docs(uiux): UIUX 设计方案大改 + 5 份作业指导书对齐 + 开发任务文档

- UIUX 文档：填充 19 个缺口（多主体画像/健康度/AI+看板/增长域/洞察域/创始人端/OODA/助推/商密）
- UIUX 文档：插入 6 个新章节（十四~十九），旧章节重编号为二十~三十一，更新目录和交叉引用
- 作业指导书 x5：导航改为 6 域分组，新增 Context Bar/工作模式/Insight Rail/决策线程/多工作区等 UI 概念
- 新建 docs/2-task-uiux.md：50 个代码落地开发任务，按 P0-P6 分优先级 + 8 Sprint 规划
- 后端/前端：大量新增模型、路由、组件（来自之前 Phase 开发）

backend/app/services/predictor.py

@@ -0,0 +1,57 @@
+"""趋势预测 + 异常检测。"""
+
+import statistics
+from datetime import datetime, timezone
+
+
+def predict_trend(historical_scores: list[float], months_ahead: int = 3) -> dict:
+    """基于历史评分预测未来健康度。"""
+    if len(historical_scores) < 2:
+        return {"predicted": [], "confidence": 0.0}
+
+    # 简单线性回归
+    n = len(historical_scores)
+    x_mean = sum(range(n)) / n
+    y_mean = sum(historical_scores) / n
+
+    numerator = sum((i - x_mean) * (y - y_mean) for i, y in enumerate(historical_scores))
+    denominator = sum((i - x_mean) ** 2 for i in range(n))
+
+    if denominator == 0:
+        slope = 0
+    else:
+        slope = numerator / denominator
+
+    intercept = y_mean - slope * x_mean
+
+    predicted = [slope * (n + i) + intercept for i in range(months_ahead)]
+    predicted = [max(0, min(100, p)) for p in predicted]
+
+    # 置信度基于历史数据量
+    confidence = min(0.9, n / 12)
+
+    return {
+        "predicted": [round(p, 1) for p in predicted],
+        "slope": round(slope, 2),
+        "confidence": round(confidence, 2),
+    }
+
+
+def detect_anomalies(values: list[float], threshold: float = 2.0) -> list[int]:
+    """统计方法识别指标突变。"""
+    if len(values) < 3:
+        return []
+
+    mean = statistics.mean(values)
+    stdev = statistics.stdev(values)
+
+    if stdev == 0:
+        return []
+
+    anomalies: list[int] = []
+    for i, v in enumerate(values):
+        z_score = abs(v - mean) / stdev
+        if z_score > threshold:
+            anomalies.append(i)
+
+    return anomalies