Improve scan decision card hydration

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

@@ -38,6 +38,14 @@ function toFiniteDecisionNumber(value: unknown) {
   return Number.isFinite(numeric) ? numeric : null;
 }
 
+function getRowModelEntries(row: ScanOpportunityRow | null) {
+  const sources = row?.model_cluster_sources;
+  if (!sources || typeof sources !== "object") return [];
+  return Object.entries(sources)
+    .map(([name, value]) => [name, Number(value)] as const)
+    .filter(([, value]) => Number.isFinite(value));
+}
+
 function parseEpochMs(value: unknown) {
   if (value == null || value === "") return null;
   const numeric = Number(value);
@@ -228,11 +236,12 @@ export function AiPinnedCityCard({
     item.cityName;
   const tempSymbol = detail?.temp_symbol || row?.temp_symbol || "°C";
   const modelView = detail ? getModelView(detail, detail.local_date) : null;
-  const modelEntries = modelView
+  const detailModelEntries = modelView
     ? Object.entries(modelView.models || {})
         .map(([name, value]) => [name, Number(value)] as const)
         .filter(([, value]) => Number.isFinite(value))
     : [];
+  const modelEntries = detailModelEntries.length ? detailModelEntries : getRowModelEntries(row);
   const modelValues = modelEntries.map(([, value]) => value);
   const modelMin = modelValues.length ? Math.min(...modelValues) : null;
   const modelMax = modelValues.length ? Math.max(...modelValues) : null;
@@ -325,10 +334,24 @@ export function AiPinnedCityCard({
     : isEn
       ? `Model support is unavailable, so this city must rely on DEB path and ${observationSourceEn}.`
       : `暂无可用多模型支撑，需要主要参考 DEB 路径和${observationSourceZh}。`;
-  const aiPredictedMax = toFiniteDecisionNumber(aiCityForecast?.predicted_max);
-  const aiRangeLow = toFiniteDecisionNumber(aiCityForecast?.range_low);
-  const aiRangeHigh = toFiniteDecisionNumber(aiCityForecast?.range_high);
-  const aiConfidence = String(aiCityForecast?.confidence || "").trim() || null;
+  const rowAiPredictedMax =
+    toFiniteDecisionNumber(row?.ai_predicted_max) ??
+    toFiniteDecisionNumber(row?.ai_predicted_high) ??
+    toFiniteDecisionNumber(row?.cluster_median) ??
+    debNumber;
+  const aiPredictedMax =
+    toFiniteDecisionNumber(aiCityForecast?.predicted_max) ?? rowAiPredictedMax;
+  const aiRangeLow =
+    toFiniteDecisionNumber(aiCityForecast?.range_low) ??
+    toFiniteDecisionNumber(row?.ai_predicted_low) ??
+    modelMin;
+  const aiRangeHigh =
+    toFiniteDecisionNumber(aiCityForecast?.range_high) ??
+    toFiniteDecisionNumber(row?.ai_predicted_high) ??
+    modelMax;
+  const aiConfidence =
+    String(aiCityForecast?.confidence || row?.ai_forecast_confidence || "").trim() ||
+    (rowAiPredictedMax != null ? (isEn ? "fast" : "快速") : null);
   const decisionExpectedHighNumber = resolveExpectedHighCandidate({
     aiPredictedMax,
     currentTemp: currentTempNumber,

frontend/components/dashboard/scan-terminal/__tests__/aiCityForecastStreamState.test.ts

@@ -123,6 +123,20 @@ export function runTests() {
   assert.equal(errorState.payload?.status, "timeout_fallback");
   assert.match(errorState.payload?.reason_zh || "", /DeepSeek|DEB|METAR/);
 
+  const modelFallbackState = buildAiCityErrorForecastState({
+    cacheKey: `${cacheKey}:models`,
+    detail: cityDetail({
+      deb: { prediction: 29 },
+      multi_model: { ECMWF: 30, GFS: 32, ICON: 31 },
+    } as unknown as Partial<CityDetail>),
+    error: new Error("timeout"),
+    isEn: false,
+    report: "",
+  });
+  assert.equal(modelFallbackState.payload?.city_forecast?.predicted_max, 31);
+  assert.equal(modelFallbackState.payload?.city_forecast?.range_low, 30);
+  assert.equal(modelFallbackState.payload?.city_forecast?.range_high, 32);
+
   const hkoState = buildAiCityErrorForecastState({
     cacheKey: `${cacheKey}:hko`,
     detail: cityDetail({

frontend/components/dashboard/scan-terminal/__tests__/aiPinnedHydrationPerformance.test.ts

@@ -14,7 +14,17 @@ export function runTests() {
     "scan-terminal",
     "use-ai-pinned-city-workspace.ts",
   );
+  const storePath = path.join(projectRoot, "hooks", "useDashboardStore.tsx");
+  const cardPath = path.join(
+    projectRoot,
+    "components",
+    "dashboard",
+    "scan-terminal",
+    "AiPinnedCityCard.tsx",
+  );
   const source = fs.readFileSync(workspacePath, "utf8");
+  const storeSource = fs.readFileSync(storePath, "utf8");
+  const cardSource = fs.readFileSync(cardPath, "utf8");
 
   assert(
     !source.includes("waitForDeepAnalysisQueue"),
@@ -24,4 +34,17 @@ export function runTests() {
     /store\.ensureCityDetail\(\s*nextCity,\s*false,\s*"full",?\s*\)/.test(source),
     "automatic deep analysis hydration should use cache-friendly full detail requests",
   );
+  assert(
+    storeSource.includes("row.model_cluster_sources") &&
+      storeSource.includes("deb_prediction") &&
+      storeSource.includes("multi_model: multiModel"),
+    "decision-card preload must hydrate model cluster and DEB data from the scan row",
+  );
+  assert(
+    cardSource.includes("getRowModelEntries") &&
+      cardSource.includes("row?.ai_predicted_max") &&
+      cardSource.includes("row?.cluster_median") &&
+      cardSource.includes("detailModelEntries.length ? detailModelEntries : getRowModelEntries(row)"),
+    "decision card should render model support and AI predicted max from the row before full detail/AI stream arrives",
+  );
 }

frontend/hooks/useDashboardStore.tsx

@@ -810,9 +810,61 @@ export function DashboardStoreProvider({
     if (!cityName || findCachedCityDetail(cityDetailsByName, cityName)) return;
     // Pre-populate cache from scan terminal row so detail panel shows data immediately
     const now = Date.now();
+    const currentTemp = Number(row.current_temp ?? row.current_max_so_far);
+    const debPrediction = Number(row.deb_prediction);
+    const rawModelSources =
+      row.model_cluster_sources && typeof row.model_cluster_sources === "object"
+        ? (row.model_cluster_sources as Record<string, unknown>)
+        : {};
+    const multiModel = Object.fromEntries(
+      Object.entries(rawModelSources)
+        .map(([name, value]) => [name, Number(value)] as const)
+        .filter(([, value]) => Number.isFinite(value)),
+    );
     setCityDetailsByName((current) => ({
       ...current,
-      [cityName]: { display_name: cityName, local_date: "", local_time: "", deb: {}, probabilities: {}, multi_model: {}, } as CityDetail,
+      [cityName]: {
+        name: String(row.city || cityName),
+        display_name: String(row.city_display_name || row.display_name || cityName),
+        detail_depth: "panel",
+        lat: 0,
+        lon: 0,
+        local_date: String(row.local_date || row.selected_date || ""),
+        local_time: String(row.local_time || ""),
+        temp_symbol: String(row.temp_symbol || "°C"),
+        current: {
+          temp: Number.isFinite(currentTemp) ? currentTemp : null,
+          max_so_far: Number.isFinite(Number(row.current_max_so_far))
+            ? Number(row.current_max_so_far)
+            : Number.isFinite(currentTemp)
+              ? currentTemp
+              : null,
+          max_temp_time: null,
+          wu_settlement: null,
+          station_code: null,
+          station_name: String(row.airport || ""),
+          obs_time: String((row.metar_context as { last_time?: string } | null)?.last_time || ""),
+          obs_age_min: null,
+          wind_speed_kt: null,
+          wind_dir: null,
+          humidity: null,
+          cloud_desc: null,
+          clouds_raw: [],
+          visibility_mi: null,
+          wx_desc: null,
+        },
+        deb: {
+          prediction: Number.isFinite(debPrediction) ? debPrediction : null,
+        },
+        forecast: { today_high: null, daily: [] },
+        hourly: { times: [], temps: [] },
+        multi_model: multiModel,
+        probabilities: {},
+        risk: {
+          level: String(row.risk_level || "medium"),
+          airport: String(row.airport || ""),
+        },
+      } as CityDetail,
     }));
     setCityDetailMetaByName((current) => ({
       ...current,

src/utils/telegram_push.py

@@ -1083,7 +1083,7 @@ def _process_airport_city(
     last_city: dict,
     chat_ids: List[str],
     bot: Any,
-) -> tuple | None:
+) -> Optional[Tuple[str, dict]]:
     """Process one airport city and return (city, new_state_entry) or None.
 
     This is the per-city unit used by the concurrent thread pool in

tests/test_telegram_hashtags.py

@@ -125,8 +125,8 @@ def test_high_freq_airport_push_forces_analysis_refresh(monkeypatch):
 
     calls = []
 
-    def fake_analyze(city, force_refresh=False, **_kwargs):
-        calls.append((city, force_refresh))
+    def fake_analyze(city, force_refresh=False, force_refresh_observations_only=False, **_kwargs):
+        calls.append((city, force_refresh, force_refresh_observations_only))
         return {
             "local_time": "12:00",
             "current": {"temp": 31.0},
@@ -156,5 +156,5 @@ def test_high_freq_airport_push_forces_analysis_refresh(monkeypatch):
     )
 
     assert sent is True
-    assert calls == [("qingdao", True)]
+    assert calls == [("qingdao", False, True)]
     assert bot.messages