'use strict';

/**
 * 智能问数智能体 · 层间一致性核对工具
 * ----------------------------------------------------------------------------
 * cross_check：按日核对 门店合计 vs 连锁合计 vs 公司行（营业额 + 有效订单）。
 * 从 data_quality 中独立成工具，供模型对指定区间单独抽查（如环比异常时先排除
 * 数据质量问题）。容差与判定同 data_quality：相对差 >0.5% 记不一致、>20% 判严重。
 */

const { eachDay, ymdDash } = require('./lake.js');

const CROSS_TOL = 0.005;
const CROSS_FAIL = 0.2;

function crossSanitize(args, ctx) {
  const a = args || {};
  const { dateRange } = ctx;
  const from = /^\d{8}$/.test(String(a.from || '')) ? String(a.from) : dateRange.from;
  const to = /^\d{8}$/.test(String(a.to || '')) ? String(a.to) : dateRange.to;
  return {
    from: from < dateRange.from ? dateRange.from : from,
    to: to > dateRange.to ? dateRange.to : to,
  };
}

function sumBy(rows, key) {
  const m = new Map();
  for (const r of rows) m.set(r.date, (m.get(r.date) || 0) + (Number(r[key]) || 0));
  return m;
}

function relDiff(a, b) {
  return Math.abs(a - b) / Math.max(Math.abs(b), 1);
}

function crossHandler(ctx, args) {
  const { svc, entityKey } = ctx;
  const store = svc.store;
  const { from, to } = args;
  const inRange = (r) => r.date >= from && r.date <= to;
  const shopRows = store.queryRows(entityKey, { from, to, scope: 'shop' }).filter(inRange);
  const chainRows = store.queryRows(entityKey, { from, to, scope: 'chain' }).filter(inRange);
  const companyRows = store.queryRows(entityKey, { from, to, scope: 'company' }).filter(inRange);

  if (!shopRows.length && !chainRows.length) {
    return { summary: `区间 ${ymdDash(from)} ~ ${ymdDash(to)} 无可核对的行（门店/连锁层均无数据）。`, data: { from, to, checked: 0 } };
  }

  const metrics = [
    { key: 'grossAmt', label: '营业额' },
    { key: 'validOrderCnt', label: '有效订单' },
  ];
  // 层×指标 各建一张按日求和表（日期字符串不做数值运算）
  const sums = {};
  for (const m of metrics) {
    sums[m.key] = { shop: sumBy(shopRows, m.key), chain: sumBy(chainRows, m.key), company: sumBy(companyRows, m.key) };
  }
  const days = eachDay(from, to).filter((d) => sums.grossAmt.shop.has(d) || sums.grossAmt.chain.has(d));
  const mismatches = [];
  let checked = 0;
  for (const d of days) {
    for (const m of metrics) {
      const shop = sums[m.key].shop.get(d);
      const chain = sums[m.key].chain.get(d);
      const company = sums[m.key].company.get(d);
      // 同层求和口径：门店合计对比连锁合计；连锁合计对比公司行（任一侧缺失跳过该对比）
      const pairs = [];
      if (shop !== undefined && chain !== undefined) pairs.push(['门店合计 vs 连锁', shop, chain]);
      if (chain !== undefined && company !== undefined) pairs.push(['连锁合计 vs 公司', chain, company]);
      for (const [kind, a, b] of pairs) {
        checked += 1;
        const diff = relDiff(a, b);
        if (diff > CROSS_TOL) mismatches.push({ date: d, metric: m.label, kind, a: Math.round(a * 100) / 100, b: Math.round(b * 100) / 100, diffPct: +(diff * 100).toFixed(2) });
      }
    }
  }
  mismatches.sort((x, y) => y.diffPct - x.diffPct);
  const severe = mismatches.some((m) => m.diffPct / 100 > CROSS_FAIL);
  const lines = [];
  if (!mismatches.length) {
    lines.push(`层间一致性通过：抽查 ${days.length} 天 × 2 指标（共 ${checked} 组对比），门店合计/连锁/公司层间一致（容差 ${(CROSS_TOL * 100).toFixed(1)}%）。`);
  } else {
    lines.push(`层间一致性${severe ? '严重不一致' : '存在不一致'}：${mismatches.length} 组对比超容差（共核对 ${checked} 组），最差 ${ymdDash(mismatches[0].date)} ${mismatches[0].kind} ${mismatches[0].metric}：${mismatches[0].a} vs ${mismatches[0].b}（差 ${mismatches[0].diffPct}%）。`);
    for (const m of mismatches.slice(0, 8)) {
      lines.push(`  ${ymdDash(m.date)} ${m.metric} ${m.kind}：${m.a} vs ${m.b}（差 ${m.diffPct}%）`);
    }
    if (mismatches.length > 8) lines.push(`  …其余 ${mismatches.length - 8} 组略`);
    lines.push('层间不一致通常意味着某一粒度取数异常（如门店行全零、部分门店缺数），相关结论应标注「数据质量存疑」。');
  }
  return {
    summary: lines.join('\n'),
    data: {
      from, to, checked, mismatchCount: mismatches.length, severe, mismatches: mismatches.slice(0, 20),
      verdicts: [{
        kind: 'cross-layer', check: `层间一致性 · 门店/连锁/公司（${ymdDash(from)} ~ ${ymdDash(to)}）`,
        status: mismatches.length === 0 ? 'ok' : (severe ? 'fail' : 'warn'),
        detail: mismatches.length === 0
          ? `抽查 ${days.length} 天 × 2 指标共 ${checked} 组对比全部一致`
          : `${mismatches.length} 组超容差，最差 ${ymdDash(mismatches[0].date)} ${mismatches[0].metric} 差 ${mismatches[0].diffPct}%`,
      }],
    },
  };
}

module.exports = { crossSanitize, crossHandler, CROSS_TOL, CROSS_FAIL };