[阶段1.1-1.7] 实现完整的认证系统

后端实现:
- 创建 AuthService 包含密码加密、JWT 生成和验证
- 创建 authMiddleware 用于 API 路由保护
- 实现 auth 路由 (register, login, logout, /me)

前端实现:
- 创建 Login 页面 (登录/注册标签页)
- 创建 ProtectedRoute 组件用于路由保护
- 更新 App.tsx 集成路由保护
- 前端 API 客户端已包含认证方法和拦截器

验收标准已满足:
- 用户可以注册和登录
- JWT Token 正确生成和验证
- 受保护的路由需要有效 Token
- 未认证用户重定向到登录页面

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>

backend/services/analysis.py

@@ -0,0 +1,119 @@
+"""
+Analysis service: metric trends + a rule-based recommendation engine.
+
+Replicates the original Node AnalysisService logic. Averages are computed over
+the most recent 14 days of available daily summaries.
+"""
+from services import health
+from db import query_all
+
+METRIC_COLUMNS = {
+    "steps": "steps",
+    "heart_rate": "heart_rate",
+    "sleep_duration": "sleep_duration",
+    "sleep_quality": "sleep_quality",
+    "stress": "stress",
+    "calories_burned": "calories_burned",
+}
+
+
+def get_trends(metric, user_id, start=None, end=None):
+    column = METRIC_COLUMNS.get(metric, "steps")
+    params = [user_id]
+    sql = "WHERE user_id = ?"
+    if start:
+        sql += " AND date >= ?"
+        params.append(start)
+    if end:
+        sql += " AND date <= ?"
+        params.append(end)
+    rows = query_all(
+        f"SELECT date, {column} AS value FROM health_data {sql} "
+        f"AND {column} IS NOT NULL ORDER BY date ASC",
+        params,
+    )
+    return [{"date": r["date"], "value": r["value"]} for r in rows]
+
+
+def get_recommendations(user_id):
+    recent = health.get_summary(user_id)
+    last14 = recent[-14:]
+    recs = []
+
+    if not last14:
+        return [
+            {
+                "id": "no-data",
+                "category": "数据",
+                "recommendation": "暂无健康数据，请先同步你的 Garmin 设备数据。",
+                "priority": "low",
+                "basedOn": [],
+            }
+        ]
+
+    avg = lambda key: sum((r.get(key) or 0) for r in last14) / len(last14)
+
+    avg_steps = avg("steps")
+    sleep_rows = [r["sleep"]["duration"] for r in last14 if r.get("sleep")]
+    avg_sleep = sum(sleep_rows) / len(sleep_rows) if sleep_rows else 0
+    avg_stress = avg("stress")
+    avg_rhr = avg("heartRate")
+    avg_hrv = avg("heartRateVariability")
+
+    if avg_steps > 0 and avg_steps < 8000:
+        recs.append({
+            "id": "steps",
+            "category": "运动",
+            "recommendation": f"近 {len(last14)} 天日均步数约 {round(avg_steps)} 步，低于 8000 步目标，建议每天增加 20 分钟快走。",
+            "priority": "medium",
+            "basedOn": ["steps"],
+        })
+
+    if avg_sleep > 0 and avg_sleep < 7:
+        recs.append({
+            "id": "sleep",
+            "category": "睡眠",
+            "recommendation": f"日均睡眠约 {avg_sleep:.1f} 小时，偏少。建议固定就寝时间，目标 7-8 小时。",
+            "priority": "high",
+            "basedOn": ["sleep_duration"],
+        })
+
+    if avg_stress > 0 and avg_stress > 50:
+        recs.append({
+            "id": "stress",
+            "category": "压力",
+            "recommendation": f"平均压力指数 {round(avg_stress)} 偏高，建议安排放松活动（冥想/散步）。",
+            "priority": "high",
+            "basedOn": ["stress"],
+        })
+
+    if avg_rhr > 0 and avg_rhr > 65:
+        recs.append({
+            "id": "rhr",
+            "category": "心肺",
+            "recommendation": f"静息心率约 {round(avg_rhr)} bpm 偏高，规律有氧运动有助于改善心肺功能。",
+            "priority": "medium",
+            "basedOn": ["heart_rate"],
+        })
+
+    if avg_hrv > 0 and avg_hrv < 40:
+        recs.append({
+            "id": "hrv",
+            "category": "恢复",
+            "recommendation": f"心率变异性（HRV）约 {round(avg_hrv)} ms 偏低，注意恢复与休息，避免过度训练。",
+            "priority": "low",
+            "basedOn": ["heart_rate_variability"],
+        })
+
+    if not recs:
+        recs.append({
+            "id": "good",
+            "category": "状态",
+            "recommendation": "近期各项指标良好，保持当前作息与运动习惯即可。",
+            "priority": "low",
+            "basedOn": [],
+        })
+
+    order = {"high": 0, "medium": 1, "low": 2}
+    recs.sort(key=lambda r: order[r["priority"]])
+    return recs