feat(assets): add dimension analysis and authorized user previews

Refactor asset drilldowns with combined dimension filters, parent summaries and progressive loading. Add the hidden /who read-only permission preview with scoped authorization, identity mapping and audit logging. Refine inventory counts and AI问数 presentation.

src/modules/assets/analytics-model.test.ts

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+import assert from 'node:assert/strict';
+import test from 'node:test';
+import type { AssetAnalysisCell } from '../../shared/assets/analytics';
+import {
+  aggregateAnalysisRows,
+  filterAnalysisCells,
+  filterCustomCells,
+  getAnalysisDimensions,
+  getAnalysisSlice,
+  getAvailableDimensions,
+  getCustomAnalysis,
+  getDefaultDimensions,
+  summarizeAnalyticsCells,
+} from './analytics-model';
+
+function cell(overrides: Partial<AssetAnalysisCell>): AssetAnalysisCell {
+  return {
+    type: '18T', model: '车型甲', region: '华东', city: '嘉兴市',
+    assetStatus: 'inventory', assetDisposition: '在库', operationKind: '可运营',
+    inventoryState: 'available', abnormalType: '不适用', abnormalAge: 'unknown',
+    department: '业务一部', manager: '负责人甲', customer: '客户甲', quantity: 1,
+    ...overrides,
+  };
+}
+
+const cells = [
+  cell({ quantity: 5 }),
+  cell({ inventoryState: 'abnormal', abnormalType: '维修', abnormalAge: '30d+', quantity: 3 }),
+  cell({ region: '华南', city: '广州市', inventoryState: 'pending', quantity: 2 }),
+  cell({ assetStatus: 'operating', assetDisposition: '租赁', operationKind: '租赁', inventoryState: 'not-applicable', quantity: 10 }),
+  cell({ type: '49T', region: '华南', city: '深圳市', assetStatus: 'operating', assetDisposition: '自营', operationKind: '自营', inventoryState: 'not-applicable', quantity: 4 }),
+  cell({ assetStatus: 'other', assetDisposition: '其他', operationKind: '未分类', inventoryState: 'not-applicable', quantity: 1 }),
+];
+
+test('inventory subset counts reconcile at group and filtered-total levels without adding to inventory', () => {
+  assert.ok(!getAvailableDimensions('inventory').includes('manager'));
+  assert.ok(!getAvailableDimensions('inventory').includes('customer'));
+  assert.ok(getAvailableDimensions('operating').includes('manager'));
+  assert.ok(getAvailableDimensions('operating').includes('customer'));
+  const result = getCustomAnalysis(cells, { view: 'inventory', dimensions: ['region', 'type'] });
+  assert.equal(result.total, 10);
+  assert.equal(result.summary.pending, 2);
+  assert.equal(result.summary.abnormal, 3);
+  assert.equal(result.rows.reduce((sum, row) => sum + row.totals.pending, 0), 2);
+  assert.equal(result.rows.reduce((sum, row) => sum + row.totals.abnormal, 0), 3);
+  const filtered = getCustomAnalysis(cells, { view: 'inventory', dimensions: ['region', 'type'], filterValues: { region: ['华东'] } });
+  assert.equal(filtered.total, 8);
+  assert.equal(filtered.summary.pending, 0);
+  assert.equal(filtered.summary.abnormal, 3);
+  assert.equal(filtered.rows[0].totals.pending, 0);
+  assert.equal(filtered.rows[0].totals.abnormal, 3);
+});
+
+test('management totals reconcile without double-counting inventory subsets', () => {
+  const summary = summarizeAnalyticsCells(cells);
+  assert.equal(summary.assets, 25);
+  assert.equal(summary.operating, 14);
+  assert.equal(summary.inventory, 10);
+  assert.equal(summary.other, 1);
+  assert.equal(summary.abnormal, 3);
+  assert.equal(summary.pending, 2);
+  assert.equal(summary.abnormalRate, 30);
+  assert.equal(summary.pendingRate, 20);
+  assert.ok(Math.abs(summary.operatingRate - 56) < 1e-10);
+});
+
+test('operating drill keeps region, city, type and operation kind as aggregate levels', () => {
+  let slice = getAnalysisSlice(cells, { view: 'operating' });
+  assert.equal(slice.dimension, 'region');
+  assert.equal(slice.total, 14);
+  assert.deepEqual(slice.rows.map(row => row.count), [10, 4]);
+  assert.equal(slice.rows.reduce((sum, row) => sum + row.share, 0), 100);
+  for (const expectedDimension of ['city', 'type', 'operationKind']) {
+    assert.equal(slice.isLeaf, false);
+    slice = getAnalysisSlice(cells, { view: 'operating', path: slice.rows[0].nextPath });
+    assert.equal(slice.dimension, expectedDimension);
+    assert.equal(slice.total, 10);
+  }
+  slice = getAnalysisSlice(cells, { view: 'operating', path: slice.rows[0].nextPath });
+  assert.equal(slice.isLeaf, true);
+  assert.equal(slice.dimension, null);
+  assert.deepEqual(slice.rows, []);
+  assert.equal(summarizeAnalyticsCells(filterAnalysisCells(cells, slice.filters, slice.metric)).assets, slice.total);
+});
+
+test('asset uses mutually exclusive stock, lease and self-operated dispositions', () => {
+  const slice = getAnalysisSlice(cells, { view: 'assets', filters: { type: '18T' } });
+  assert.equal(slice.dimension, 'assetDisposition');
+  assert.equal(slice.rows.reduce((sum, row) => sum + row.count, 0), slice.total);
+  assert.deepEqual(slice.rows.map(row => row.value).sort(), ['其他', '在库', '租赁'].sort());
+});
+
+test('fixed global dimensions are skipped and conflicting stale paths cannot widen results', () => {
+  const slice = getAnalysisSlice(cells, {
+    view: 'operating', filters: { region: '华南', type: '49T' },
+    path: [{ dimension: 'region', value: '华南' }, { dimension: 'city', value: '深圳市' }],
+  });
+  assert.equal(slice.dimension, 'operationKind');
+  assert.equal(slice.total, 4);
+  assert.deepEqual(slice.path, [{ dimension: 'city', value: '深圳市' }]);
+  const conflict = getAnalysisSlice(cells, {
+    view: 'operating', filters: { region: '华南' }, path: [{ dimension: 'region', value: '华东' }],
+  });
+  assert.equal(conflict.total, 0);
+  assert.deepEqual(conflict.rows, []);
+});
+
+test('abnormal pivot retains all three dimensions once and ends at vehicle type', () => {
+  for (const start of ['abnormalType', 'region', 'abnormalAge'] as const) {
+    const dimensions = getAnalysisDimensions('abnormal', start);
+    assert.equal(dimensions[0], start);
+    assert.equal(dimensions.at(-1), 'type');
+    assert.equal(new Set(dimensions).size, dimensions.length);
+  }
+  const missingTime = cell({ inventoryState: 'abnormal', abnormalType: '维修', abnormalAge: 'unknown', quantity: 2 });
+  const slice = getAnalysisSlice([...cells, missingTime], { view: 'abnormal', abnormalStart: 'abnormalAge' });
+  assert.equal(slice.total, 5);
+  assert.equal(slice.rows.find(row => row.value === 'unknown')?.label, '审批时间缺失');
+  assert.equal(slice.rows.find(row => row.value === 'unknown')?.count, 2);
+});
+
+test('chronological label sort preserves all duration buckets without rounding totals', () => {
+  const durationCells = ['unknown', '30d+', '0-3d', '15-30d', '7-15d', '3-7d'].map(abnormalAge => cell({
+    inventoryState: 'abnormal', abnormalAge: abnormalAge as AssetAnalysisCell['abnormalAge'],
+  }));
+  const rows = aggregateAnalysisRows(durationCells, 'abnormalAge', 'abnormal', 'label');
+  assert.deepEqual(rows.map(row => row.value), ['0-3d', '3-7d', '7-15d', '15-30d', '30d+', 'unknown']);
+  assert.equal(rows.reduce((sum, row) => sum + row.count, 0), 6);
+  assert.equal(summarizeAnalyticsCells([]).operatingRate, 0);
+});
+
+test('custom analysis combines independent dimensions and conserves the selected metric', () => {
+  const analysis = getCustomAnalysis(cells, { view: 'assets', dimensions: ['region', 'type'] });
+  assert.deepEqual(analysis.dimensions, ['region', 'type']);
+  assert.equal(analysis.total, 25);
+  assert.equal(analysis.rows.length, 3);
+  assert.equal(analysis.rows.reduce((sum, row) => sum + row.count, 0), analysis.total);
+  assert.equal(analysis.rows.find(row => row.values.region === '华东')?.count, 19);
+  assert.equal(analysis.rows.find(row => row.values.region === '华南' && row.values.type === '18T')?.count, 2);
+  assert.equal(analysis.rows.find(row => row.values.region === '华南' && row.values.type === '49T')?.count, 4);
+  assert.ok(Math.abs(analysis.rows.reduce((sum, row) => sum + row.share, 0) - 100) < 1e-10);
+});
+
+test('custom composite keys cannot collide when dimension values contain delimiters', () => {
+  const collisionCells = [
+    cell({ region: 'A · B', city: 'C', quantity: 2 }),
+    cell({ region: 'A', city: 'B · C', quantity: 3 }),
+    cell({ region: '["A","B"]', city: '"C"', quantity: 4 }),
+  ];
+  const analysis = getCustomAnalysis(collisionCells, { view: 'assets', dimensions: ['region', 'city'] });
+  assert.equal(analysis.rows.length, 3);
+  assert.equal(new Set(analysis.rows.map(row => row.key)).size, 3);
+  assert.equal(analysis.rows.reduce((sum, row) => sum + row.count, 0), 9);
+});
+
+test('custom filters OR values within a dimension and AND across dimensions', () => {
+  const filterValues = { region: ['华东', '华南'], type: ['18T'], inventoryState: ['available', 'pending'] };
+  const filtered = filterCustomCells(cells, filterValues, 'inventory');
+  assert.equal(summarizeAnalyticsCells(filtered).inventory, 7);
+  const analysis = getCustomAnalysis(cells, { view: 'inventory', dimensions: ['region'], filterValues });
+  assert.equal(analysis.total, 7);
+  assert.deepEqual(analysis.rows.map(row => row.count), [5, 2]);
+  assert.equal(analysis.rows[0].share, 5 / 7 * 100);
+  assert.equal(getCustomAnalysis(cells, { view: 'inventory', dimensions: ['region'], filterValues: { region: ['不存在区域'] } }).total, 0);
+});
+
+test('empty filter selections impose no restriction and filter-only analysis gives one total', () => {
+  const analysis = getCustomAnalysis(cells, { view: 'operating', dimensions: [], filterValues: { region: [] } });
+  assert.equal(analysis.total, 14);
+  assert.equal(analysis.rows.length, 1);
+  assert.equal(analysis.rows[0].label, '合计');
+  assert.equal(analysis.rows[0].count, 14);
+  assert.equal(analysis.rows[0].share, 100);
+  assert.deepEqual(analysis.rows[0].values, {});
+  assert.equal(analysis.summary.assets, 25);
+  assert.equal(getCustomAnalysis([], { view: 'operating', dimensions: [] }).rows.length, 0);
+  assert.equal(getCustomAnalysis([cell({})], { view: 'operating', dimensions: [] }).rows.length, 0);
+});
+
+test('custom shares use the selected metric rather than all assets in the scope', () => {
+  const analysis = getCustomAnalysis(cells, { view: 'operating', dimensions: ['region'] });
+  assert.equal(analysis.total, 14);
+  assert.equal(analysis.summary.assets, 25);
+  assert.equal(analysis.rows.find(row => row.values.region === '华东')?.share, 10 / 14 * 100);
+  assert.equal(analysis.rows.find(row => row.values.region === '华南')?.share, 4 / 14 * 100);
+  assert.equal(analysis.rows.reduce((sum, row) => sum + row.count, 0), analysis.total);
+});
+
+test('custom dimensions remove unknown, duplicate and inapplicable values without losing counts', () => {
+  const analysis = getCustomAnalysis(cells, {
+    view: 'operating',
+    dimensions: ['region', 'bogus', 'region', 'abnormalAge', 'type'] as Parameters<typeof getCustomAnalysis>[1]['dimensions'],
+  });
+  assert.deepEqual(analysis.dimensions, ['region', 'type']);
+  assert.equal(analysis.rows.reduce((sum, row) => sum + row.count, 0), 14);
+  for (const view of ['assets', 'operating', 'inventory', 'abnormal', 'pending', 'department', 'customer'] as const) {
+    assert.equal(getDefaultDimensions(view).length, 1);
+    assert.ok(getAvailableDimensions(view).includes(getDefaultDimensions(view)[0]));
+  }
+});
+
+test('custom sorting supports count order and semantic duration order', () => {
+  const ascending = getCustomAnalysis(cells, { view: 'assets', dimensions: ['region', 'type'], sort: 'count-asc' });
+  assert.deepEqual(ascending.rows.map(row => row.count), [2, 4, 19]);
+  const durationCells = ['unknown', '30d+', '0-3d'].map(abnormalAge => cell({ inventoryState: 'abnormal', abnormalAge: abnormalAge as AssetAnalysisCell['abnormalAge'] }));
+  const byLabel = getCustomAnalysis(durationCells, { view: 'abnormal', dimensions: ['abnormalAge'], sort: 'label' });
+  assert.deepEqual(byLabel.rows.map(row => row.values.abnormalAge), ['0-3d', '30d+', 'unknown']);
+});