john
2baf29b3ae
Deliver encrypted health zone, observation API, baseline maturity pipeline, page-data bindings, and H5/WeChat channel integration for health P0. Co-authored-by: Cursor <cursoragent@cursor.com>
100 lines
3.3 KiB
JavaScript
100 lines
3.3 KiB
JavaScript
import assert from 'node:assert/strict';
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import test from 'node:test';
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import { computeHealthBaselines, deviationFromBaseline } from './health-baseline-engine.mjs';
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import { evaluateHealthEvents } from './health-event-engine.mjs';
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import { createHealthObservationService } from './health-observation-service.mjs';
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import { createInMemoryHealthObservationStore } from './health-observation-store.mjs';
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import { buildHealthAssessSummary } from './health-assess-summary.mjs';
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import { buildHealthTimeline } from './health-timeline.mjs';
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const now = Date.UTC(2026, 8, 2, 8, 0, 0);
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function seedBp(store, userId, dayOffset, systolic, diastolic, context = 'morning') {
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const observedAt = now - dayOffset * 24 * 60 * 60 * 1000;
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return store.insert(userId, {
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confirmed: true,
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observedAt,
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metricType: 'bp_systolic',
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valueNum: systolic,
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unit: 'mmHg',
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context,
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source: 'manual',
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qualityFlag: 'ok',
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}).then(() => store.insert(userId, {
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confirmed: true,
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observedAt,
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metricType: 'bp_diastolic',
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valueNum: diastolic,
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unit: 'mmHg',
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context,
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source: 'manual',
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qualityFlag: 'ok',
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}));
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}
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test('health observation service commits blood pressure pair', async () => {
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const service = createHealthObservationService({ store: createInMemoryHealthObservationStore() });
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const result = await service.commit('u1', {
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confirmed: true,
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metricSet: 'blood_pressure',
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systolic: 128,
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diastolic: 76,
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context: 'morning',
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});
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assert.equal(result.observations.length, 2);
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});
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test('timeline groups observations by day', async () => {
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const store = createInMemoryHealthObservationStore();
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await seedBp(store, 'u1', 0, 130, 80);
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const service = createHealthObservationService({ store });
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const timeline = await service.listTimeline('u1');
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assert.equal(timeline.length, 1);
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assert.ok(timeline[0].metrics.bp_systolic);
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});
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test('baseline engine reaches stable after 30 samples', () => {
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const observations = Array.from({ length: 30 }, (_, i) => ({
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metricType: 'bp_systolic',
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valueNum: 120 + (i % 3),
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observedAt: now - i * 24 * 60 * 60 * 1000,
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context: 'morning',
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qualityFlag: 'ok',
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deletedAt: null,
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}));
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const baselines = computeHealthBaselines(observations, { now });
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const target = baselines.find((b) => b.metricType === 'bp_systolic' && b.windowDays === 30 && b.context === 'morning');
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assert.equal(target.maturity, 'stable');
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assert.ok(target.mean > 0);
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});
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test('event engine detects absolute threshold breach', () => {
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const observations = [{
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metricType: 'bp_systolic',
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valueNum: 185,
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observedAt: now,
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context: 'morning',
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qualityFlag: 'ok',
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deletedAt: null,
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}];
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const events = evaluateHealthEvents(observations, [], { now });
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assert.ok(events.some((e) => e.ruleId === 'bp_sys_high'));
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});
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test('assess summary mentions open events when drift exists', () => {
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const observations = Array.from({ length: 30 }, (_, i) => ({
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metricType: 'bp_systolic',
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valueNum: i < 5 ? 150 : 120,
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observedAt: now - i * 24 * 60 * 60 * 1000,
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context: 'morning',
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qualityFlag: 'ok',
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deletedAt: null,
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}));
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const summary = buildHealthAssessSummary(observations, { now });
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assert.match(summary, /基线|记录|诊断/);
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});
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test('deviationFromBaseline computes percent change', () => {
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assert.equal(deviationFromBaseline(132, 120), 10);
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});
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