forked from lunanting/cangku
lunanting
c374cbc713
- 货位口径按前缀：XA/XD/XE=拣货位，XX/X9/X5/X4=存储位 - 判断逻辑改为当日订单量>拣货位库存的超出部分=需从存储位补到拣货位的量（上限存储位库存） - 订单取自 rpt.jst_Orders/jst_qmcust_orderlist（含组合装展开），按最近订单日 - 状态：需补货/存储不足/拣货位充足/今日无单；移除旧的货位清单导入（前缀规则取代） - 前端列与说明同步更新
462 lines
18 KiB
JavaScript
462 lines
18 KiB
JavaScript
'use strict';
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/**
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* 缺货补货监控（订单驱动版，2026-10-22 重写）
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* ==========================================================================
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* 业务需求（用户确认）：
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* 监控崔桥工厂仓（主仓）现有库存 —— 看哪些「拣货库位」的货不够今天的订单拣，
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* 需要从「存储库位」把货补过去。
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*
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* ── 货位口径（前缀规则，用户指定）──────────────────────────────────────
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* 拣货库位（拣货仓位）：以 XA / XD / XE 开头
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* 存储库位（存储仓位）：以 XX / X9 / X5 / X4 开头
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* 其余前缀（DB / Y* / Z* / YC 等，崔桥占比极小）归为「未分类」，不计入拣货/存储。
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*
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* ── 数据源 ───────────────────────────────────────────────────────────
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* 库存：rpt.jst_packInventoryInfo（按 wms_co_id + sku_id 聚合，按前缀切拣货/存储）
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* 订单：rpt.jst_Orders × jst_OrderItem（线上店铺）
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* + rpt.jst_qmcust_orderlist × jst_qmcust_orderlist_item（全民客户 B2B）
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* + 组合装（sku_type='combine'）→ jst_comboProductItem 展开为子件
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* ⚠️ 仓库维度必须用 **wms_co_id**：wh_id 是货主（简尚=1），
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* 按 wh_id 会把多仓混成一个桶。
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* 订单日期：取该仓订单表 send_date 的最大日期（即「最近一个有订单的营业日」，
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* 副本 ETL 有延迟时不会为空，页面会明确标注统计的是哪一天）。
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*
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* ── 判断逻辑（核心）──────────────────────────────────────────────────
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* 对每一个 SKU（在拣货/存储位上有库存）：
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* 拣货位库存 pickQty = 该 SKU 在 XA/XD/XE 货位上的 qty 合计
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* 存储位库存 storageQty = 该 SKU 在 XX/X9/X5/X4 货位上的 qty 合计
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* 当日订单 orderQty = 该 SKU 在订单日期当天的订单量（含组合装展开）
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* 超出订单数 excess = max(0, orderQty − pickQty)
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* —— 拣货位不够今天的订单拣，超出的这部分要从存储位补到拣货位
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* 建议补货量 suggestQty = min(excess, storageQty) （不能超过存储位现有量）
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* 存储缺口 shortfall = max(0, excess − storageQty)（存储位也不够，需采购/急调）
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*
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* 状态：
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* short 存储不足（orderQty > pickQty 且 storageQty < excess）—— 最紧急
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* need 需补货 （orderQty > pickQty 且 storageQty ≥ excess）
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* enough 拣货位充足（0 < orderQty ≤ pickQty）
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* no_order 今日无单（orderQty = 0）
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* （pickQty = 0 且 orderQty > 0 归入 need/short，并标记 pickEmpty 强调）
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*
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* ⚠️ 性能：库存视图聚合 + 当日订单 4 分支 union 各自都要 30s~1min，
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* 必须缓存（默认 10 分钟）+ 启动预热，不要每次请求都回源。
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*/
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const cloudDb = require('../config/cloudDatabase');
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/**
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* 仓库清单：**按 wms_co_id 取数**。
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* 前缀货位口径（XA/XD/XE 拣货、XX/X9/X5/X4 存储）是崔桥工厂仓的约定；
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* 其他仓若用不同前缀，页面会标注，默认聚焦崔桥工厂仓。
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*/
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const WAREHOUSES = Object.freeze({
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cuiqiao: { key: 'cuiqiao', wmsCoId: 15038426, name: '简尚-崔桥工厂仓', shortName: '崔桥工厂仓', order: 10 },
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shandong: { key: 'shandong', wmsCoId: 15137523, name: '简尚-山东青岗集仓', shortName: '山东青岗集仓', order: 20 },
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henglin2: { key: 'henglin2', wmsCoId: 15033302, name: '简尚-横林二仓', shortName: '横林二仓', order: 30 },
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subei: { key: 'subei', wmsCoId: 15038347, name: '简尚-新苏北仓', shortName: '新苏北仓', order: 40 },
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henan: { key: 'henan', wmsCoId: 15539084, name: '简尚-河南洛宁仓', shortName: '河南洛宁仓', order: 50 },
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shandongBrand: { key: 'shandongBrand', wmsCoId: 15182705, name: '简尚-山东品牌仓', shortName: '山东品牌仓', order: 60 },
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cuiqiaoBcp: { key: 'cuiqiaoBcp', wmsCoId: 15718196, name: '简尚-崔桥工厂半成品包装项目仓', shortName: '崔桥半成品包装仓', order: 70 },
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suining: { key: 'suining', wmsCoId: 15032603, name: '简尚-睢宁仓', shortName: '睢宁仓', order: 80 },
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cuiqiaoBrand: { key: 'cuiqiaoBrand', wmsCoId: 15870495, name: '简尚-崔桥工厂品牌仓', shortName: '崔桥品牌仓', order: 90 },
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henglin3: { key: 'henglin3', wmsCoId: 15043561, name: '简尚-横林三仓', shortName: '横林三仓', order: 100 },
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});
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const WAREHOUSE_LIST = Object.values(WAREHOUSES).sort((a, b) => a.order - b.order);
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const DEFAULT_WAREHOUSE_KEY = 'cuiqiao';
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const DEFAULT_CACHE_TTL_MS = 10 * 60 * 1000; // 与上游 ETL 的 10 分钟增量节奏对齐
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const MAX_PAGE_SIZE = 5000;
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// ── 货位前缀规则（用户指定）────────────────────────────────────────
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const PICK_PREFIXES = Object.freeze(['XA', 'XD', 'XE']);
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const STORE_PREFIXES = Object.freeze(['XX', 'X9', 'X5', 'X4']);
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const PICK_PREFIX_IN = PICK_PREFIXES.map((p) => `'${p}'`).join(',');
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const STORE_PREFIX_IN = STORE_PREFIXES.map((p) => `'${p}'`).join(',');
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const STATUS = Object.freeze({
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SHORT: 'short',
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NEED: 'need',
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ENOUGH: 'enough',
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NO_ORDER: 'no_order',
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});
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const STATUS_LABELS = Object.freeze({
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short: '存储不足',
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need: '需补货',
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enough: '拣货位充足',
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no_order: '今日无单',
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});
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// 排序权重：最紧急的排最前
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const STATUS_RANK = Object.freeze({ short: 4, need: 3, no_order: 2, enough: 1 });
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const cacheStore = new Map();
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let cacheEpoch = 0;
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function toText(value, maxLength = 200) {
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return String(value ?? '').trim().slice(0, maxLength);
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}
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function toNumber(value, fallback = 0) {
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const num = Number(value);
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return Number.isFinite(num) ? num : fallback;
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}
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function round2(value) {
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return Math.round((toNumber(value) + Number.EPSILON) * 100) / 100;
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}
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function resolveWarehouseKey(value) {
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const key = toText(value, 32).toLowerCase();
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return WAREHOUSES[key] ? key : DEFAULT_WAREHOUSE_KEY;
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}
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function resolveWarehouse(value) {
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return WAREHOUSES[resolveWarehouseKey(value)];
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}
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function resolveCacheTtlMs() {
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const raw = process.env.BIN_REPLENISH_CACHE_TTL_MS;
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if (raw === undefined || raw === null || String(raw).trim() === '') return DEFAULT_CACHE_TTL_MS;
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const num = Number(raw);
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if (!Number.isFinite(num) || num < 0) return DEFAULT_CACHE_TTL_MS;
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return num;
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}
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/** 订单日期的窗口边界（仅用日期字符串，避免时区问题；副本 send_date 按中国营业日存储） */
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function orderWindow(orderDate) {
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const d = new Date(`${orderDate}T00:00:00Z`);
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d.setUTCDate(d.getUTCDate() + 1);
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const next = d.toISOString().slice(0, 10);
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return { shipFrom: `${orderDate} 00:00:00`, shipTo: `${next} 00:00:00` };
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}
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// ── 副本取数 ────────────────────────────────────────────────────────
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/**
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* 按 SKU 聚合库存，并按货位前缀切出拣货位 / 存储位库存。
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* 只统计用户指定的前缀宇宙（XA/XD/XE/XX/X9/X5/X4），其余货位不计入。
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*/
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async function loadReplicaInventory(warehouse) {
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const sql = `
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SELECT p.sku_id AS skuId,
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SUM(CASE WHEN UPPER(LEFT(LTRIM(p.bin), 2)) IN (${PICK_PREFIX_IN})
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THEN CAST(ISNULL(p.qty, 0) AS DECIMAL(18,2)) ELSE 0 END) AS pickQty,
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SUM(CASE WHEN UPPER(LEFT(LTRIM(p.bin), 2)) IN (${STORE_PREFIX_IN})
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THEN CAST(ISNULL(p.qty, 0) AS DECIMAL(18,2)) ELSE 0 END) AS storageQty,
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MAX(s.name) AS productName,
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MAX(s.supplier_name) AS supplier
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FROM rpt.jst_packInventoryInfo p
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LEFT JOIN rpt.jst_sku s ON s.sku_id = p.sku_id
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WHERE p.wms_co_id = @wmsCoId
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AND ISNULL(p.bin, '') <> ''
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AND UPPER(LEFT(LTRIM(p.bin), 2)) IN (${PICK_PREFIX_IN}, ${STORE_PREFIX_IN})
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GROUP BY p.sku_id`;
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return cloudDb.query(sql, { wmsCoId: warehouse.wmsCoId }, { requestTimeout: 600000 });
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}
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/** 该仓最近的订单日期（线上 + 全民客户 取最大 send_date 日期） */
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async function loadOrderDate(warehouse) {
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const rows = await cloudDb.query(
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`SELECT MAX(d) AS d FROM (
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SELECT MAX(CONVERT(VARCHAR(10), send_date, 23)) AS d
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FROM rpt.jst_Orders WHERE wms_co_id = @wmsCoId
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UNION ALL
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SELECT MAX(CONVERT(VARCHAR(10), send_date, 23)) AS d
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FROM rpt.jst_qmcust_orderlist WHERE wms_co_id = @wmsCoId
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) t`,
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{ wmsCoId: warehouse.wmsCoId },
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{ requestTimeout: 120000 }
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);
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return (rows && rows[0] && rows[0].d) || null;
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}
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/** 当日（订单日期）各 SKU 的订单量：4 个来源分支，组合装展开为子件。 */
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async function loadTodayOrderMap(warehouse, orderDate) {
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if (!orderDate) return new Map();
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const { shipFrom, shipTo } = orderWindow(orderDate);
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const sql = `
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WITH ship AS (
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-- 1) 线上店铺订单：普通商品行
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SELECT oi.sku_id AS skuId,
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SUM(CAST(ISNULL(oi.qty, 0) AS DECIMAL(18,2))) AS orderQty
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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 = @wmsCoId
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AND o.send_date >= @shipFrom AND o.send_date < @shipTo
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AND ISNULL(o.status, '') NOT IN ('Delete', 'Cancelled')
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AND ISNULL(oi.sku_type, 'normal') <> 'combine'
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GROUP BY oi.sku_id
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UNION ALL
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-- 2) 线上店铺订单：组合装行 → 展开为子件
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SELECT ci.src_sku_id AS skuId,
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SUM(CAST(ISNULL(oi.qty, 0) * ISNULL(ci.qty, 0) AS DECIMAL(18,2))) AS orderQty
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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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JOIN rpt.jst_comboProductItem ci ON ci.combine_sku_id = oi.sku_id
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WHERE o.wms_co_id = @wmsCoId
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AND o.send_date >= @shipFrom AND o.send_date < @shipTo
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AND ISNULL(o.status, '') NOT IN ('Delete', 'Cancelled')
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AND oi.sku_type = 'combine'
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GROUP BY ci.src_sku_id
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UNION ALL
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-- 3) 全民客户（B2B 分销）订单：普通商品行（拆单父单 Split 与子单重复，需排除）
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SELECT oi.sku_id AS skuId,
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SUM(CAST(ISNULL(oi.qty, 0) AS DECIMAL(18,2))) AS orderQty
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FROM rpt.jst_qmcust_orderlist_item oi
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JOIN rpt.jst_qmcust_orderlist o ON o.o_id = oi.o_id
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WHERE o.wms_co_id = @wmsCoId
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AND o.send_date >= @shipFrom AND o.send_date < @shipTo
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AND ISNULL(o.status, '') NOT IN ('Delete', 'Cancelled', 'Split', 'Question')
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AND ISNULL(oi.sku_type, 'normal') <> 'combine'
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GROUP BY oi.sku_id
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UNION ALL
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-- 4) 全民客户订单：组合装行 → 展开为子件
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SELECT ci.src_sku_id AS skuId,
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SUM(CAST(ISNULL(oi.qty, 0) * ISNULL(ci.qty, 0) AS DECIMAL(18,2))) AS orderQty
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FROM rpt.jst_qmcust_orderlist_item oi
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JOIN rpt.jst_qmcust_orderlist o ON o.o_id = oi.o_id
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JOIN rpt.jst_comboProductItem ci ON ci.combine_sku_id = oi.sku_id
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WHERE o.wms_co_id = @wmsCoId
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AND o.send_date >= @shipFrom AND o.send_date < @shipTo
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AND ISNULL(o.status, '') NOT IN ('Delete', 'Cancelled', 'Split', 'Question')
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AND oi.sku_type = 'combine'
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GROUP BY ci.src_sku_id
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)
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SELECT skuId, SUM(orderQty) AS orderQty
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FROM ship
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GROUP BY skuId`;
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const runQuery = typeof cloudDb.querySlow === 'function' ? cloudDb.querySlow : cloudDb.query;
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const rows = await runQuery(
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sql,
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{ wmsCoId: warehouse.wmsCoId, shipFrom, shipTo },
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{ requestTimeout: 300000 }
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);
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const map = new Map();
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(rows || []).forEach((row) => {
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const skuId = toText(row.skuId, 120);
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if (skuId) map.set(skuId, toNumber(row.orderQty));
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});
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return map;
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}
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// ── 组装 ────────────────────────────────────────────────────────────
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function buildSkuRows({ invRows, orderMap, orderDate }) {
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const rows = [];
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(invRows || []).forEach((row) => {
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const skuId = toText(row.skuId, 120);
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if (!skuId) return;
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const pickQty = round2(row.pickQty);
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const storageQty = round2(row.storageQty);
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const orderQty = round2(toNumber(orderMap.get(skuId), 0));
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const excess = Math.max(0, round2(orderQty - pickQty));
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let status = STATUS.NO_ORDER;
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let suggestQty = 0;
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let shortfall = 0;
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let pickEmpty = false;
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if (orderQty > 0) {
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if (pickQty <= 0) pickEmpty = true;
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if (excess > 0) {
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const can = Math.min(excess, storageQty);
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suggestQty = round2(can);
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if (storageQty >= excess) {
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status = STATUS.NEED;
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} else {
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status = STATUS.SHORT;
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shortfall = round2(excess - storageQty);
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}
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} else {
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status = STATUS.ENOUGH;
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}
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}
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rows.push({
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skuId,
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productName: toText(row.productName, 300) || '',
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supplier: toText(row.supplier, 120) || '',
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status,
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statusLabel: STATUS_LABELS[status],
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pickQty,
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storageQty,
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totalQty: round2(pickQty + storageQty),
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orderQty,
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excess,
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suggestQty,
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shortfall,
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pickEmpty,
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});
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});
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return rows;
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}
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function buildSummary(rows, orderSkuCount) {
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const summary = {
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skuTotal: rows.length,
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need: 0,
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short: 0,
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enough: 0,
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noOrder: 0,
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pickEmpty: 0,
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suggestQtyTotal: 0,
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orderSkuCount: orderSkuCount || 0,
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orderQtyTotal: 0,
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exceedTotal: 0,
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shortfallTotal: 0,
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pickTotal: 0,
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storageTotal: 0,
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};
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rows.forEach((row) => {
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if (summary[row.status] !== undefined) summary[row.status] += 1;
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summary.suggestQtyTotal += row.suggestQty || 0;
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summary.orderQtyTotal += row.orderQty || 0;
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summary.exceedTotal += row.excess || 0;
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summary.shortfallTotal += row.shortfall || 0;
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summary.pickTotal += row.pickQty || 0;
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summary.storageTotal += row.storageQty || 0;
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if (row.pickEmpty) summary.pickEmpty += 1;
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});
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summary.suggestQtyTotal = round2(summary.suggestQtyTotal);
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summary.orderQtyTotal = round2(summary.orderQtyTotal);
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summary.exceedTotal = round2(summary.exceedTotal);
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summary.shortfallTotal = round2(summary.shortfallTotal);
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summary.pickTotal = round2(summary.pickTotal);
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summary.storageTotal = round2(summary.storageTotal);
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return summary;
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}
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async function loadDataset(warehouse, { refresh = false } = {}) {
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const orderDate = await loadOrderDate(warehouse);
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// 把订单日期纳入缓存键：跨天后自动失效，无需等 TTL
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const cacheKey = `${warehouse.key}::${orderDate || 'none'}`;
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const cached = cacheStore.get(cacheKey);
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const ttl = resolveCacheTtlMs();
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const now = Date.now();
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if (!refresh && cached && ttl > 0 && now - cached.at < ttl) {
|
||
return { ...cached, cacheHit: true };
|
||
}
|
||
|
||
const epoch = cacheEpoch;
|
||
const [invRows, orderMap] = await Promise.all([
|
||
loadReplicaInventory(warehouse),
|
||
loadTodayOrderMap(warehouse, orderDate).catch(() => new Map()),
|
||
]);
|
||
|
||
const rows = buildSkuRows({ invRows, orderMap, orderDate });
|
||
|
||
const dataset = {
|
||
at: now,
|
||
rows,
|
||
warehouse,
|
||
orderDate,
|
||
meta: {
|
||
rowCount: rows.length,
|
||
orderSkuCount: orderMap.size,
|
||
},
|
||
};
|
||
// 只有最新一轮（未被清缓存）的结果才写回，避免清缓存后旧查询覆盖新数据
|
||
if (epoch === cacheEpoch) cacheStore.set(cacheKey, dataset);
|
||
return { ...dataset, cacheHit: false };
|
||
}
|
||
|
||
function invalidateCache(warehouseKey) {
|
||
cacheEpoch += 1;
|
||
if (warehouseKey) cacheStore.delete(resolveWarehouseKey(warehouseKey));
|
||
else cacheStore.clear();
|
||
}
|
||
|
||
function matchesStatus(row, status) {
|
||
const key = toText(status, 20).toLowerCase();
|
||
if (!key || key === 'all') return true;
|
||
if (key === 'action' || key === 'todo' || key === 'replenish') {
|
||
return row.status === STATUS.NEED || row.status === STATUS.SHORT;
|
||
}
|
||
return row.status === key;
|
||
}
|
||
|
||
function sortRows(rows) {
|
||
return rows.slice().sort((a, b) => (
|
||
(STATUS_RANK[b.status] || 0) - (STATUS_RANK[a.status] || 0)
|
||
|| (b.suggestQty || 0) - (a.suggestQty || 0)
|
||
|| (b.excess || 0) - (a.excess || 0)
|
||
|| String(a.skuId).localeCompare(String(b.skuId))
|
||
));
|
||
}
|
||
|
||
function filterRows(rows, { keyword, status }) {
|
||
const kw = toText(keyword, 120).toLowerCase();
|
||
return rows.filter((row) => {
|
||
if (!matchesStatus(row, status)) return false;
|
||
if (!kw) return true;
|
||
return row.skuId.toLowerCase().includes(kw)
|
||
|| String(row.productName || '').toLowerCase().includes(kw)
|
||
|| String(row.supplier || '').toLowerCase().includes(kw);
|
||
});
|
||
}
|
||
|
||
async function getReplenishList(options = {}) {
|
||
const warehouse = resolveWarehouse(options.warehouse);
|
||
const dataset = await loadDataset(warehouse, { refresh: String(options.refresh) === 'true' });
|
||
const filtered = sortRows(filterRows(dataset.rows, options));
|
||
const summary = buildSummary(dataset.rows, dataset.meta.orderSkuCount);
|
||
const page = Math.max(1, Number.parseInt(options.page, 10) || 1);
|
||
const pageSize = Math.min(MAX_PAGE_SIZE, Math.max(1, Number.parseInt(options.pageSize, 10) || 50));
|
||
const start = (page - 1) * pageSize;
|
||
|
||
return {
|
||
rows: filtered.slice(start, start + pageSize),
|
||
total: filtered.length,
|
||
page,
|
||
pageSize,
|
||
summary,
|
||
warehouse,
|
||
warehouses: Object.values(WAREHOUSES),
|
||
orderDate: dataset.orderDate,
|
||
cacheHit: dataset.cacheHit,
|
||
updatedAt: new Date(dataset.at).toISOString(),
|
||
cacheTtlMs: resolveCacheTtlMs(),
|
||
};
|
||
}
|
||
|
||
async function getReplenishExportRows(options = {}) {
|
||
const warehouse = resolveWarehouse(options.warehouse);
|
||
const dataset = await loadDataset(warehouse, { refresh: String(options.refresh) === 'true' });
|
||
return {
|
||
warehouse,
|
||
orderDate: dataset.orderDate,
|
||
rows: sortRows(filterRows(dataset.rows, options)),
|
||
summary: buildSummary(dataset.rows, dataset.meta.orderSkuCount),
|
||
};
|
||
}
|
||
|
||
/** 启动预热：库存视图 + 当日订单 union 各自 30s~1min，不能让首个用户请求等 */
|
||
async function warmBinReplenishCache() {
|
||
try {
|
||
const warehouse = WAREHOUSES[DEFAULT_WAREHOUSE_KEY];
|
||
const t0 = Date.now();
|
||
await loadDataset(warehouse, { refresh: true });
|
||
console.log(`[BinReplenish] 预热完成 ${warehouse.name}，耗时 ${Date.now() - t0}ms`);
|
||
} catch (error) {
|
||
console.warn('[BinReplenish] 预热失败：', error.message);
|
||
}
|
||
}
|
||
|
||
module.exports = {
|
||
DEFAULT_WAREHOUSE_KEY,
|
||
PICK_PREFIXES,
|
||
STORE_PREFIXES,
|
||
STATUS,
|
||
STATUS_LABELS,
|
||
WAREHOUSES,
|
||
clearPickBins: async () => ({ removed: 0 }),
|
||
getReplenishExportRows,
|
||
getReplenishList,
|
||
loadDataset,
|
||
resolveWarehouse,
|
||
resolveWarehouseKey,
|
||
warmBinReplenishCache,
|
||
};
|