Show usable recent DEB accuracy

frontend/components/dashboard/scan-terminal/TrainingDashboard.tsx

@@ -40,6 +40,19 @@ type DebHistoricalSummary = {
   hits?: number;
 };
 
+type DebUsableRecentSummary = {
+  window?: "recent_7d" | "recent_14d" | string;
+  city_count?: number;
+  samples?: number;
+  hits?: number;
+  hit_rate?: number | null;
+  avg_mae?: number | null;
+  recommendations?: {
+    primary?: number;
+    supporting?: number;
+  };
+};
+
 type DebVersionSummary = {
   version?: string;
   samples?: number;
@@ -51,6 +64,7 @@ type DebVersionSummary = {
 
 type DebSummaryPayload = {
   historical?: DebHistoricalSummary;
+  usable_recent?: DebUsableRecentSummary;
   recent_7d?: DebWindowSummary;
   recent_14d?: DebWindowSummary;
   versions?: Record<string, DebVersionSummary>;
@@ -178,6 +192,7 @@ export function TrainingDashboard({ isEn }: { isEn: boolean }) {
       cities: debSummary?.historical?.city_count ?? debSorted.length,
       sampleDays: debSummary?.historical?.sample_days,
       weightedHit: debSummary?.historical?.weighted_hit_rate,
+      usableRecent: debSummary?.usable_recent,
     };
   }, [debSorted, debSummary]);
 
@@ -218,6 +233,10 @@ export function TrainingDashboard({ isEn }: { isEn: boolean }) {
 
   const formatPct = (value: number | null | undefined) => value == null ? "--" : `${value.toFixed(1)}%`;
   const formatMaybeDeg = (value: number | null | undefined, digits = 1) => value == null ? "--" : `${value.toFixed(digits)}°`;
+  const usableWindowLabel = (window?: string) => {
+    if (window === "recent_14d") return isEn ? "Usable 14d" : "可用近14天";
+    return isEn ? "Usable 7d" : "可用近7天";
+  };
 
   return (
     <div className="h-full overflow-auto bg-[#f5f7fa]">
@@ -242,7 +261,7 @@ export function TrainingDashboard({ isEn }: { isEn: boolean }) {
             <div className="grid grid-cols-2 gap-2 mb-3 md:grid-cols-3 xl:grid-cols-6">
               {[
                 { icon: Hash, label: isEn ? "Cities" : "城市数", value: debStats.cities, tone: "blue" },
-                { icon: Target, label: isEn ? "Historical Avg" : "历史平均", value: `${debStats.avgHit.toFixed(1)}%`, tone: "emerald" },
+                { icon: Target, label: usableWindowLabel(debStats.usableRecent?.window), value: formatPct(debStats.usableRecent?.hit_rate), tone: "emerald" },
                 { icon: TrendingUp, label: isEn ? "Recent 7d" : "近7天", value: formatPct(debSummary?.recent_7d?.hit_rate), tone: "emerald" },
                 { icon: TrendingUp, label: isEn ? "Recent 14d" : "近14天", value: formatPct(debSummary?.recent_14d?.hit_rate), tone: "purple" },
                 { icon: Thermometer, label: isEn ? "Avg Error" : "平均误差", value: `${debStats.avgMae.toFixed(1)}°`, tone: "amber" },

frontend/components/ops/training/TrainingPageClient.tsx

@@ -89,6 +89,18 @@ interface DebSummary {
     sample_days?: number;
     city_count?: number;
   };
+  usable_recent?: {
+    window?: string;
+    city_count?: number;
+    samples?: number;
+    hits?: number;
+    hit_rate?: number | null;
+    avg_mae?: number | null;
+    recommendations?: {
+      primary?: number;
+      supporting?: number;
+    };
+  };
   recent_7d?: {
     hit_rate?: number | null;
     mae?: number | null;
@@ -160,6 +172,7 @@ export function TrainingPageClient() {
     return {
       avgHit: debSummary?.historical?.avg_hit_rate ?? avgHit,
       avgMae: debSummary?.historical?.avg_mae ?? avgMae,
+      usableRecent: debSummary?.usable_recent,
       recent7Hit: debSummary?.recent_7d?.hit_rate,
       recent14Hit: debSummary?.recent_14d?.hit_rate,
       best,
@@ -167,6 +180,9 @@ export function TrainingPageClient() {
     };
   }, [accuracy, debSummary]);
 
+  const usableWindowLabel = (window?: string) =>
+    window === "recent_14d" ? "DEB 可用近 14 天命中" : "DEB 可用近 7 天命中";
+
   const debChartData = useMemo(() => {
     if (!accuracy?.length) return [];
     return accuracy
@@ -247,7 +263,9 @@ export function TrainingPageClient() {
         <div className="grid grid-cols-2 md:grid-cols-6 gap-4">
           <KpiCard
             icon={Target} color="bg-cyan-500/20 text-cyan-400"
-            label="DEB 历史平均命中" value={`${kpis.avgHit.toFixed(1)}%`}
+            label={usableWindowLabel(kpis.usableRecent?.window)}
+            value={kpis.usableRecent?.hit_rate == null ? "—" : `${kpis.usableRecent.hit_rate.toFixed(1)}%`}
+            sub={`可用城市 ${kpis.usableRecent?.city_count ?? 0} · 样本 ${kpis.usableRecent?.samples ?? 0} · 历史 ${kpis.avgHit.toFixed(1)}%`}
           />
           <KpiCard
             icon={Target} color="bg-emerald-500/20 text-emerald-400"

frontend/lib/ops-api.ts

@@ -202,6 +202,18 @@ export const opsApi = {
           sample_days?: number;
           hits?: number;
         };
+        usable_recent?: {
+          window?: string;
+          city_count?: number;
+          samples?: number;
+          hits?: number;
+          hit_rate?: number | null;
+          avg_mae?: number | null;
+          recommendations?: {
+            primary?: number;
+            supporting?: number;
+          };
+        };
         recent_7d?: {
           start_date?: string | null;
           end_date?: string | null;

tests/test_ops_training_accuracy.py

@@ -90,6 +90,47 @@ def test_training_accuracy_payload_includes_city_deb_trust_strategy():
     assert rows["gamma"]["deb_recent"]["recommendation"] == "insufficient"
 
 
+def test_training_accuracy_payload_summarizes_usable_recent_deb_cities():
+    history = {
+        "alpha": {
+            "2026-06-01": {"actual_high": 20.0, "deb_prediction": 20.1},
+            "2026-06-02": {"actual_high": 21.0, "deb_prediction": 21.1},
+            "2026-06-03": {"actual_high": 22.0, "deb_prediction": 22.1},
+            "2026-06-04": {"actual_high": 23.0, "deb_prediction": 23.1},
+        },
+        "beta": {
+            "2026-06-01": {"actual_high": 30.0, "deb_prediction": 28.0},
+            "2026-06-02": {"actual_high": 31.0, "deb_prediction": 29.0},
+            "2026-06-03": {"actual_high": 32.0, "deb_prediction": 30.0},
+            "2026-06-04": {"actual_high": 33.0, "deb_prediction": 31.0},
+        },
+        "gamma": {
+            "2026-06-04": {"actual_high": 25.0, "deb_prediction": 25.1},
+        },
+    }
+    registry = {
+        "alpha": {"name": "Alpha"},
+        "beta": {"name": "Beta"},
+        "gamma": {"name": "Gamma"},
+    }
+
+    payload = _build_training_accuracy_payload(
+        history,
+        registry,
+        today_str="2026-06-07",
+    )
+
+    usable = payload["deb_summary"]["usable_recent"]
+
+    assert usable["window"] == "recent_7d"
+    assert usable["city_count"] == 1
+    assert usable["samples"] == 4
+    assert usable["hits"] == 4
+    assert usable["hit_rate"] == 100.0
+    assert usable["avg_mae"] == 0.1
+    assert usable["recommendations"] == {"primary": 1, "supporting": 0}
+
+
 def test_training_accuracy_payload_caps_version_backtest_samples(monkeypatch):
     start = date(2025, 1, 1)
     history = {

web/services/ops_api.py

@@ -2471,6 +2471,55 @@ def _build_deb_historical_summary(accuracy_data: List[Dict[str, Any]]) -> Dict[s
     }
 
 
+def _build_deb_usable_recent_summary(
+    accuracy_data: List[Dict[str, Any]],
+) -> Dict[str, Any]:
+    usable_rows = []
+    recommendations = {"primary": 0, "supporting": 0}
+    for row in accuracy_data:
+        recent = row.get("deb_recent") if isinstance(row.get("deb_recent"), dict) else None
+        if not recent:
+            continue
+        recommendation = str(recent.get("recommendation") or "").strip().lower()
+        if recommendation not in {"primary", "supporting"}:
+            continue
+        usable_rows.append(row)
+        recommendations[recommendation] += 1
+
+    def build_window(window_key: str) -> Dict[str, Any]:
+        samples = 0
+        hits = 0
+        weighted_mae = 0.0
+        city_count = 0
+        for row in usable_rows:
+            recent = row.get("deb_recent") if isinstance(row.get("deb_recent"), dict) else {}
+            metrics = recent.get(window_key) if isinstance(recent.get(window_key), dict) else {}
+            row_samples = int(metrics.get("samples") or 0)
+            if row_samples <= 0:
+                continue
+            row_hits = int(metrics.get("hits") or 0)
+            row_mae = _sf(metrics.get("mae"))
+            samples += row_samples
+            hits += row_hits
+            city_count += 1
+            if row_mae is not None:
+                weighted_mae += row_mae * row_samples
+        return {
+            "window": window_key,
+            "city_count": city_count,
+            "samples": samples,
+            "hits": hits,
+            "hit_rate": _round_metric((hits / samples * 100) if samples else None, 1),
+            "avg_mae": _round_metric((weighted_mae / samples) if samples else None, 2),
+            "recommendations": dict(recommendations),
+        }
+
+    recent_7d = build_window("recent_7d")
+    if int(recent_7d.get("samples") or 0) > 0:
+        return recent_7d
+    return build_window("recent_14d")
+
+
 def _flatten_training_history(history: Dict[str, Dict[str, Dict[str, Any]]], today_str: str) -> List[Dict[str, Any]]:
     rows: List[Dict[str, Any]] = []
     for city, city_rows in (history or {}).items():
@@ -2545,6 +2594,7 @@ def _build_training_accuracy_payload(
         "accuracy": accuracy_data,
         "deb_summary": {
             "historical": _build_deb_historical_summary(accuracy_data),
+            "usable_recent": _build_deb_usable_recent_summary(accuracy_data),
             "recent_7d": _evaluate_deb_records(
                 all_rows,
                 start_date=recent_7_start,