feat(ai): AI 教练 —— 晨间简报、运动处方、趋势归因与 Copilot

数值全部在服务端算好再交给模型，模型只做解读。让模型从 CSV 里自己推
z 分数，它算错的次数足以让简报引用图表反驳它的数字。

- services/insights.py：z 分数（28 天个人基线，且**排除当天**——用一个
  值参与算出来的均值去衡量它自己，会把真实离群点摊平）、13 个月趋势斜率
  （按序数日期最小二乘，手表放充电器上一周不会压缩 x 轴）、近 7 天活动量
  对比。
- services/coach.py：三套提示词 + 回复解析，每套都配一个规则引擎版本。
  网关一次生成要几分钟，上游被限流时给一个朴素的答案，好过给一张空卡片。
- services/ai.py：多轮 chat()、SSE stream()、complete()/stream_chat()，
  以及 extract_json()——上游是推理模型，可见输出以思维链开头，所以从末尾
  倒着找最后一个配平的 JSON（字符串感知，扛得住引号里的 } 和转义引号）。
- 接口 briefing / trend-insight / copilot(SSE)，缓存表 ai_insights。
- 前端：今日页晨报卡（后台生成 + 轮询升级）、全局 Copilot 浮窗、指标详情
  页归因面板。features.ai 打开。

实测（对着自建 ai-gateway）：晨报一次 273 秒，缓存命中 18 毫秒——所以简报
绝不能同步阻塞首屏。网关的流式通道比阻塞通道更不可靠：同一条提示词流式
139 秒后返回「所有模型均不可用」，阻塞则成功，因此 stream_chat() 在流式零
输出时对同一模型退回非流式重试。Copilot 实测 TTFB 9ms、全程 40 秒。

顺带修两处：refresh 原来只跳过缓存读、不删行，导致「重新生成」后的轮询读
到旧行、看到 cached 就停了，用户一直盯着他刚要求替换掉的那段字；基线零方差
时原来返回 z=0.0，把「和每一条观测都不同」标成「完全正常」，改为 z=null。

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

client/src/components/TrendInsight.tsx

@@ -0,0 +1,148 @@
+import { useEffect, useRef, useState } from 'react';
+import {
+  apiClient, errorMessage, InsightMeta, TrendInsight as Insight,
+} from '../services/api';
+import './TrendInsight.css';
+
+/**
+ * Metric ids the backend's feature engineering knows, keyed by the id this app
+ * uses. Two names differ (the sleep shares), the rest are identical.
+ *
+ * Kept as an explicit list rather than sent optimistically: the endpoint 400s
+ * on an unknown metric, and a button that reliably fails is worse than no
+ * button on the metrics this cannot explain.
+ */
+const BACKEND_METRIC: Record<string, string> = {
+  steps: 'steps',
+  intensityMinutes: 'intensityMinutes',
+  heartRate: 'heartRate',
+  heartRateVariability: 'heartRateVariability',
+  stress: 'stress',
+  bodyBatteryHigh: 'bodyBatteryHigh',
+  respirationAvg: 'respirationAvg',
+  sleepDuration: 'sleepDuration',
+  sleepQuality: 'sleepQuality',
+  deepShare: 'sleepDeepPct',
+  remShare: 'sleepRemPct',
+  trainingReadiness: 'trainingReadiness',
+  enduranceScore: 'enduranceScore',
+};
+
+export function supportsInsight(metricId: string) {
+  return metricId in BACKEND_METRIC;
+}
+
+const CONFIDENCE_LABEL: Record<string, string> = {
+  high: '证据充分', medium: '证据一般', low: '证据薄弱',
+};
+
+interface Props {
+  /** This app's metric id, e.g. `heartRateVariability`. */
+  metricId: string;
+  startDate: string;
+  endDate: string;
+}
+
+/**
+ * AI attribution for the span currently on the chart.
+ *
+ * The product spec asks for this on a brush selection over a desktop chart.
+ * On a phone the equivalent gesture is the range selector that is already
+ * there, so the panel explains whatever window the user has selected rather
+ * than adding a drag interaction that fights the page's own scrolling.
+ *
+ * On demand, never on load: one generation takes minutes on the gateway, so
+ * running it for every metric a user browses past would spend that on nothing.
+ */
+function TrendInsight({ metricId, startDate, endDate }: Props) {
+  const [insight, setInsight] = useState<Insight | null>(null);
+  const [meta, setMeta] = useState<InsightMeta | null>(null);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState('');
+  const live = useRef(true);
+
+  useEffect(() => {
+    live.current = true;
+    return () => { live.current = false; };
+  }, []);
+
+  // A new window is a different question; clear the old answer rather than
+  // leaving it under a chart it no longer describes.
+  useEffect(() => {
+    setInsight(null);
+    setMeta(null);
+    setError('');
+  }, [metricId, startDate, endDate]);
+
+  const run = async (refresh?: boolean) => {
+    const backend = BACKEND_METRIC[metricId];
+    if (!backend || loading) return;
+    setLoading(true);
+    setError('');
+    try {
+      const data = await apiClient.getTrendInsight(backend, startDate, endDate, refresh);
+      if (!live.current) return;
+      setInsight(data.insight);
+      setMeta(data.meta);
+    } catch (err: any) {
+      if (live.current) setError(errorMessage(err, '归因分析失败'));
+    } finally {
+      if (live.current) setLoading(false);
+    }
+  };
+
+  if (!supportsInsight(metricId)) return null;
+
+  return (
+    <section className="ti">
+      <div className="ti-head">
+        <h3 className="sec-title">AI 归因</h3>
+        {insight && !loading && (
+          <button className="ti-link" onClick={() => run(true)}>重新分析</button>
+        )}
+      </div>
+
+      {!insight && !loading && !error && (
+        <>
+          <p className="ti-hint">
+            分析 {startDate} ~ {endDate} 这段区间内该指标的变化及其关联因素。
+          </p>
+          <button className="ti-run" onClick={() => run()}>分析这段区间</button>
+        </>
+      )}
+
+      {loading && (
+        <div className="ti-loading">
+          <span className="ti-spinner" aria-hidden="true" />
+          正在分析，通常需要 2–5 分钟
+        </div>
+      )}
+
+      {error && <p className="ti-error">{error}</p>}
+
+      {insight && (
+        <div className="ti-body">
+          {insight.summary && <p className="ti-summary">{insight.summary}</p>}
+
+          {insight.drivers.map((d) => (
+            <div className="ti-driver" key={d.factor + d.detail}>
+              <span className="ti-factor">{d.factor}</span>
+              <p>{d.detail}</p>
+            </div>
+          ))}
+
+          {insight.caution && <p className="ti-caution">{insight.caution}</p>}
+
+          <div className="ti-foot">
+            <span>
+              {meta?.source === 'ai' ? `AI 生成${meta.upstream ? ` · ${meta.upstream}` : ''}` : '规则引擎'}
+            </span>
+            <span>{CONFIDENCE_LABEL[insight.confidence]}</span>
+          </div>
+        </div>
+      )}
+    </section>
+  );
+}
+
+export default TrendInsight;