Merge pull request 'feat: 每日进销存补充流水与期初数据，修复合计行 NaN' (#43) from codex/daily-inventory-complete-data-0927 into main
This commit is contained in:
commit
dc12f6753d
@ -5,6 +5,8 @@ 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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@ -210,10 +212,12 @@ async function fetchInventoryTakenAt() {
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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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* - 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：业务库缺调拨/盘点/加工/其它出入库单据表，返回 null（前端显示「—」）
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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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@ -240,7 +244,7 @@ router.get('/', async (req, res) => {
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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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const openingMap = await snapshotDb.fetchSnapshot(openingDate).catch(() => new Map());
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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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@ -261,10 +265,40 @@ router.get('/', async (req, res) => {
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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：业务库无单据来源，一律 null（前端渲染为「—」）
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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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@ -294,7 +328,8 @@ router.get('/', async (req, res) => {
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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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totals[k] = Number(sum(k).toFixed(2));
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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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@ -311,7 +346,7 @@ router.get('/', async (req, res) => {
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asOf,
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shipWindow: asOf ? { ...shipWindow(asOf), days: SHIP_WINDOW_DAYS } : null,
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flowColumns: FLOW_COLUMNS,
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flowColumnsAvailable: false,
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flowColumnsAvailable: AVAILABLE_FLOW_FIELDS,
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noOpeningCount,
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items,
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totals,
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@ -6,7 +6,9 @@
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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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* - 期末数量：业务库实时库存 rpt.jst_inventoryInfo（按 wms_co_id 单仓）
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* - 周转率 / 四级库存量：近 30 天发货 × 实时库存 × SKU 库龄，见 src/api/dailyInventory.js
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* - 12 个流水列：业务库缺少调拨/盘点/加工/其它出入库单据表，暂无法显示（前端渲染为「—」）
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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 src/services/dailyInventoryFlows.js）；其余 8 列缺调拨/盘点/加工/其它出入库单据表，
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* 暂无法显示（前端渲染为「—」）
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*
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* 仓库清单随聚水潭仓库档案变化时需同步维护（来源表：rpt.jst_warehouseInfo）。
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*/
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118
backend/src/services/dailyInventoryFlows.js
Normal file
118
backend/src/services/dailyInventoryFlows.js
Normal file
@ -0,0 +1,118 @@
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'use strict';
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/**
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* 每日进销存 —— 流水列取数（业务库原生口径）
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*
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* 业务库 JSReportReplica 能还原的 4 个流水列（其余 8 列缺单据表，保持「—」）：
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* 采购进仓(+): rpt.jst_purchase × jst_purchaseItem，按 po_date，状态 Confirmed/Finished，取明细 qty
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* （明细 inQty 全为 0 未同步，故用 qty）
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* 采购退货(−): rpt.jst_purchaseout × jst_purchaseoutItem，按 io_date
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* 销售出仓(−): rpt.jst_Orders × jst_OrderItem，按 send_date，排除 Delete/Cancelled
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* （与「库存周转天数」的发货口径一致；与聚水潭进销存导出的出库单口径有偏差，
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* 因为销售出库单明细数量在业务库未同步，无法精确还原）
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* 销售退货(+): rpt.jst_returnAndRefundInfo × jst_returnAndRefundItem，按 as_date，
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* 类型限定 普通退货/拒收退货/换货/维修，取明细 r_qty（实收数量；
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* qty 字段大多未同步，不能用）
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*
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* 符号约定与页面一致：流入为正、流出为负（前端负数标红）。
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* 因为还有 8 类单据（调拨/盘点/加工/其它出入库）在业务库无表，
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* 「期末 ≠ 期初 + 流水之和」，差值即缺失单据的影响。
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*/
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const FLOW_QUERIES = [
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{
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field: 'purchaseIn',
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sign: 1,
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sql: `
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SELECT p.wms_co_id AS wmsCoId,
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SUM(CAST(ISNULL(pi.qty, 0) AS DECIMAL(18, 2))) AS qty
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FROM rpt.jst_purchase p
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JOIN rpt.jst_purchaseItem pi ON pi.po_id = p.po_id
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WHERE p.wms_co_id IN (%WAREHOUSES%)
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AND p.po_date >= @from
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AND ISNULL(p.status, '') IN ('Confirmed', 'Finished')
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GROUP BY p.wms_co_id`,
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},
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{
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field: 'purchaseReturn',
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sign: -1,
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sql: `
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SELECT o.wms_co_id AS wmsCoId,
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SUM(CAST(ISNULL(oi.qty, 0) AS DECIMAL(18, 2))) AS qty
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FROM rpt.jst_purchaseout o
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JOIN rpt.jst_purchaseoutItem oi ON oi.io_id = o.io_id
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WHERE o.wms_co_id IN (%WAREHOUSES%)
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AND o.io_date >= @from
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GROUP BY o.wms_co_id`,
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},
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{
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field: 'salesOut',
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sign: -1,
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sql: `
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SELECT o.wms_co_id AS wmsCoId,
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SUM(CAST(ISNULL(oi.qty, 0) AS DECIMAL(18, 2))) AS qty
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FROM rpt.jst_Orders o
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JOIN rpt.jst_OrderItem oi ON oi.o_id = o.o_id
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WHERE o.wms_co_id IN (%WAREHOUSES%)
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AND o.send_date >= @from
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AND ISNULL(o.status, '') NOT IN ('Delete', 'Cancelled')
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GROUP BY o.wms_co_id`,
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},
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{
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field: 'salesReturn',
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sign: 1,
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sql: `
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SELECT r.wms_co_id AS wmsCoId,
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SUM(CAST(ISNULL(ri.r_qty, 0) AS DECIMAL(18, 2))) AS qty
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FROM rpt.jst_returnAndRefundInfo r
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JOIN rpt.jst_returnAndRefundItem ri ON ri.as_id = r.as_id
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WHERE r.wms_co_id IN (%WAREHOUSES%)
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AND r.as_date >= @from
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AND ISNULL(r.type, '') IN (N'普通退货', N'拒收退货', N'换货', N'维修')
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GROUP BY r.wms_co_id`,
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},
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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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params[`${prefix}${i}`] = v;
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return `@${prefix}${i}`;
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})
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.join(',');
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return { clause, params };
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}
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/** 业务库可还原的流水字段（带符号：流入 +1 / 流出 −1） */
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const AVAILABLE_FLOW_FIELDS = FLOW_QUERIES.map((q) => q.field);
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/**
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* 拉取自 fromText（含）以来各仓的流水合计。
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* @returns {Promise<Map<string, Object>>} key = wmsCoId 字符串，value = { purchaseIn, purchaseReturn, salesOut, salesReturn }（带符号，无记录的字段不出现）
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*/
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async function fetchFlows(cloudDb, wmsCoIds, fromText) {
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const map = new Map();
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if (!wmsCoIds.length || !fromText) return map;
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for (const q of FLOW_QUERIES) {
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const { clause, params } = buildInClause(wmsCoIds, 'w');
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const sqlText = q.sql.replace('%WAREHOUSES%', clause);
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const rows = await cloudDb.query(sqlText, { ...params, from: fromText }, { requestTimeout: 120000 });
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for (const r of rows) {
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const key = String(r.wmsCoId);
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const entry = map.get(key) || {};
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const qty = Number(r.qty) || 0;
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entry[q.field] = q.sign * qty;
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map.set(key, entry);
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}
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}
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return map;
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}
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module.exports = {
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AVAILABLE_FLOW_FIELDS,
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FLOW_QUERIES,
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fetchFlows,
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buildInClause,
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};
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129
backend/src/services/dailyInventoryOpening.js
Normal file
129
backend/src/services/dailyInventoryOpening.js
Normal file
@ -0,0 +1,129 @@
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'use strict';
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/**
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* 每日进销存 —— 期初快照缺失时的兜底推导
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*
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* 正常情况下「期初 = 上一业务日快照」（每日 01:30 自动落库）。
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* 若快照缺失（新纳入仓库、服务停机等），按两条路推导出「上一业务日期末结存」并补写快照：
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*
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* 路径 A（精确）：rpt.pm_stockMonitor（报表库每日 01:00 跑批，T-1 口径）
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* 该表 date = 最新业务日 D 的行，就是「D-1 期末」的库存监控值，
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* 按 warehouse（= 监控表仓库名 dbWarehouse）聚合 jsStock 即可。
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* 覆盖基线中与监控表仓库名一致的仓库（实测 14/26，
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* 运营专用仓 / 京东仓等虚拟仓不在监控表中）。
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*
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* 路径 B（推导）：实时库存扣流水回推
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* closing(D-1) = 实时库存 − 自 D 01:00 以来的净流入
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* 净流入 = 采购进仓 + 销售退货 − 销售出仓 − 采购退货（带符号流水求和）
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* 仅覆盖路径 A 匹配不到的仓库。因业务库缺 8 类单据表，
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* 若该仓有未记录的调拨/盘点等流水，推导值会偏离真实历史，属近似值。
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*/
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const { fetchFlows } = require('./dailyInventoryFlows');
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/**
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* 推导 openingDate 的期末快照并补写。
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* @param {Object} opts
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* @param {Object} opts.cloudDb 业务库（mssql）
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* @param {Object} opts.snapshotDb 本地快照表访问模块
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* @param {Array} opts.baseline 仓库基线（warehouse/dbWarehouse/wmsCoId）
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* @param {string} opts.openingDate 期初业务日（YYYY-MM-DD，即要补快照的日期）
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* @param {string} opts.latestBizDate 最新业务日（YYYY-MM-DD，pm_stockMonitor 的 date）
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* @param {Date|null} opts.takenAt 实时库存数据时间
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* @returns {Promise<{count:number, pmCount:number, derivedCount:number}>}
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*/
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async function deriveAndStoreOpeningSnapshot(opts) {
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const { cloudDb, snapshotDb, baseline, openingDate, latestBizDate, takenAt } = opts;
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if (!openingDate || !latestBizDate) return { count: 0, pmCount: 0, derivedCount: 0 };
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const whs = baseline.filter((b) => b.wmsCoId != null);
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const wmsCoIds = whs.map((b) => b.wmsCoId);
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const flowFrom = `${latestBizDate} 01:00:00`;
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// 路径 A：pm_stockMonitor 按仓库名聚合
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const dbNames = whs.map((b) => b.dbWarehouse).filter(Boolean);
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let pmMap = new Map();
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if (dbNames.length) {
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const params = {};
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const inCls = dbNames.map((n, i) => {
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params[`n${i}`] = n;
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return `@n${i}`;
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}).join(',');
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try {
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const rows = await cloudDb.query(
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`SELECT warehouse,
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SUM(CAST(ISNULL(jsStock, 0) AS DECIMAL(18, 2))) AS qty,
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SUM(CASE WHEN ISNULL(jsStock, 0) > 0.0001 THEN 1 ELSE 0 END) AS skuCount
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FROM rpt.pm_stockMonitor
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WHERE CONVERT(VARCHAR(10), date, 23) = @bizDate
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AND warehouse IN (${inCls})
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GROUP BY warehouse`,
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{ ...params, bizDate: latestBizDate },
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{ requestTimeout: 120000 }
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);
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pmMap = new Map(rows.map((r) => [r.warehouse, r]));
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} catch (error) {
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console.error('[daily-inventory-opening] pm_stockMonitor 聚合失败:', error.message);
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}
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}
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// 路径 B：实时库存 + 流水回推
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let realtimeMap = new Map();
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try {
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const rows = await cloudDb.query(
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`SELECT i.wms_co_id AS wmsCoId,
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ISNULL(SUM(CAST(i.qty AS DECIMAL(18, 2))), 0) AS qty,
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SUM(CASE WHEN ABS(ISNULL(i.qty, 0)) > 0.0001 THEN 1 ELSE 0 END) AS skuCount
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FROM rpt.jst_inventoryInfo i
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WHERE i.wms_co_id IN (${wmsCoIds.map((_, i) => `@w${i}`).join(',')})
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GROUP BY i.wms_co_id`,
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Object.fromEntries(wmsCoIds.map((id, i) => [`w${i}`, id])),
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{ requestTimeout: 120000 }
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);
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realtimeMap = new Map(rows.map((r) => [String(r.wmsCoId), r]));
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} catch (error) {
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console.error('[daily-inventory-opening] 实时库存聚合失败:', error.message);
|
||||
}
|
||||
|
||||
let flowMap = new Map();
|
||||
try {
|
||||
flowMap = await fetchFlows(cloudDb, wmsCoIds, flowFrom);
|
||||
} catch (error) {
|
||||
console.error('[daily-inventory-opening] 流水取数失败:', error.message);
|
||||
}
|
||||
|
||||
let pmCount = 0;
|
||||
let derivedCount = 0;
|
||||
const payload = whs.map((b) => {
|
||||
const pm = pmMap.get(b.dbWarehouse);
|
||||
let closingQty;
|
||||
let skuCount;
|
||||
if (pm) {
|
||||
// 路径 A：报表库 01:00 跑批的 T-1 库存监控值（精确）
|
||||
closingQty = Number(pm.qty) || 0;
|
||||
skuCount = Number(pm.skuCount) || 0;
|
||||
pmCount += 1;
|
||||
} else {
|
||||
// 路径 B：实时库存 − 净流入（带符号流水求和，流出为负）
|
||||
const rt = realtimeMap.get(String(b.wmsCoId));
|
||||
const f = flowMap.get(String(b.wmsCoId)) || {};
|
||||
const netInflow = (f.purchaseIn || 0) + (f.salesReturn || 0) + (f.salesOut || 0) + (f.purchaseReturn || 0);
|
||||
closingQty = Number(rt?.qty ?? 0) - netInflow;
|
||||
skuCount = Number(rt?.skuCount ?? 0);
|
||||
derivedCount += 1;
|
||||
}
|
||||
return {
|
||||
wmsCoId: b.wmsCoId,
|
||||
warehouse: b.warehouse,
|
||||
dbWarehouse: b.dbWarehouse,
|
||||
closingQty: Number(closingQty.toFixed(2)),
|
||||
skuCount,
|
||||
takenAt: takenAt ? new Date(takenAt) : null,
|
||||
};
|
||||
});
|
||||
|
||||
const count = await snapshotDb.upsertSnapshot(openingDate, payload);
|
||||
console.log(`[daily-inventory-opening] 已补写 ${openingDate} 快照 ${count}/${whs.length} 仓（pm_stockMonitor 精确 ${pmCount}，流水回推 ${derivedCount}）`);
|
||||
return { count, pmCount, derivedCount };
|
||||
}
|
||||
|
||||
module.exports = { deriveAndStoreOpeningSnapshot };
|
||||
107
backend/test/dailyInventoryFlows.test.js
Normal file
107
backend/test/dailyInventoryFlows.test.js
Normal file
@ -0,0 +1,107 @@
|
||||
'use strict';
|
||||
|
||||
const test = require('node:test');
|
||||
const assert = require('node:assert/strict');
|
||||
|
||||
const {
|
||||
AVAILABLE_FLOW_FIELDS,
|
||||
buildInClause,
|
||||
fetchFlows,
|
||||
} = require('../src/services/dailyInventoryFlows');
|
||||
const { deriveAndStoreOpeningSnapshot } = require('../src/services/dailyInventoryOpening');
|
||||
|
||||
const BASELINE = [
|
||||
{ warehouse: '仓A主仓', dbWarehouse: '仓A', wmsCoId: 101 },
|
||||
{ warehouse: '仓B主仓', dbWarehouse: '仓B', wmsCoId: 102 },
|
||||
{ warehouse: '仓C主仓', dbWarehouse: null, wmsCoId: 103 },
|
||||
];
|
||||
|
||||
/** 构造一个按 SQL 关键字路由的假业务库 */
|
||||
function fakeCloudDb(handler) {
|
||||
return { query: async (sql, params) => handler(String(sql), params || {}) };
|
||||
}
|
||||
|
||||
test('buildInClause 展开参数且不为 async', () => {
|
||||
const { clause, params } = buildInClause([1, 2], 'w');
|
||||
assert.equal(clause, '@w0,@w1');
|
||||
assert.deepEqual(params, { w0: 1, w1: 2 });
|
||||
});
|
||||
|
||||
test('fetchFlows 查询四类单据并按符号约定返回（流入正、流出负）', async () => {
|
||||
const seen = [];
|
||||
const db = fakeCloudDb((sql, params) => {
|
||||
seen.push(sql);
|
||||
if (sql.includes('jst_purchaseItem')) return [{ wmsCoId: 101, qty: '100' }];
|
||||
if (sql.includes('jst_purchaseoutItem')) return [{ wmsCoId: 101, qty: '30' }];
|
||||
if (sql.includes('jst_OrderItem')) return [{ wmsCoId: 101, qty: '50' }];
|
||||
if (sql.includes('jst_returnAndRefundItem')) return [{ wmsCoId: 101, qty: '8' }];
|
||||
return [];
|
||||
});
|
||||
const map = await fetchFlows(db, [101, 102], '2026-09-26 01:00:00');
|
||||
const f = map.get('101');
|
||||
assert.equal(f.purchaseIn, 100, '采购进仓为流入正数');
|
||||
assert.equal(f.purchaseReturn, -30, '采购退货为流出负数');
|
||||
assert.equal(f.salesOut, -50, '销售出仓为流出负数');
|
||||
assert.equal(f.salesReturn, 8, '销售退货为流入正数');
|
||||
assert.equal(seen.length, 4, '应发出 4 条单据查询');
|
||||
assert.ok(seen.every((s) => s.includes('@from')), '每条查询都带 @from 参数');
|
||||
assert.deepEqual(AVAILABLE_FLOW_FIELDS.sort(), ['purchaseIn', 'purchaseReturn', 'salesOut', 'salesReturn']);
|
||||
});
|
||||
|
||||
test('fetchFlows 空仓库列表直接返回空 Map', async () => {
|
||||
const map = await fetchFlows(fakeCloudDb(() => []), [], '2026-09-26 01:00:00');
|
||||
assert.equal(map.size, 0);
|
||||
});
|
||||
|
||||
test('deriveAndStoreOpeningSnapshot：监控表命中走精确值，未命中用实时库存扣流水回推', async () => {
|
||||
const upserts = [];
|
||||
const cloudDb = fakeCloudDb((sql, params) => {
|
||||
if (sql.includes('pm_stockMonitor')) {
|
||||
// 只有仓A在监控表（精确路径）
|
||||
return [{ warehouse: '仓A', qty: '500', skuCount: '12' }];
|
||||
}
|
||||
if (sql.includes('jst_inventoryInfo')) {
|
||||
return [
|
||||
{ wmsCoId: 101, qty: '520', skuCount: '13' },
|
||||
{ wmsCoId: 102, qty: '300', skuCount: '9' },
|
||||
{ wmsCoId: 103, qty: '10', skuCount: '2' },
|
||||
];
|
||||
}
|
||||
if (sql.includes('jst_purchaseItem')) return [{ wmsCoId: 102, qty: '60' }];
|
||||
if (sql.includes('jst_purchaseoutItem')) return [{ wmsCoId: 102, qty: '10' }];
|
||||
if (sql.includes('jst_OrderItem')) return [{ wmsCoId: 102, qty: '40' }];
|
||||
if (sql.includes('jst_returnAndRefundItem')) return [];
|
||||
return [];
|
||||
});
|
||||
const snapshotDb = {
|
||||
async upsertSnapshot(bizDate, rows) {
|
||||
upserts.push({ bizDate, rows });
|
||||
return rows.length;
|
||||
},
|
||||
};
|
||||
|
||||
const result = await deriveAndStoreOpeningSnapshot({
|
||||
cloudDb,
|
||||
snapshotDb,
|
||||
baseline: BASELINE,
|
||||
openingDate: '2026-09-25',
|
||||
latestBizDate: '2026-09-26',
|
||||
takenAt: null,
|
||||
});
|
||||
|
||||
assert.equal(result.count, 3);
|
||||
assert.equal(result.pmCount, 1, '仅仓A走 pm_stockMonitor 精确路径');
|
||||
assert.equal(result.derivedCount, 2, '仓B/仓C走流水回推路径');
|
||||
assert.equal(upserts.length, 1);
|
||||
assert.equal(upserts[0].bizDate, '2026-09-25');
|
||||
|
||||
const byWh = new Map(upserts[0].rows.map((r) => [r.wmsCoId, r]));
|
||||
// 仓A：精确值 500，不受流水影响
|
||||
assert.equal(byWh.get(101).closingQty, 500);
|
||||
// 仓B：300 − 净流入(60 − 10 − 40) = 300 − 10 = 290
|
||||
assert.equal(byWh.get(102).closingQty, 290);
|
||||
// 仓C：无流水 → 等于实时库存 10
|
||||
assert.equal(byWh.get(103).closingQty, 10);
|
||||
// 带回基线仓库名，保证后续按 wmsCoId 读取正确
|
||||
assert.equal(byWh.get(102).warehouse, '仓B主仓');
|
||||
});
|
||||
@ -15,21 +15,22 @@ export const COLUMN_SOURCE = {
|
||||
};
|
||||
|
||||
const NO_SOURCE_TIP = '业务库 JSReportReplica 缺少该单据表，无法还原，待 DBA 补充后自动接入（详见 docs/库存流水单据表需求.md）';
|
||||
const FLOW_CALIBER_NOTE = '口径说明：与聚水潭进销存导出可能存在少量偏差（业务库缺 8 类单据表，导出口径含调拨/盘点等，本页只还原数据库可得的 4 类单据）';
|
||||
|
||||
/** 表格存在两个名为「期初」的列，第二个加后缀区分 */
|
||||
export const DAILY_INVENTORY_COLUMNS = [
|
||||
{ key: 'warehouse', label: '仓库', source: 'snapshot', tip: '取自主数据表 rpt.jst_warehouseInfo 的仓库名，表格显示为「XX主仓」' },
|
||||
{ key: 'openingQty', label: '期初数量', source: 'snapshot', tip: '上一业务日的期末结存，取自本地快照表 daily_inventory_snapshot（每日 01:30 自动落库）' },
|
||||
{ key: 'openingQty', label: '期初数量', source: 'snapshot', tip: '上一业务日的期末结存，取自本地快照表 daily_inventory_snapshot（每日 01:30 自动落库）；快照缺失时自动用 pm_stockMonitor（精确）或实时库存扣流水（近似）推导补写' },
|
||||
{ key: 'processIn', label: '加工进仓', source: 'unavailable', tip: `加工进仓。${NO_SOURCE_TIP}` },
|
||||
{ key: 'purchaseReturn', label: '采购退货', source: 'unavailable', tip: `采购退货。业务库 jst_purchaseout 有单据但数值与聚水潭进销存口径不一致，暂不展示` },
|
||||
{ key: 'purchaseIn', label: '采购进仓', source: 'unavailable', tip: `采购进仓。业务库 jst_purchase 按日汇总与进销存口径不一致（崔桥 9-23 基准 1295，库内仅数百），暂不展示` },
|
||||
{ key: 'purchaseReturn', label: '采购退货', source: 'runtime', tip: `采购退货（流出，负数）。rpt.jst_purchaseout × jst_purchaseoutItem，按 io_date 统计。${FLOW_CALIBER_NOTE}` },
|
||||
{ key: 'purchaseIn', label: '采购进仓', source: 'runtime', tip: `采购进仓（流入）。rpt.jst_purchase × jst_purchaseItem，按 po_date 统计，仅计 Confirmed/Finished 状态。${FLOW_CALIBER_NOTE}` },
|
||||
{ key: 'otherIn', label: '其它进仓', source: 'unavailable', tip: `其它进仓。${NO_SOURCE_TIP}` },
|
||||
{ key: 'otherReturn', label: '其它退货', source: 'unavailable', tip: `其它退货。${NO_SOURCE_TIP}` },
|
||||
{ key: 'salesOut', label: '销售出仓', source: 'unavailable', tip: `销售出仓。业务库订单表任何日期字段都还原不出日度出库量（崔桥 9-23 基准 4273，send_date 仅 1769），暂不展示` },
|
||||
{ key: 'salesOut', label: '销售出仓', source: 'runtime', tip: `销售出仓（流出，负数）。rpt.jst_Orders × jst_OrderItem，按 send_date 统计，排除 Delete/Cancelled，与「库存周转天数」的发货口径一致。${FLOW_CALIBER_NOTE}` },
|
||||
{ key: 'stocktaking', label: '盘点', source: 'unavailable', tip: `盘点。${NO_SOURCE_TIP}` },
|
||||
{ key: 'processOut', label: '加工出仓', source: 'unavailable', tip: `加工出仓。${NO_SOURCE_TIP}` },
|
||||
{ key: 'transferOut', label: '调拨出', source: 'unavailable', tip: `调拨出。${NO_SOURCE_TIP}` },
|
||||
{ key: 'salesReturn', label: '销售退货', source: 'unavailable', tip: `销售退货。业务库 jst_returnAndRefundInfo 数值与进销存口径不一致，暂不展示` },
|
||||
{ key: 'salesReturn', label: '销售退货', source: 'runtime', tip: `销售退货（流入）。rpt.jst_returnAndRefundInfo × jst_returnAndRefundItem，按 as_date 统计，类型限定普通退货/拒收退货/换货/维修，取明细实收数量 r_qty。${FLOW_CALIBER_NOTE}` },
|
||||
{ key: 'transferIn', label: '调拨入', source: 'unavailable', tip: `调拨入。${NO_SOURCE_TIP}` },
|
||||
{ key: 'openingQtyExtra', label: '期初(2)', source: 'unavailable', tip: '聚水潭导出表中的第二个「期初」列，业务库无对应来源' },
|
||||
{ key: 'otherOut', label: '其它出库', source: 'unavailable', tip: `其它出库。${NO_SOURCE_TIP}` },
|
||||
@ -47,10 +48,11 @@ export const FORMULA_DESCRIPTION = [
|
||||
'· 期初数量：项目本地快照表 daily_inventory_snapshot 中「上一业务日」的期末结存。',
|
||||
' 业务库 JSReportReplica 没有按天的历史库存结存表，因此由本系统每天 01:30 把 26 仓实时库存落一份快照，',
|
||||
' 今日期初 = 昨日快照，逐日自动滚动。',
|
||||
'· 12 个流水列（加工进仓 / 采购退货 / 采购进仓 / 其它进仓 / 其它退货 / 销售出仓 / 盘点 / 加工出仓 / 调拨出 / 销售退货 / 调拨入 / 其它出库）：',
|
||||
' 业务库缺少调拨 / 盘点 / 加工 / 其它出入库单据表；已有的采购与销售单据按日汇总后与聚水潭进销存口径不一致',
|
||||
' （崔桥 9-23 销售出仓基准 4273，订单表 send_date 仅 1769，偏差约 60%），因此不展示估算值，',
|
||||
' 已整理单据表需求给 DBA，补齐后自动接入。',
|
||||
'· 流水列（12 列）：其中 4 列已从业务库单据表还原 ——',
|
||||
' 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（加工进仓 / 其它进仓 / 其它退货 / 盘点 / 加工出仓 / 调拨出 / 调拨入 / 其它出库）业务库无单据表，显示「—」，',
|
||||
' 已整理单据表需求给 DBA，补齐后自动接入。因此「期初 + 流水」与「期末」暂不相等，差值即缺失单据的影响。',
|
||||
'· 期末数量：运行时取聚水潭实时库存 rpt.jst_inventoryInfo（按 wms_co_id 单仓），随刷新滚动。',
|
||||
'· 库存周转天数 / 四级库存量：运行时计算，库龄基准时刻为快照日次日 01:00。',
|
||||
'',
|
||||
|
||||
@ -126,6 +126,9 @@ const DailyInventory = () => {
|
||||
render: (value, record) => {
|
||||
if (record?.isTotal) {
|
||||
// 合计行：四级库存量累加，周转率按「总发货 / 总库存」重算（后端已给出）
|
||||
if (col.key === 'warehouse') return <span className="daily-inventory-warehouse">{value}</span>;
|
||||
// 流水列暂无数据来源时，合计行同样显示「—」，避免误读为 0
|
||||
if (isUnavailable) return <span className="daily-inventory-cell--na">—</span>;
|
||||
if (col.key === 'turnoverDays') return percent(value);
|
||||
return int(value);
|
||||
}
|
||||
@ -281,12 +284,13 @@ const DailyInventory = () => {
|
||||
<Alert
|
||||
type="info"
|
||||
showIcon
|
||||
message="全部数据来自数据库，12 个流水列暂缺单据来源"
|
||||
message="全部数据来自数据库，流水列已接入 4／12"
|
||||
description={
|
||||
<span>
|
||||
期初数量取本地快照表中 <b>{data?.openingDate || '—'}</b> 的期末结存（每日 01:30 自动落库，逐日滚动）；
|
||||
期初数量取本地快照表中 <b>{data?.openingDate || '—'}</b> 的期末结存（每日 01:30 自动落库，逐日滚动；快照缺失时自动用库存监控表／实时库存扣流水推导补写）；
|
||||
期末数量与右侧 5 列为运行时从业务库实时取数。
|
||||
12 个流水列因业务库缺少调拨／盘点／加工／其它出入库单据表、且已有采购与销售单据口径不一致，暂显示「—」，已向 DBA 提交补表需求。
|
||||
流水列中采购进仓／采购退货／销售出仓／销售退货已从业务库单据表取数；
|
||||
其余 8 列（加工／其它／盘点／调拨）因业务库缺单据表仍显示「—」，已向 DBA 提交补表需求，因此「期初＋流水」与「期末」暂不相等。
|
||||
{data?.noOpeningCount > 0 ? ` 其中 ${data.noOpeningCount} 个仓尚无 ${data.openingDate} 的期初快照（新纳入的仓库从次日起自动补齐）。` : ''}
|
||||
{noGradeCount > 0 ? ` 其中 ${noGradeCount} 个仓在聚水潭库存表中无有效库存，周转率与四级库存量显示「—」。` : ''}
|
||||
</span>
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user