feat: implement ScanTerminalDashboard component and scan_terminal_service for real-time market opportunity monitoring

frontend/components/dashboard/ScanTerminalDashboard.tsx

@@ -64,6 +64,9 @@ type AiPinnedCity = {
 type AiCityForecastPayload = {
   status?: string | null;
   reason?: string | null;
+  reason_zh?: string | null;
+  reason_en?: string | null;
+  raw_reason?: string | null;
   model?: string | null;
   provider?: string | null;
   city_forecast?: {
@@ -584,11 +587,12 @@ function AiPinnedCityCard({
     item.cityName;
   const tempSymbol = detail?.temp_symbol || row?.temp_symbol || "°C";
   const modelView = detail ? getModelView(detail, detail.local_date) : null;
-  const modelValues = modelView
-    ? Object.values(modelView.models || {})
-        .map((value) => Number(value))
-        .filter((value) => Number.isFinite(value))
+  const modelEntries = modelView
+    ? Object.entries(modelView.models || {})
+        .map(([name, value]) => [name, Number(value)] as const)
+        .filter(([, value]) => Number.isFinite(value))
     : [];
+  const modelValues = modelEntries.map(([, value]) => value);
   const modelMin = modelValues.length ? Math.min(...modelValues) : null;
   const modelMax = modelValues.length ? Math.max(...modelValues) : null;
   const paceView = detail ? getTodayPaceView(detail, locale as "zh-CN" | "en-US") : null;
@@ -682,6 +686,21 @@ function AiPinnedCityCard({
     (isEn
       ? aiCityForecast?.model_cluster_note_en
       : aiCityForecast?.model_cluster_note_zh) || "";
+  const modelPreview = modelEntries
+    .slice(0, 4)
+    .map(([name, value]) => `${name} ${formatTemperatureValue(value, tempSymbol, { digits: 1 })}`)
+    .join(isEn ? " / " : " / ");
+  const localModelSupportNote = modelEntries.length
+    ? isEn
+      ? modelEntries.length <= 2
+        ? `Model support is sparse: only ${modelEntries.length} sources are available${modelPreview ? ` (${modelPreview})` : ""}, so the read should lean more on DEB path and METAR.`
+        : `Model support: ${modelEntries.length} sources cluster between ${modelRange}; ${modelPreview}.`
+      : modelEntries.length <= 2
+        ? `多模型支撑偏少：当前只有 ${modelEntries.length} 个模型${modelPreview ? `（${modelPreview}）` : ""}，需要更重视 DEB 路径和 METAR 实测。`
+        : `多模型支撑：${modelEntries.length} 个模型集中在 ${modelRange}，代表模型为 ${modelPreview}。`
+    : isEn
+      ? "Model support is unavailable, so this city must rely on DEB path and METAR observations."
+      : "暂无可用多模型支撑，需要主要参考 DEB 路径和 METAR 实测。";
   const localizedRisksRaw =
     (isEn ? aiCityForecast?.risks_en : aiCityForecast?.risks_zh) || [];
   const localizedRisks = Array.isArray(localizedRisksRaw)
@@ -692,9 +711,13 @@ function AiPinnedCityCard({
   const aiBullets = [
     localizedMetarRead,
     localizedReasoning !== localizedFinalJudgment ? localizedReasoning : "",
-    localizedModelNote,
+    localizedModelNote || localModelSupportNote,
     ...localizedRisks,
   ].filter((line) => String(line || "").trim());
+  const fallbackAiReason =
+    (isEn ? aiForecast.payload?.reason_en : aiForecast.payload?.reason_zh) ||
+    aiForecast.payload?.reason ||
+    "";
 
   const collapseId = `ai-city-body-${normalizeCityKey(item.cityName) || item.addedAt}`;
 
@@ -840,23 +863,47 @@ function AiPinnedCityCard({
                   </p>
                 </>
               ) : aiForecast.status === "ready" ? (
-                <p>
-                  {aiForecast.payload?.status === "timeout"
-                    ? isEn
-                      ? "Deepseek V4-Pro timed out. You can retry; city data and the right briefing were not refreshed."
-                      : "Deepseek V4-Pro 本次解读超时，可稍后重试；城市数据和右侧简报不会被刷新。"
-                    : aiForecast.payload?.reason ||
-                      (isEn
-                        ? "AI read is unavailable for this city right now."
-                        : "该城市暂时没有可用的 AI 解读。")}
-                </p>
+                <>
+                  <p>
+                    {aiForecast.payload?.status === "timeout"
+                      ? isEn
+                        ? "Deepseek V4-Pro timed out. You can retry; city data and the right briefing were not refreshed."
+                        : "Deepseek V4-Pro 本次解读超时，可稍后重试；城市数据和右侧简报不会被刷新。"
+                      : fallbackAiReason ||
+                        (isEn
+                          ? "AI read is unavailable for this city right now."
+                          : "该城市暂时没有可用的 AI 解读。")}
+                  </p>
+                  <ul className="scan-ai-weather-bullets">
+                    <li>{localModelSupportNote}</li>
+                    <li>
+                      {report
+                        ? `${isEn ? "Raw METAR" : "原始 METAR"}：${`${airportStation} ${report}`.trim()}`
+                        : isEn
+                          ? "Raw METAR is unavailable."
+                          : "暂无原始 METAR。"}
+                    </li>
+                  </ul>
+                </>
               ) : aiForecast.status === "failed" ? (
-                <p>
-                  {isEn
-                    ? "AI read failed. The raw METAR remains below as fallback context."
-                    : "AI 解读失败。下方仅保留原始 METAR 作为兜底上下文。"}
-                  {aiForecast.error ? ` ${aiForecast.error}` : ""}
-                </p>
+                <>
+                  <p>
+                    {isEn
+                      ? "AI read failed. Model support and the raw METAR remain as fallback context."
+                      : "AI 解读失败。下方保留多模型支撑和原始 METAR 作为兜底上下文。"}
+                    {aiForecast.error ? ` ${aiForecast.error}` : ""}
+                  </p>
+                  <ul className="scan-ai-weather-bullets">
+                    <li>{localModelSupportNote}</li>
+                    <li>
+                      {report
+                        ? `${isEn ? "Raw METAR" : "原始 METAR"}：${`${airportStation} ${report}`.trim()}`
+                        : isEn
+                          ? "Raw METAR is unavailable."
+                          : "暂无原始 METAR。"}
+                    </li>
+                  </ul>
+                </>
               ) : (
                 <p>
                   {isEn

web/scan_terminal_service.py

@@ -933,7 +933,9 @@ def _call_deepseek_city_ai(ai_input: Dict[str, Any], *, locale: str = "zh-CN") -
             "reasoning_zh, reasoning_en, risks_zh, risks_en, model_cluster_note_zh, model_cluster_note_en. "
             f"Primary output language is {primary_language}; the UI will read fields ending with {primary_suffix}. "
             "Keep final_judgment one short decision sentence. metar_read should explain the latest airport bulletin "
-            "and how wind/cloud/visibility/dewpoint may affect the temperature path. Keep the whole JSON compact."
+            "and how wind/cloud/visibility/dewpoint may affect the temperature path. model_cluster_note must state "
+            "how many model sources are available, whether they support DEB, and whether the sample is too sparse. "
+            "Keep the whole JSON compact."
         ),
         "city_snapshot": ai_input,
     }
@@ -965,22 +967,69 @@ def _call_deepseek_city_ai(ai_input: Dict[str, Any], *, locale: str = "zh-CN") -
         request_json.get("max_tokens"),
         SCAN_CITY_AI_TIMEOUT_SEC,
     )
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json",
+    }
     with httpx.Client(timeout=timeout) as client:
         response = client.post(
             f"{SCAN_AI_BASE_URL}/chat/completions",
-            headers={
-                "Authorization": f"Bearer {api_key}",
-                "Content-Type": "application/json",
-            },
+            headers=headers,
             json=request_json,
         )
         response.raise_for_status()
         data = response.json()
-    content = (
-        ((data.get("choices") or [{}])[0].get("message") or {}).get("content")
-        if isinstance(data, dict)
-        else None
-    )
+        content = (
+            ((data.get("choices") or [{}])[0].get("message") or {}).get("content")
+            if isinstance(data, dict)
+            else None
+        )
+        if not str(content or "").strip():
+            logger.warning(
+                "scan city AI provider returned empty content city={} locale={} finish_reason={} retrying_without_json_mode=true",
+                ai_input.get("city"),
+                normalized_locale,
+                ((data.get("choices") or [{}])[0] or {}).get("finish_reason") if isinstance(data, dict) else None,
+            )
+            retry_payload = dict(request_json)
+            retry_payload.pop("response_format", None)
+            retry_payload["temperature"] = 0.1
+            retry_payload["messages"] = [
+                {
+                    "role": "system",
+                    "content": (
+                        system_prompt
+                        + " 这次重试必须返回一个紧凑 JSON object，不要解释，不要空回复。"
+                        + " If you cannot infer a field, still return the field with a cautious sentence."
+                    ),
+                },
+                {
+                    "role": "user",
+                    "content": json.dumps(
+                        {
+                            **user_payload,
+                            "retry_reason": "previous provider response had empty message.content",
+                            "task": (
+                                user_payload["task"]
+                                + " The previous response had empty content. Return only one compact JSON object now."
+                            ),
+                        },
+                        ensure_ascii=False,
+                    ),
+                },
+            ]
+            response = client.post(
+                f"{SCAN_AI_BASE_URL}/chat/completions",
+                headers=headers,
+                json=retry_payload,
+            )
+            response.raise_for_status()
+            data = response.json()
+            content = (
+                ((data.get("choices") or [{}])[0].get("message") or {}).get("content")
+                if isinstance(data, dict)
+                else None
+            )
     parsed = _extract_ai_json_object(str(content or ""))
     if isinstance(data, dict):
         parsed["_polyweather_meta"] = {
@@ -1118,12 +1167,24 @@ def build_scan_city_ai_forecast_payload(
         }
     except Exception as exc:
         duration_ms = int((time.time() - started_at) * 1000)
+        raw_reason = str(exc)
+        empty_ai_content = raw_reason.strip().lower() == "empty ai content"
+        reason_en = (
+            "DeepSeek V4-Pro returned no usable text. Retry the city analysis."
+            if empty_ai_content
+            else raw_reason
+        )
+        reason_zh = (
+            "DeepSeek V4-Pro 没有返回有效正文，请刷新重试。"
+            if empty_ai_content
+            else raw_reason
+        )
         logger.warning(
             "scan city AI forecast failed city={} duration_ms={} model={} error={}",
             data.get("name") or city_name,
             duration_ms,
             SCAN_AI_MODEL,
-            exc,
+            raw_reason,
         )
         return {
             "status": "failed",
@@ -1132,7 +1193,10 @@ def build_scan_city_ai_forecast_payload(
             "city": data.get("name") or city_name,
             "city_display_name": data.get("display_name") or city_name,
             "duration_ms": duration_ms,
-            "reason": str(exc),
+            "reason": reason_en if normalized_locale == "en-US" else reason_zh,
+            "reason_en": reason_en,
+            "reason_zh": reason_zh,
+            "raw_reason": raw_reason,
         }
     generated_at = datetime.utcnow().isoformat() + "Z"
     with _SCAN_CITY_AI_CACHE_LOCK: