MiMo AI 能力扩展：TAF解读、概率分布解读、异常检测、市场概览

AI 解读字段扩展:
    - 新增 taf_read_zh/en：解读机场预报中影响今日峰值窗口的变化
    - 新增 probability_read_zh/en：描述概率分布形态（最高桶、偏左/偏右）
    - stream max_tokens 900→1200 容纳新输出字段
    - 缓存 key 简化为 METAR原文+观测时间，大幅提升命中率
    - 兜底函数补全 TAF 和概率字段的确定性生成

    异常检测:
    - 纯数学计算，零 AI 延迟：实测温度 vs 全部模型预测上下限
    - 三级告警：breakout_above / breakout_below / deviation

    市场概览:
    - 新增 POST /api/scan/terminal/overview（MiMo 批量解读，缓存10分钟）
    - 前端 MarketOverviewBanner 可折叠横幅（顶栏与标签栏之间）
    - 移动端适配 640px/768px 断点，暗色/亮色双主题

    Scope-risk: MEDIUM — 170 测试通过，TypeScript 零错误，ruff 零告警
    Tested: python -m pytest -q (170 passed), npx tsc --noEmit (0 errors), ruff check .

web/services/anomaly_detection.py

@@ -0,0 +1,102 @@
+"""Anomaly detection — pure math, no AI call.
+
+Flags cities where current observations deviate from model predictions.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+from web.scan_city_ai_helpers import _safe_float
+
+
+def _check_city_anomaly(
+    data: Dict[str, Any],
+    *,
+    high_temp_threshold: float = 2.0,
+) -> Optional[Dict[str, Any]]:
+    """Return anomaly flag if current observation breaks model cluster bounds."""
+    current = data.get("current") if isinstance(data.get("current"), dict) else {}
+    airport = data.get("airport_current") if isinstance(data.get("airport_current"), dict) else {}
+    multi = data.get("multi_model") if isinstance(data.get("multi_model"), dict) else {}
+    deb = data.get("deb") if isinstance(data.get("deb"), dict) else {}
+
+    observed = _safe_float(current.get("temp") or airport.get("temp"))
+    if observed is None:
+        return None
+
+    model_highs = [
+        _safe_float(v)
+        for v in multi.values()
+        if _safe_float(v) is not None
+    ]
+    deb_pred = _safe_float(deb.get("prediction"))
+    if deb_pred is not None:
+        model_highs.append(deb_pred)
+
+    if not model_highs:
+        return None
+
+    model_max = max(model_highs)
+    model_min = min(model_highs)
+    model_median = sorted(model_highs)[len(model_highs) // 2]
+
+    delta_above_max = observed - model_max
+    delta_below_min = model_min - observed
+    delta_from_median = observed - model_median
+
+    anomaly: Optional[Dict[str, Any]] = None
+
+    if delta_above_max > high_temp_threshold:
+        anomaly = {
+            "level": "breakout_above",
+            "observed": observed,
+            "model_max": model_max,
+            "delta": round(delta_above_max, 1),
+            "model_count": len(model_highs),
+        }
+    elif delta_below_min > high_temp_threshold:
+        anomaly = {
+            "level": "breakout_below",
+            "observed": observed,
+            "model_min": model_min,
+            "delta": round(delta_below_min, 1),
+            "model_count": len(model_highs),
+        }
+    elif abs(delta_from_median) > 1.5:
+        anomaly = {
+            "level": "deviation",
+            "observed": observed,
+            "model_median": model_median,
+            "delta": round(delta_from_median, 1),
+            "model_count": len(model_highs),
+        }
+
+    if anomaly:
+        anomaly.update(
+            {
+                "city": data.get("name") or data.get("city"),
+                "local_date": data.get("local_date"),
+                "temp_unit": data.get("temp_symbol", "°C"),
+                "deb_prediction": deb_pred,
+            }
+        )
+    return anomaly
+
+
+def detect_scan_terminal_anomalies(
+    rows: List[Dict[str, Any]],
+    *,
+    high_temp_threshold: float = 2.0,
+) -> List[Dict[str, Any]]:
+    """Scan all terminal rows and return anomaly flags."""
+    anomalies = []
+    for row in rows:
+        if not isinstance(row, dict):
+            continue
+        city_data = row.get("city_data") or row
+        flag = _check_city_anomaly(city_data, high_temp_threshold=high_temp_threshold)
+        if flag:
+            flag["row_id"] = row.get("row_id") or row.get("id")
+            anomalies.append(flag)
+    return anomalies