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",
+  );
 }