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});
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// 合计行：便于与表格总额核对
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const sum = (k) => items.reduce((acc, it) => acc + (Number(it[k]) || 0), 0);
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const totals = { warehouse: '合计', dbWarehouse: '', isTotal: true };
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for (const k of ['openingQty', 'openingQtyExtra', ...FLOW_COLUMNS.map((c) => c.field), 'closingQty', 'warnLevel', 'slowLevel', 'staleLevel', 'zombieLevel']) {
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// 若所有仓库该列均无数据（null），合计同样置空，避免误读为 0
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totals[k] = items.every((it) => it[k] == null) ? null : Number(sum(k).toFixed(2));
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}
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// 合计行周转率按「总发货 / 总库存」重算，避免简单累加失真
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totals.shipQty30d = Number(sum('shipQty30d').toFixed(2));
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const totalStock = sum('closingQty');
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totals.turnoverDays = totalStock > 0
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? Number((sum('shipQty30d') * 100 / totalStock).toFixed(2))
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: null;
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const noOpeningCount = items.filter((it) => it.openingQty == null).length;
|
||
|
||
const payload = {
|
||
openingDate,
|
||
latestBizDate,
|
||
asOf,
|
||
shipWindow: asOf ? { ...shipWindow(asOf), days: SHIP_WINDOW_DAYS } : null,
|
||
flowColumns: FLOW_COLUMNS,
|
||
flowColumnsAvailable: AVAILABLE_FLOW_FIELDS,
|
||
noOpeningCount,
|
||
items,
|
||
totals,
|
||
};
|
||
cachedResult = payload;
|
||
cachedResultAt = Date.now();
|
||
return res.json({ success: true, data: payload });
|
||
} catch (error) {
|
||
console.error('[daily-inventory] 查询失败:', error.message);
|
||
return res.status(500).json({ success: false, error: '每日进销存数据查询失败', detail: error.message });
|
||
}
|
||
});
|
||
|
||
module.exports = router;
|