Upgrade V4 scan to city analysis

frontend/components/dashboard/Dashboard.module.css

@@ -10399,6 +10399,192 @@
     radial-gradient(circle at 0 0, rgba(34, 211, 238, 0.12), transparent 34%);
 }
 
+.root :global(.scan-ai-analysis-view) {
+  display: flex;
+  flex-direction: column;
+  gap: 14px;
+  min-height: 520px;
+}
+
+.root :global(.scan-ai-analysis-view.empty) {
+  min-height: 620px;
+}
+
+.root :global(.scan-ai-summary-card) {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 16px;
+  padding: 16px 18px;
+  border: 1px solid rgba(34, 211, 238, 0.22);
+  border-radius: 18px;
+  background:
+    linear-gradient(135deg, rgba(8, 32, 52, 0.94), rgba(8, 21, 38, 0.94)),
+    radial-gradient(circle at 0 0, rgba(45, 212, 191, 0.15), transparent 32%);
+}
+
+.root :global(.scan-ai-summary-card strong) {
+  color: #ecf8ff;
+  font-size: 16px;
+  font-weight: 900;
+}
+
+.root :global(.scan-ai-summary-card p) {
+  margin: 6px 0 0;
+  color: #a6bbd7;
+  font-size: 13px;
+  font-weight: 700;
+  line-height: 1.55;
+}
+
+.root :global(.scan-ai-inline-button) {
+  min-height: 42px;
+  display: inline-flex;
+  align-items: center;
+  gap: 8px;
+  padding: 0 16px;
+  border: 1px solid rgba(45, 212, 191, 0.3);
+  border-radius: 12px;
+  color: #d9fff6;
+  background: linear-gradient(135deg, rgba(13, 148, 136, 0.36), rgba(8, 47, 73, 0.4));
+  font-size: 13px;
+  font-weight: 900;
+  cursor: pointer;
+}
+
+.root :global(.scan-ai-inline-button:disabled) {
+  cursor: not-allowed;
+  opacity: 0.48;
+}
+
+.root :global(.scan-ai-log-panel.in-tab) {
+  margin: 0;
+}
+
+.root :global(.scan-ai-city-list) {
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+}
+
+.root :global(.scan-ai-city-card) {
+  border: 1px solid rgba(68, 100, 150, 0.2);
+  border-radius: 18px;
+  overflow: hidden;
+  background: rgba(10, 24, 42, 0.82);
+}
+
+.root :global(.scan-ai-city-head) {
+  display: flex;
+  align-items: flex-start;
+  justify-content: space-between;
+  gap: 14px;
+  padding: 16px 18px;
+  background: rgba(15, 34, 57, 0.9);
+}
+
+.root :global(.scan-ai-city-head strong) {
+  color: #f2f8ff;
+  font-size: 17px;
+  font-weight: 900;
+}
+
+.root :global(.scan-ai-city-head p) {
+  margin: 6px 0 0;
+  color: #9fb4d2;
+  font-size: 13px;
+  font-weight: 700;
+  line-height: 1.45;
+}
+
+.root :global(.scan-ai-city-head span) {
+  flex: 0 0 auto;
+  padding: 5px 9px;
+  border-radius: 999px;
+  color: #68f3c8;
+  background: rgba(16, 185, 129, 0.12);
+  border: 1px solid rgba(16, 185, 129, 0.2);
+  font-size: 11px;
+  font-weight: 900;
+}
+
+.root :global(.scan-ai-cluster-note) {
+  margin: 0 18px 14px;
+  padding: 10px 12px;
+  border: 1px solid rgba(34, 211, 238, 0.18);
+  border-radius: 12px;
+  color: #b7cbe6;
+  background: rgba(6, 18, 32, 0.5);
+  font-size: 12px;
+  font-weight: 800;
+  line-height: 1.45;
+}
+
+.root :global(.scan-ai-contracts) {
+  display: flex;
+  flex-direction: column;
+  padding: 0 12px 12px;
+}
+
+.root :global(.scan-ai-contract) {
+  display: grid;
+  grid-template-columns: minmax(220px, 1fr) auto;
+  gap: 10px 16px;
+  align-items: center;
+  padding: 12px;
+  border-top: 1px solid rgba(68, 100, 150, 0.16);
+}
+
+.root :global(.scan-ai-contract b) {
+  display: block;
+  color: #edf7ff;
+  font-size: 13px;
+  font-weight: 900;
+}
+
+.root :global(.scan-ai-contract small) {
+  display: block;
+  margin-top: 3px;
+  color: #8fa8c7;
+  font-size: 11px;
+  font-weight: 800;
+}
+
+.root :global(.scan-ai-contract > span) {
+  justify-self: end;
+  padding: 5px 9px;
+  border-radius: 999px;
+  color: #a8bedc;
+  background: rgba(68, 100, 150, 0.16);
+  font-size: 11px;
+  font-weight: 900;
+}
+
+.root :global(.scan-ai-contract.approve > span) {
+  color: #23f0a8;
+  background: rgba(16, 185, 129, 0.14);
+}
+
+.root :global(.scan-ai-contract.veto > span) {
+  color: #ff8a8a;
+  background: rgba(248, 113, 113, 0.12);
+}
+
+.root :global(.scan-ai-contract.downgrade > span),
+.root :global(.scan-ai-contract.watchlist > span) {
+  color: #ffd166;
+  background: rgba(245, 158, 11, 0.12);
+}
+
+.root :global(.scan-ai-contract p) {
+  grid-column: 1 / -1;
+  margin: 0;
+  color: #9fb4d2;
+  font-size: 12px;
+  font-weight: 700;
+  line-height: 1.45;
+}
+
 .root :global(.scan-ai-log-head) {
   display: flex;
   align-items: center;
@@ -11144,6 +11330,45 @@
   border-color: rgba(220, 38, 38, 0.28);
 }
 
+.root :global(.scan-terminal.light .scan-ai-summary-card),
+.root :global(.scan-terminal.light .scan-ai-city-card) {
+  background: rgba(255, 255, 255, 0.92);
+  border-color: rgba(148, 163, 184, 0.28);
+}
+
+.root :global(.scan-terminal.light .scan-ai-summary-card strong),
+.root :global(.scan-terminal.light .scan-ai-city-head strong),
+.root :global(.scan-terminal.light .scan-ai-contract b) {
+  color: #0f172a;
+}
+
+.root :global(.scan-terminal.light .scan-ai-summary-card p),
+.root :global(.scan-terminal.light .scan-ai-city-head p),
+.root :global(.scan-terminal.light .scan-ai-contract p),
+.root :global(.scan-terminal.light .scan-ai-contract small) {
+  color: #475569;
+}
+
+.root :global(.scan-terminal.light .scan-ai-city-head) {
+  background: #f8fafc;
+}
+
+.root :global(.scan-terminal.light .scan-ai-cluster-note) {
+  color: #334155;
+  background: #f8fafc;
+  border-color: rgba(148, 163, 184, 0.26);
+}
+
+.root :global(.scan-terminal.light .scan-ai-contract) {
+  border-top-color: rgba(148, 163, 184, 0.2);
+}
+
+.root :global(.scan-terminal.light .scan-ai-inline-button) {
+  color: #064e3b;
+  background: #d1fae5;
+  border-color: rgba(16, 185, 129, 0.28);
+}
+
 .root :global(.scan-terminal.light .scan-cta-ghost),
 .root :global(.scan-terminal.light .scan-detail-analysis-button) {
   border-color: rgba(7, 160, 100, 0.24);

frontend/components/dashboard/OpportunityTable.tsx

@@ -156,6 +156,16 @@ function getAiMeta(row: ScanOpportunityRow, locale: string) {
         null,
     };
   }
+  if (decision === "watchlist") {
+    return {
+      label: locale === "en-US" ? "AI watch" : "AI 观察",
+      tone: "downgrade",
+      reason:
+        locale === "en-US"
+          ? row.ai_watchlist_reason_en || row.ai_watchlist_reason_zh
+          : row.ai_watchlist_reason_zh || row.ai_watchlist_reason_en,
+    };
+  }
   return null;
 }
 

frontend/components/dashboard/ScanTerminalDashboard.tsx

@@ -58,7 +58,7 @@ const SCAN_AUTO_REFRESH_MS = 5 * 60 * 1000;
 
 interface FilterState extends ScanTerminalFilters {}
 
-type ContentView = "list" | "map" | "calendar";
+type ContentView = "list" | "map" | "ai" | "calendar";
 type ThemeMode = "dark" | "light";
 type ScanAiLogTone = "info" | "success" | "warning" | "error";
 
@@ -166,6 +166,209 @@ function findRowForCity(rows: ScanOpportunityRow[], cityName?: string | null) {
   return rows.find((row) => rowMatchesCity(row, cityName || "")) || null;
 }
 
+function getRowAiReason(row: ScanOpportunityRow, locale: string) {
+  const isEn = locale === "en-US";
+  return (
+    (isEn ? row.ai_reason_en || row.ai_reason_zh : row.ai_reason_zh || row.ai_reason_en) ||
+    row.ai_model_cluster_note ||
+    (isEn
+      ? row.ai_watchlist_reason_en || row.ai_watchlist_reason_zh
+      : row.ai_watchlist_reason_zh || row.ai_watchlist_reason_en) ||
+    null
+  );
+}
+
+function getAiDecisionLabel(row: ScanOpportunityRow, locale: string) {
+  const decision = String(row.ai_decision || "neutral").toLowerCase();
+  if (decision === "veto") return locale === "en-US" ? "Excluded" : "排除";
+  if (decision === "downgrade") return locale === "en-US" ? "Downgraded" : "降级";
+  if (decision === "watchlist") return locale === "en-US" ? "Watch" : "观察";
+  if (row.ai_rank != null || decision === "approve") {
+    return locale === "en-US" ? `Pick ${row.ai_rank || ""}`.trim() : `推荐 ${row.ai_rank || ""}`.trim();
+  }
+  return locale === "en-US" ? "Unreviewed" : "未复核";
+}
+
+function ScanAiAnalysisView({
+  response,
+  rows,
+  logs,
+  loading,
+  error,
+  locale,
+  onRunAi,
+}: {
+  response: ScanTerminalResponse | null;
+  rows: ScanOpportunityRow[];
+  logs: ScanAiLogEntry[];
+  loading: boolean;
+  error?: string | null;
+  locale: string;
+  onRunAi: () => void;
+}) {
+  const isEn = locale === "en-US";
+  const aiScan = response?.ai_scan || null;
+  const theses = aiScan?.city_theses || [];
+  const thesisByCity = useMemo(
+    () => new Map(theses.map((item) => [normalizeCityKey(item.city), item])),
+    [theses],
+  );
+  const groups = useMemo(() => {
+    const map = new Map<string, ScanOpportunityRow[]>();
+    rows.forEach((row) => {
+      const key = normalizeCityKey(row.city || row.city_display_name || row.display_name);
+      if (!key) return;
+      const current = map.get(key) || [];
+      current.push(row);
+      map.set(key, current);
+    });
+    return Array.from(map.entries()).map(([key, items]) => ({
+      key,
+      rows: items,
+      city:
+        items[0]?.city_display_name ||
+        items[0]?.display_name ||
+        items[0]?.city ||
+        key,
+      thesis: thesisByCity.get(key),
+    }));
+  }, [rows, thesisByCity]);
+
+  if (!rows.length) {
+    return (
+      <div className="scan-ai-analysis-view empty">
+        <div className="scan-empty-state">
+          <div className="scan-empty-title">
+            {isEn ? "Run a rule scan first" : "先完成规则扫描"}
+          </div>
+          <div className="scan-empty-copy">
+            {isEn
+              ? "V4 needs the current city candidates before it can analyze them."
+              : "V4 需要先拿到当前城市候选，才能做城市级研判。"}
+          </div>
+        </div>
+      </div>
+    );
+  }
+
+  return (
+    <div className="scan-ai-analysis-view">
+      <section className="scan-ai-summary-card">
+        <div>
+          <strong>{isEn ? "V4 city analysis" : "V4 城市级分析"}</strong>
+          <p>
+            {aiScan?.status === "ready"
+              ? (isEn ? aiScan.summary_en || aiScan.summary_zh : aiScan.summary_zh || aiScan.summary_en) ||
+                (isEn ? "V4 has reviewed the grouped city snapshot." : "V4 已复核城市分组快照。")
+              : error ||
+                aiScan?.reason ||
+                (isEn
+                  ? "Click AI Deep Scan after a rule scan to generate city theses and contract decisions."
+                  : "规则扫描后点击 AI 深度扫描，生成每城 thesis 和合约推荐/排除理由。")}
+          </p>
+        </div>
+        <button
+          type="button"
+          className="scan-ai-inline-button"
+          onClick={onRunAi}
+          disabled={loading || !rows.length}
+        >
+          <Sparkles size={14} className={loading ? "spin" : undefined} />
+          {loading
+            ? isEn
+              ? "Analyzing..."
+              : "分析中..."
+            : isEn
+              ? "Run V4"
+              : "运行 V4"}
+        </button>
+      </section>
+
+      <section className="scan-ai-log-panel in-tab">
+        <div className="scan-ai-log-head">
+          <strong>{isEn ? "Run log" : "运行日志"}</strong>
+          <span>
+            {aiScan?.status === "ready"
+              ? isEn
+                ? `${aiScan.sent_cities ?? "--"} cities · ${aiScan.sent_contracts ?? aiScan.sent_rows ?? "--"} contracts`
+                : `${aiScan.sent_cities ?? "--"} 城 · ${aiScan.sent_contracts ?? aiScan.sent_rows ?? "--"} 合约`
+              : isEn
+                ? "No V4 result yet"
+                : "暂无 V4 结果"}
+          </span>
+        </div>
+        <div className="scan-ai-log-list">
+          {logs.length ? (
+            logs.map((entry) => (
+              <div key={entry.id} className={`scan-ai-log-item ${entry.tone}`}>
+                <span className="scan-ai-log-time">{entry.time}</span>
+                <div>
+                  <b>{entry.title}</b>
+                  {entry.detail ? <small>{entry.detail}</small> : null}
+                </div>
+              </div>
+            ))
+          ) : (
+            <div className="scan-ai-log-empty">
+              {isEn ? "No request has been sent." : "还没有发起请求。"}
+            </div>
+          )}
+        </div>
+      </section>
+
+      <div className="scan-ai-city-list">
+        {groups.map((group) => {
+          const cityThesis =
+            (isEn
+              ? group.thesis?.thesis_en || group.thesis?.summary_en || group.rows[0]?.ai_city_thesis_en
+              : group.thesis?.thesis_zh || group.thesis?.summary_zh || group.rows[0]?.ai_city_thesis_zh) ||
+            (isEn ? "Waiting for V4 city thesis." : "等待 V4 城市研判。");
+          const clusterNote =
+            group.thesis?.model_cluster_note || group.rows[0]?.ai_city_model_cluster_note || null;
+          return (
+            <section key={group.key} className="scan-ai-city-card">
+              <div className="scan-ai-city-head">
+                <div>
+                  <strong>{group.city}</strong>
+                  <p>{cityThesis}</p>
+                </div>
+                {group.thesis?.confidence || group.rows[0]?.ai_city_confidence ? (
+                  <span>{group.thesis?.confidence || group.rows[0]?.ai_city_confidence}</span>
+                ) : null}
+              </div>
+              {clusterNote ? (
+                <div className="scan-ai-cluster-note">{clusterNote}</div>
+              ) : null}
+              <div className="scan-ai-contracts">
+                {group.rows.map((row) => {
+                  const decision = String(row.ai_decision || "neutral").toLowerCase();
+                  const reason = getRowAiReason(row, locale);
+                  const tempSymbol = row.temp_symbol || row.target_unit || "°C";
+                  return (
+                    <div key={row.id} className={`scan-ai-contract ${decision}`}>
+                      <div>
+                        <b>{row.action || row.target_label || row.market_question || row.id}</b>
+                        <small>
+                          {row.deb_prediction != null
+                            ? `DEB ${formatTemperatureValue(row.deb_prediction, tempSymbol)} · `
+                            : ""}
+                          {row.edge_percent != null ? `edge ${Number(row.edge_percent).toFixed(1)}%` : "--"}
+                        </small>
+                      </div>
+                      <span>{getAiDecisionLabel(row, locale)}</span>
+                      {reason ? <p>{reason}</p> : null}
+                    </div>
+                  );
+                })}
+              </div>
+            </section>
+          );
+        })}
+      </div>
+    </div>
+  );
+}
+
 function CalendarView({
   rows,
   locale,
@@ -441,19 +644,20 @@ function ScanTerminalScreen() {
         tone: "info",
         title: isEn ? "V4 review queued" : "V4 复核已排队",
         detail: isEn
-          ? `snapshot ${snapshotId || "--"} · ${rowCount} rule candidates`
-          : `快照 ${snapshotId || "--"} · 规则候选 ${rowCount} 条`,
+          ? `snapshot ${snapshotId || "--"} · ${rowCount} rule candidates grouped by city`
+          : `快照 ${snapshotId || "--"} · ${rowCount} 条候选将按城市分组`,
       },
       {
         id: `send-${Date.now() + 1}`,
         time: formatLogTime(),
         tone: "info",
-        title: isEn ? "Sending compact candidate set" : "正在发送精简候选集",
+        title: isEn ? "Sending city-level context" : "正在发送城市级上下文",
         detail: isEn
-          ? "VPS will call DeepSeek; Vercel only proxies the request."
-          : "由 VPS 调用 DeepSeek，Vercel 只做短代理。",
+          ? "Each city includes EMOS distribution, model cluster and contracts."
+          : "每城包含 EMOS 分布、模型集群和候选合约。",
       },
     ]);
+    setActiveView("ai");
     setAiLoading(true);
     setAiError(null);
     try {
@@ -493,8 +697,8 @@ function ScanTerminalScreen() {
           detail:
             review?.status === "ready"
               ? isEn
-                ? `${review.model || "V4"} · ${formatDuration(review.duration_ms)} · sent ${review.sent_rows ?? "--"} rows · ${review.recommended_count ?? 0} picks / ${review.vetoed_count ?? 0} veto`
-                : `${review.model || "V4"} · ${formatDuration(review.duration_ms)} · 发送 ${review.sent_rows ?? "--"} 条 · 推荐 ${review.recommended_count ?? 0} / 排除 ${review.vetoed_count ?? 0}`
+                ? `${review.model || "V4"} · ${formatDuration(review.duration_ms)} · ${review.sent_cities ?? "--"} cities / ${review.sent_contracts ?? review.sent_rows ?? "--"} contracts · ${review.recommended_count ?? 0} picks / ${review.vetoed_count ?? 0} veto`
+                : `${review.model || "V4"} · ${formatDuration(review.duration_ms)} · ${review.sent_cities ?? "--"} 城 / ${review.sent_contracts ?? review.sent_rows ?? "--"} 合约 · 推荐 ${review.recommended_count ?? 0} / 排除 ${review.vetoed_count ?? 0}`
               : review?.reason || (isEn ? "No AI result returned." : "没有返回 AI 结果。"),
         },
         ...(review?.usage
@@ -608,7 +812,8 @@ function ScanTerminalScreen() {
     setMapSelectedCityName(cityName);
     const matchedRow = findRowForCity(timeSortedRows, cityName);
     setSelectedRowId(matchedRow?.id || null);
-  }, [timeSortedRows]);
+    void store.selectCity(cityName);
+  }, [store, timeSortedRows]);
 
   const handleSelectRow = useCallback((row: ScanOpportunityRow) => {
     setSelectedRowId(row.id);
@@ -658,6 +863,19 @@ function ScanTerminalScreen() {
         />
       );
     }
+    if (resolvedView === "ai") {
+      return (
+        <ScanAiAnalysisView
+          response={terminalData}
+          rows={timeSortedRows}
+          logs={aiLogs}
+          loading={aiLoading}
+          error={aiError}
+          locale={locale}
+          onRunAi={() => void runAiReview()}
+        />
+      );
+    }
     return (
       <>
         <OpportunityTable
@@ -837,6 +1055,20 @@ function ScanTerminalScreen() {
                 >
                   {isEn ? "Opportunity List" : "机会列表"}
                 </button>
+                <button
+                  type="button"
+                  className={resolvedView === "ai" ? "active" : ""}
+                  title={!isPro ? (isEn ? "Pro scan required" : "扫描需 Pro") : undefined}
+                  onClick={() => {
+                    if (!isPro) {
+                      openScanPaywall();
+                      return;
+                    }
+                    setActiveView("ai");
+                  }}
+                >
+                  {isEn ? "V4 Analysis" : "V4 分析"}
+                </button>
                 <button
                   type="button"
                   className={resolvedView === "calendar" ? "active" : ""}
@@ -870,43 +1102,6 @@ function ScanTerminalScreen() {
                 ) : null}
               </div>
             </div>
-            {isPro ? (
-              <div className="scan-ai-log-panel">
-                <div className="scan-ai-log-head">
-                  <strong>{isEn ? "V4 run log" : "V4 运行日志"}</strong>
-                  <span>
-                    {aiLoading
-                      ? isEn
-                        ? "Waiting for DeepSeek response"
-                        : "正在等待 DeepSeek 返回"
-                      : aiScan?.status
-                        ? `${aiScan.model || "V4"} · ${aiScan.status}`
-                        : isEn
-                          ? "No V4 request yet"
-                          : "还没有发起 V4 请求"}
-                  </span>
-                </div>
-                <div className="scan-ai-log-list">
-                  {aiLogs.length ? (
-                    aiLogs.map((entry) => (
-                      <div key={entry.id} className={`scan-ai-log-item ${entry.tone}`}>
-                        <span className="scan-ai-log-time">{entry.time}</span>
-                        <div>
-                          <b>{entry.title}</b>
-                          {entry.detail ? <small>{entry.detail}</small> : null}
-                        </div>
-                      </div>
-                    ))
-                  ) : (
-                    <div className="scan-ai-log-empty">
-                      {isEn
-                        ? "After a rule scan, click AI Deep Scan to see request and result logs here."
-                        : "规则扫描完成后点击 AI 深度扫描，这里会显示请求和返回日志。"}
-                    </div>
-                  )}
-                </div>
-              </div>
-            ) : null}
 
             {scanStatus === "failed" && !terminalData ? (
               <div className="scan-empty-state">

frontend/lib/dashboard-types.ts

@@ -401,6 +401,7 @@ export interface MarketScan {
   websocket?: Record<string, unknown>;
   distribution_bias?: DistributionBias | null;
   distribution_preview?: DistributionPreviewPoint[] | null;
+  distribution_full?: DistributionPreviewPoint[] | null;
   window_phase?: string | null;
   window_score?: number | null;
   primary_signal?: PrimarySignal | null;
@@ -513,6 +514,7 @@ export interface ScanOpportunityRow {
   cluster_model_count?: number | null;
   cluster_deb_reference?: number | null;
   cluster_median?: number | null;
+  model_cluster_sources?: Record<string, number | null> | null;
   window_phase?: string | null;
   window_score?: number | null;
   remaining_window_minutes?: number | null;
@@ -541,10 +543,35 @@ export interface ScanOpportunityRow {
   ai_reason_zh?: string | null;
   ai_reason_en?: string | null;
   ai_model_cluster_note?: string | null;
+  ai_city_thesis_zh?: string | null;
+  ai_city_thesis_en?: string | null;
+  ai_city_confidence?: string | null;
+  ai_city_model_cluster_note?: string | null;
+  ai_watchlist_reason_zh?: string | null;
+  ai_watchlist_reason_en?: string | null;
 }
 
 export interface PrimarySignal extends ScanOpportunityRow {}
 
+export interface ScanAiCityThesis {
+  city: string;
+  thesis_zh?: string | null;
+  thesis_en?: string | null;
+  summary_zh?: string | null;
+  summary_en?: string | null;
+  model_cluster_note?: string | null;
+  confidence?: string | null;
+  recommended_row_ids?: string[] | null;
+  vetoed_row_ids?: string[] | null;
+}
+
+export interface ScanAiWatchlistItem {
+  row_id: string;
+  reason?: string | null;
+  reason_zh?: string | null;
+  reason_en?: string | null;
+}
+
 export interface ScanAiReview {
   status?: "ready" | "disabled" | "missing_key" | "failed" | "no_rows" | "no_snapshot" | "snapshot_mismatch" | string;
   stage?: "completed" | "fallback" | string | null;
@@ -554,6 +581,8 @@ export interface ScanAiReview {
   snapshot_id?: string | null;
   input_rows?: number | null;
   sent_rows?: number | null;
+  sent_cities?: number | null;
+  sent_contracts?: number | null;
   duration_ms?: number | null;
   timeout_sec?: number | null;
   cache_ttl_sec?: number | null;
@@ -570,9 +599,12 @@ export interface ScanAiReview {
   reason?: string | null;
   summary_zh?: string | null;
   summary_en?: string | null;
+  city_theses?: ScanAiCityThesis[] | null;
+  watchlist?: ScanAiWatchlistItem[] | null;
   recommended_count?: number | null;
   vetoed_count?: number | null;
   downgraded_count?: number | null;
+  watchlist_count?: number | null;
 }
 
 export interface ScanTerminalResponse {

src/data_collection/polymarket_readonly.py

@@ -3359,5 +3359,6 @@ class PolymarketReadOnlyLayer:
             "window_phase": window_meta.get("phase"),
             "window_score": window_meta.get("score"),
             "distribution_preview": distribution_preview[:6],
+            "distribution_full": distribution_preview,
             "resolved_market_type": "maxtemp",
         }

web/scan_terminal_service.py

@@ -335,11 +335,6 @@ def _set_cached_scan_ai_result(snapshot_id: str, filters: Dict[str, Any], result
 def _compact_ai_candidate(row: Dict[str, Any]) -> Dict[str, Any]:
     return {
         "id": row.get("id"),
-        "city": row.get("city_display_name") or row.get("display_name") or row.get("city"),
-        "local_time": row.get("local_time"),
-        "current_temp": row.get("current_temp"),
-        "current_max_so_far": row.get("current_max_so_far"),
-        "deb_prediction": row.get("deb_prediction"),
         "action": row.get("action"),
         "side": row.get("side"),
         "target_label": row.get("target_label"),
@@ -357,35 +352,118 @@ def _compact_ai_candidate(row: Dict[str, Any]) -> Dict[str, Any]:
         "quote_age_ms": row.get("quote_age_ms"),
         "edge_percent": row.get("edge_percent"),
         "final_score": row.get("final_score"),
-        "window_phase": row.get("window_phase"),
-        "remaining_window_minutes": row.get("remaining_window_minutes"),
-        "distribution_preview": (row.get("distribution_preview") or [])[:6],
-        "peak_value": row.get("peak_value"),
-        "peak_probability": row.get("peak_probability"),
         "cluster_role": row.get("cluster_role"),
-        "cluster_core_low": row.get("cluster_core_low"),
-        "cluster_core_high": row.get("cluster_core_high"),
-        "cluster_median": row.get("cluster_median"),
-        "cluster_deb_reference": row.get("cluster_deb_reference"),
-        "cluster_model_count": row.get("cluster_model_count"),
         "trend_alignment": row.get("trend_alignment"),
         "tradable": row.get("tradable"),
         "accepting_orders": row.get("accepting_orders"),
     }
 
 
+def _normalize_ai_city_key(value: Any) -> str:
+    return str(value or "").strip().lower().replace(" ", "").replace("-", "").replace("_", "")
+
+
+def _compact_ai_distribution(row: Dict[str, Any]) -> List[Dict[str, Any]]:
+    raw_items = row.get("distribution_full") or row.get("distribution_preview") or []
+    if not isinstance(raw_items, list):
+        return []
+    out: List[Dict[str, Any]] = []
+    for item in raw_items:
+        if not isinstance(item, dict):
+            continue
+        out.append(
+            {
+                "label": item.get("label"),
+                "value": item.get("value"),
+                "unit": item.get("unit") or row.get("target_unit") or row.get("temp_symbol"),
+                "model_probability": item.get("model_probability"),
+                "market_probability": item.get("market_probability"),
+                "highlighted": item.get("highlighted"),
+            }
+        )
+    return out
+
+
+def _compact_ai_model_sources(row: Dict[str, Any]) -> List[Dict[str, Any]]:
+    raw_sources = row.get("model_cluster_sources")
+    if not isinstance(raw_sources, dict):
+        return []
+    sources: List[Dict[str, Any]] = []
+    for name, value in raw_sources.items():
+        if _safe_float(value) is None:
+            continue
+        sources.append({"model": str(name), "value": value})
+    return sources[:12]
+
+
+def _compact_ai_city_group(rows: List[Dict[str, Any]]) -> Dict[str, Any]:
+    first = rows[0]
+    distribution = _compact_ai_distribution(first)
+    peak = next((item for item in distribution if item.get("highlighted")), None)
+    if not peak and distribution:
+        peak = max(
+            distribution,
+            key=lambda item: _safe_float(item.get("model_probability")) or -1.0,
+        )
+    return {
+        "city": first.get("city"),
+        "city_display_name": first.get("city_display_name") or first.get("display_name") or first.get("city"),
+        "selected_date": first.get("selected_date") or first.get("local_date"),
+        "local_time": first.get("local_time"),
+        "temp_symbol": first.get("temp_symbol") or first.get("target_unit"),
+        "current_temp": first.get("current_temp"),
+        "current_max_so_far": first.get("current_max_so_far"),
+        "deb_prediction": first.get("deb_prediction"),
+        "window_phase": first.get("window_phase"),
+        "remaining_window_minutes": first.get("remaining_window_minutes"),
+        "emos_distribution": distribution,
+        "emos_peak": {
+            "label": (peak or {}).get("label"),
+            "value": (peak or {}).get("value"),
+            "probability": (peak or {}).get("model_probability"),
+        },
+        "model_cluster": {
+            "core_low": first.get("cluster_core_low"),
+            "core_high": first.get("cluster_core_high"),
+            "median": first.get("cluster_median"),
+            "deb_reference": first.get("cluster_deb_reference"),
+            "model_count": first.get("cluster_model_count"),
+            "sources": _compact_ai_model_sources(first),
+        },
+        "contracts": [_compact_ai_candidate(row) for row in rows],
+    }
+
+
 def _build_scan_ai_prompt(payload: Dict[str, Any]) -> Dict[str, Any]:
-    rows = [
-        _compact_ai_candidate(row)
+    raw_rows = [
+        row
         for row in (payload.get("rows") or [])[:SCAN_AI_MAX_ROWS]
         if isinstance(row, dict) and row.get("id")
     ]
+    grouped: Dict[str, List[Dict[str, Any]]] = {}
+    for row in raw_rows:
+        key = "|".join(
+            [
+                _normalize_ai_city_key(row.get("city") or row.get("city_display_name")),
+                str(row.get("selected_date") or row.get("local_date") or ""),
+            ]
+        )
+        grouped.setdefault(key, []).append(row)
+    cities = [_compact_ai_city_group(rows) for rows in grouped.values() if rows]
+    sent_contracts = sum(len(city.get("contracts") or []) for city in cities)
     return {
+        "schema_version": "city_group_v2",
         "snapshot_id": payload.get("snapshot_id"),
         "generated_at": payload.get("generated_at"),
         "summary": payload.get("summary") or {},
         "filters": payload.get("filters") or {},
-        "candidates": rows,
+        "city_count": len(cities),
+        "candidate_row_count": len(raw_rows),
+        "cities": cities,
+        "_polyweather_input_meta": {
+            "sent_cities": len(cities),
+            "sent_contracts": sent_contracts,
+        },
     }
 
 
@@ -395,19 +473,26 @@ def _call_deepseek_scan_ai(ai_input: Dict[str, Any]) -> Dict[str, Any]:
         raise RuntimeError("POLYWEATHER_DEEPSEEK_API_KEY is not configured")
 
     system_prompt = (
-        "你是 PolyWeather 的付费市场扫描员。你只能基于用户提供的 JSON 快照做判断，"
-        "不得编造城市、价格、概率、盘口或天气数据。你的任务是从候选 rows 中选择真正值得看的机会，"
-        "尤其要检查 DEB、EMOS 分布、模型集群、当前实测、峰值窗口和盘口价格是否一致。"
-        "若模型集群不支持某个 YES，不要机械推荐 YES；可以 veto 或改为偏向 NO 候选。"
-        "只能引用输入中的 row id。必须输出 JSON object，字段为 recommendations、vetoed、downgraded、summary_zh、summary_en。"
+        "你是 PolyWeather 的付费 V4-Flash 市场扫描员。你只能基于用户提供的 JSON 快照做判断，"
+        "不得编造城市、价格、概率、盘口或天气数据。输入已经按城市分组，每城包含 EMOS 分布、"
+        "DEB、模型集群、当前实测和候选合约。你的任务不是复述 edge，而是形成 city thesis，"
+        "再决定哪些合约值得推荐、哪些必须排除、哪些只降级观察。"
+        "重点规则：最高温市场一天只会落入一个桶；如果 DEB/模型集群/EMOS 主峰集中在更高温区，"
+        "低温 YES 即使有表面 edge 也通常应 veto，优先考虑相邻尾部 NO；若价格过期、盘口太宽或窗口不支持，也要降级。"
+        "只能引用输入中的 row id。必须输出 JSON object。"
     )
+    model_snapshot = dict(ai_input)
+    model_snapshot.pop("_polyweather_input_meta", None)
     user_payload = {
         "task": (
-            "Review these existing PolyWeather market candidates. "
-            "Return strict JSON only. recommendations items need row_id, rank, decision, confidence, "
-            "reason_zh, reason_en, model_cluster_note. vetoed/downgraded items need row_id and reason."
+            "Analyze each city first, then contract decisions. Return strict JSON only with: "
+            "summary_zh, summary_en, city_theses, recommendations, vetoed, downgraded, watchlist. "
+            "city_theses items need city, thesis_zh, thesis_en, model_cluster_note, confidence, "
+            "recommended_row_ids, vetoed_row_ids. recommendations items need row_id, rank, decision, "
+            "confidence, reason_zh, reason_en, model_cluster_note. vetoed/downgraded items need row_id, "
+            "reason_zh/reason_en. watchlist items are optional and need row_id plus reason."
         ),
-        "snapshot": ai_input,
+        "snapshot": model_snapshot,
     }
     with httpx.Client(timeout=float(SCAN_AI_TIMEOUT_SEC)) as client:
         response = client.post(
@@ -460,6 +545,20 @@ def _normalize_ai_items(raw_items: Any) -> List[Dict[str, Any]]:
     return out
 
 
+def _normalize_ai_city_theses(raw_items: Any) -> List[Dict[str, Any]]:
+    if not isinstance(raw_items, list):
+        return []
+    out: List[Dict[str, Any]] = []
+    for item in raw_items:
+        if not isinstance(item, dict):
+            continue
+        city = str(item.get("city") or item.get("city_name") or "").strip()
+        if not city:
+            continue
+        out.append({**item, "city": city})
+    return out
+
+
 def _merge_scan_ai_result(
     payload: Dict[str, Any],
     ai_raw: Dict[str, Any],
@@ -473,10 +572,30 @@ def _merge_scan_ai_result(
     recommendations = _normalize_ai_items(ai_raw.get("recommendations"))
     vetoed = _normalize_ai_items(ai_raw.get("vetoed"))
     downgraded = _normalize_ai_items(ai_raw.get("downgraded"))
+    watchlist = _normalize_ai_items(ai_raw.get("watchlist"))
+    city_theses = _normalize_ai_city_theses(ai_raw.get("city_theses"))
 
     veto_ids = {str(item.get("row_id")) for item in vetoed}
     downgrade_ids = {str(item.get("row_id")) for item in downgraded}
     recommended_ids: set[str] = set()
+    watchlist_ids = {str(item.get("row_id")) for item in watchlist}
+
+    thesis_by_city: Dict[str, Dict[str, Any]] = {}
+    for item in city_theses:
+        key = _normalize_ai_city_key(item.get("city"))
+        if key:
+            thesis_by_city[key] = item
+
+    for row in rows:
+        thesis = thesis_by_city.get(_normalize_ai_city_key(row.get("city"))) or thesis_by_city.get(
+            _normalize_ai_city_key(row.get("city_display_name"))
+        )
+        if not thesis:
+            continue
+        row["ai_city_thesis_zh"] = thesis.get("thesis_zh") or thesis.get("summary_zh")
+        row["ai_city_thesis_en"] = thesis.get("thesis_en") or thesis.get("summary_en")
+        row["ai_city_confidence"] = thesis.get("confidence")
+        row["ai_city_model_cluster_note"] = thesis.get("model_cluster_note")
 
     for item in vetoed:
         row = by_id.get(str(item.get("row_id")))
@@ -492,6 +611,12 @@ def _merge_scan_ai_result(
         row["ai_decision"] = "downgrade"
         row["ai_reason_zh"] = item.get("reason_zh") or item.get("reason")
         row["ai_reason_en"] = item.get("reason_en")
+    for item in watchlist:
+        row = by_id.get(str(item.get("row_id")))
+        if not row:
+            continue
+        row["ai_watchlist_reason_zh"] = item.get("reason_zh") or item.get("reason")
+        row["ai_watchlist_reason_en"] = item.get("reason_en")
     for fallback_rank, item in enumerate(recommendations, start=1):
         row_id = str(item.get("row_id"))
         row = by_id.get(row_id)
@@ -511,6 +636,8 @@ def _merge_scan_ai_result(
         row_id = str(row.get("id"))
         if row_id not in recommended_ids and row_id not in veto_ids and row_id not in downgrade_ids:
             row["ai_decision"] = row.get("ai_decision") or "neutral"
+        if row_id in watchlist_ids and row.get("ai_decision") == "neutral":
+            row["ai_decision"] = "watchlist"
 
     def _ai_sort_key(row: Dict[str, Any]) -> tuple:
         decision = str(row.get("ai_decision") or "").lower()
@@ -534,6 +661,9 @@ def _merge_scan_ai_result(
         (row for row in rows if str(row.get("ai_decision") or "").lower() != "veto"),
         rows[0] if rows else None,
     )
+    input_meta = ai_raw.get("_polyweather_input_meta")
+    sent_cities = input_meta.get("sent_cities") if isinstance(input_meta, dict) else None
+    sent_contracts = input_meta.get("sent_contracts") if isinstance(input_meta, dict) else None
     ai_scan = {
         "status": "ready",
         "stage": "completed",
@@ -542,7 +672,9 @@ def _merge_scan_ai_result(
         "generated_at": datetime.utcnow().isoformat() + "Z",
         "snapshot_id": payload.get("snapshot_id"),
         "input_rows": input_rows if input_rows is not None else len(payload.get("rows") or []),
-        "sent_rows": min(len(payload.get("rows") or []), SCAN_AI_MAX_ROWS),
+        "sent_rows": sent_contracts if sent_contracts is not None else min(len(payload.get("rows") or []), SCAN_AI_MAX_ROWS),
+        "sent_cities": sent_cities,
+        "sent_contracts": sent_contracts,
         "duration_ms": duration_ms,
         "timeout_sec": SCAN_AI_TIMEOUT_SEC,
         "cache_ttl_sec": SCAN_AI_CACHE_TTL_SEC,
@@ -550,9 +682,12 @@ def _merge_scan_ai_result(
         "base_url": SCAN_AI_BASE_URL,
         "summary_zh": ai_raw.get("summary_zh"),
         "summary_en": ai_raw.get("summary_en"),
+        "city_theses": city_theses,
+        "watchlist": watchlist,
         "recommended_count": sum(1 for row in rows if row.get("ai_rank") is not None),
         "vetoed_count": sum(1 for row in rows if row.get("ai_decision") == "veto"),
         "downgraded_count": sum(1 for row in rows if row.get("ai_decision") == "downgrade"),
+        "watchlist_count": sum(1 for row in rows if row.get("ai_decision") == "watchlist"),
     }
     meta = ai_raw.get("_polyweather_meta")
     if isinstance(meta, dict):
@@ -676,6 +811,8 @@ def _build_terminal_row(
         "risk_level": ((data.get("risk") or {}).get("level") if isinstance(data.get("risk"), dict) else None),
         "distribution_bias": scan.get("distribution_bias"),
         "distribution_preview": scan.get("distribution_preview") or row.get("distribution_preview") or [],
+        "distribution_full": scan.get("distribution_full") or scan.get("distribution_preview") or row.get("distribution_preview") or [],
+        "model_cluster_sources": daily_entry.get("models") if isinstance(daily_entry.get("models"), dict) else data.get("multi_model"),
         "window_phase": row.get("window_phase") or scan.get("window_phase"),
         "window_score": row.get("window_score") if row.get("window_score") is not None else scan.get("window_score"),
         "signal_status": scan.get("signal_status"),
@@ -1015,15 +1152,19 @@ def build_scan_terminal_ai_payload(
 
     try:
         ai_input = _build_scan_ai_prompt(payload)
-        sent_rows = len(ai_input.get("candidates") or [])
+        input_meta = ai_input.get("_polyweather_input_meta") if isinstance(ai_input, dict) else {}
+        sent_rows = int((input_meta or {}).get("sent_contracts") or 0)
+        sent_cities = int((input_meta or {}).get("sent_cities") or 0)
         logger.info(
-            "scan terminal AI review start snapshot={} rows={} sent_rows={} model={}",
+            "scan terminal AI review start snapshot={} rows={} sent_cities={} sent_contracts={} model={}",
             current_snapshot_id,
             len(payload.get("rows") or []),
+            sent_cities,
             sent_rows,
             SCAN_AI_MODEL,
         )
         ai_raw = _call_deepseek_scan_ai(ai_input)
+        ai_raw["_polyweather_input_meta"] = input_meta
         _set_cached_scan_ai_result(current_snapshot_id, filters, ai_raw)
         duration_ms = int((time.time() - ai_started_at) * 1000)
         logger.info(