@

将 web/routes.py 拆分为模块化 router + service 架构

    - 新增 web/app_factory.py 集中注册 7 个域名 router
    - 新增 web/routers/ 薄壳路由层（auth/city/system/scan/ops/payments/analytics）
    - 新增 web/services/ 业务函数下沉（每域独立 service 文件）
    - web/routes.py 缩减为 city_runtime 的兼容重导出 facade
    - analysis_service.py/app.py 适配新入口并清理冗余导入

    Scope-risk: LOW — 全量 170 测试通过，router 注册顺序与原路由一致
    Tested: python -m pytest -q (170 passed), ruff check . (All checks passed)
@

web/services/city_payloads.py

@@ -0,0 +1,277 @@
+"""City payload builders for API-facing response shapes."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional
+
+from src.analysis.settlement_rounding import apply_city_settlement
+from web.core import _is_excluded_model_name, _market_layer, _sf
+
+TURKISH_MGM_CITIES = {"ankara", "istanbul"}
+
+
+def build_city_summary_payload(data: Dict[str, Any]) -> Dict[str, Any]:
+    return {
+        "name": data.get("name"),
+        "display_name": data.get("display_name"),
+        "icao": data.get("risk", {}).get("icao"),
+        "utc_offset_seconds": data.get("utc_offset_seconds"),
+        "local_time": data.get("local_time"),
+        "temp_symbol": data.get("temp_symbol"),
+        "current": {
+            "temp": data.get("current", {}).get("temp"),
+            "obs_time": data.get("current", {}).get("obs_time"),
+            "settlement_source": data.get("current", {}).get("settlement_source"),
+            "settlement_source_label": data.get("current", {}).get("settlement_source_label"),
+        },
+        "deb": {"prediction": data.get("deb", {}).get("prediction")},
+        "deviation_monitor": data.get("deviation_monitor") or {},
+        "risk": {
+            "level": data.get("risk", {}).get("level"),
+            "warning": data.get("risk", {}).get("warning"),
+        },
+        "updated_at": data.get("updated_at"),
+    }
+
+
+def build_city_market_scan_payload(
+    data: Dict[str, Any],
+    market_slug: Optional[str] = None,
+    target_date: Optional[str] = None,
+    lite: bool = False,
+    scan_filters: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    city = str(data.get("name") or "").strip().lower()
+    local_date = str(data.get("local_date") or "").strip()
+    requested_date = str(target_date or "").strip()
+    selected_date = requested_date or local_date
+
+    multi_model_daily = data.get("multi_model_daily") or {}
+    selected_daily = (
+        multi_model_daily.get(selected_date)
+        if isinstance(multi_model_daily, dict)
+        else None
+    )
+    if not isinstance(selected_daily, dict):
+        selected_daily = {}
+        selected_date = local_date
+
+    distribution = selected_daily.get("probabilities")
+    if not isinstance(distribution, list) or not distribution:
+        distribution = data.get("probabilities", {}).get("distribution", []) or []
+    distribution_all = selected_daily.get("probabilities_all")
+    if not isinstance(distribution_all, list) or not distribution_all:
+        distribution_all = data.get("probabilities", {}).get("distribution_all", []) or []
+    if not distribution_all:
+        distribution_all = distribution
+
+    model_map = selected_daily.get("models") or data.get("multi_model") or {}
+    if not isinstance(model_map, dict):
+        model_map = {}
+
+    anchor_temp = None
+    anchor_model = None
+    for model_name, raw_value in model_map.items():
+        value = _sf(raw_value)
+        if value is None:
+            continue
+        if anchor_temp is None or value > anchor_temp:
+            anchor_temp = value
+            anchor_model = str(model_name or "").strip() or None
+
+    anchor_temp_c = anchor_temp
+    temp_symbol = str(data.get("temp_symbol") or "")
+    if anchor_temp_c is not None and "F" in temp_symbol.upper():
+        anchor_temp_c = (anchor_temp_c - 32.0) * 5.0 / 9.0
+    anchor_settlement = apply_city_settlement(city, anchor_temp_c) if anchor_temp_c is not None else None
+
+    primary_bucket = None
+    if isinstance(distribution, list) and distribution:
+        ranked_buckets = []
+        temp_symbol_upper = str(temp_symbol or "").upper()
+        max_primary_bucket_delta = 16.0 if "F" in temp_symbol_upper else 8.0
+        for idx, row in enumerate(distribution_all):
+            if not isinstance(row, dict):
+                continue
+            bucket_value = _sf(
+                row.get("temp")
+                if row.get("temp") is not None
+                else row.get("value")
+                if row.get("value") is not None
+                else row.get("lower")
+            )
+            if (
+                anchor_temp is not None
+                and bucket_value is not None
+                and abs(float(bucket_value) - float(anchor_temp)) > max_primary_bucket_delta
+            ):
+                continue
+            bucket_prob = _sf(row.get("probability"))
+            prob_rank = bucket_prob if bucket_prob is not None else -1.0
+            ranked_buckets.append((-prob_rank, idx, row))
+        if ranked_buckets:
+            ranked_buckets.sort(key=lambda x: (x[0], x[1]))
+            primary_bucket = ranked_buckets[0][2]
+        elif anchor_temp is None:
+            primary_bucket = distribution[0]
+
+    model_probability = None
+    if isinstance(primary_bucket, dict) and primary_bucket.get("probability") is not None:
+        try:
+            raw_probability = float(primary_bucket.get("probability"))
+            model_probability = raw_probability / 100.0 if raw_probability > 1.0 else raw_probability
+        except Exception:
+            model_probability = None
+
+    fallback_sparkline = [
+        p.get("probability", 0)
+        for p in distribution_all[:8]
+        if isinstance(p, dict)
+    ]
+    current = data.get("current") or {}
+    selected_deb = selected_daily.get("deb") if isinstance(selected_daily.get("deb"), dict) else {}
+    current_deb = data.get("deb") if isinstance(data.get("deb"), dict) else {}
+    scan_context = {
+        "local_date": data.get("local_date"),
+        "local_time": data.get("local_time"),
+        "peak": data.get("peak") or {},
+        "current_max_so_far": current.get("max_so_far"),
+        "current_temp": current.get("temp"),
+        "trend": data.get("trend") or {},
+        "network_lead_signal": data.get("network_lead_signal") or {},
+        "models": model_map,
+        "deb_prediction": selected_deb.get("prediction") or current_deb.get("prediction"),
+    }
+    market_scan = _market_layer.build_market_scan(
+        city=data.get("name"),
+        target_date=selected_date or data.get("local_date"),
+        temperature_bucket=primary_bucket if isinstance(primary_bucket, dict) else None,
+        model_probability=model_probability,
+        probability_distribution=distribution_all,
+        temp_symbol=temp_symbol,
+        fallback_sparkline=fallback_sparkline,
+        forced_market_slug=market_slug,
+        include_related_buckets=not lite,
+        scan_filters=scan_filters,
+        scan_context=scan_context,
+    )
+    if isinstance(market_scan, dict):
+        market_scan["anchor_model"] = anchor_model
+        market_scan["anchor_high"] = anchor_temp
+        market_scan["anchor_settlement"] = anchor_settlement
+        market_scan["open_meteo_settlement"] = anchor_settlement
+        probabilities = data.get("probabilities") or {}
+        market_scan["probability_engine"] = str(
+            probabilities.get("engine") or "legacy"
+        ).strip() or "legacy"
+        market_scan["probability_calibration_mode"] = str(
+            probabilities.get("calibration_mode") or "legacy"
+        ).strip() or "legacy"
+    return {
+        "market_scan": market_scan,
+        "selected_date": selected_date or data.get("local_date"),
+        "fetched_at": data.get("updated_at"),
+    }
+
+
+def build_city_detail_payload(
+    data: Dict[str, Any],
+    market_slug: Optional[str] = None,
+    target_date: Optional[str] = None,
+) -> Dict[str, Any]:
+    market_payload = build_city_market_scan_payload(
+        data,
+        market_slug=market_slug,
+        target_date=target_date,
+    )
+    market_scan = market_payload.get("market_scan")
+    return {
+        "city": data.get("name"),
+        "fetched_at": data.get("updated_at"),
+        "overview": {
+            "name": data.get("name"),
+            "display_name": data.get("display_name"),
+            "icao": data.get("risk", {}).get("icao"),
+            "airport": data.get("risk", {}).get("airport"),
+            "lat": data.get("lat"),
+            "lon": data.get("lon"),
+            "local_time": data.get("local_time"),
+            "local_date": data.get("local_date"),
+            "temp_symbol": data.get("temp_symbol"),
+            "current_temp": data.get("current", {}).get("temp"),
+            "settlement_source": data.get("current", {}).get("settlement_source"),
+            "settlement_source_label": data.get("current", {}).get("settlement_source_label"),
+            "settlement_station": data.get("settlement_station") or {},
+            "deb_prediction": data.get("deb", {}).get("prediction"),
+            "risk_level": data.get("risk", {}).get("level"),
+            "risk_warning": data.get("risk", {}).get("warning"),
+            "updated_at": data.get("updated_at"),
+        },
+        "official": {
+            "available": bool(data.get("current", {}).get("temp") is not None),
+            "metar": {
+                "observation_time": data.get("airport_current", {}).get("obs_time"),
+                "obs_age_min": data.get("airport_current", {}).get("obs_age_min"),
+                "report_time": data.get("airport_current", {}).get("report_time"),
+                "receipt_time": data.get("airport_current", {}).get("receipt_time"),
+                "raw_metar": data.get("airport_current", {}).get("raw_metar"),
+                "current": data.get("airport_current") or {},
+            },
+            "taf": data.get("taf") or {},
+            "weather_gov": {},
+            "mgm": data.get("mgm") or {},
+            "mgm_nearby": data.get("mgm_nearby") or [],
+            "nearby_source": data.get("nearby_source") or ("mgm" if str(data.get("name") or "").lower() in TURKISH_MGM_CITIES else "metar_cluster"),
+            "airport_primary": data.get("airport_primary") or {},
+            "airport_primary_today_obs": data.get("airport_primary_today_obs") or [],
+            "official_nearby": data.get("official_nearby") or [],
+            "official_network_source": data.get("official_network_source"),
+            "official_network_status": data.get("official_network_status") or {},
+            "network_lead_signal": data.get("network_lead_signal") or {},
+            "network_spread_signal": data.get("network_spread_signal") or {},
+            "center_station_candidate": data.get("center_station_candidate"),
+            "airport_vs_network_delta": data.get("airport_vs_network_delta"),
+        },
+        "timeseries": {
+            "metar_recent_obs": data.get("metar_recent_obs") or [],
+            "metar_today_obs": data.get("metar_today_obs") or [],
+            "settlement_today_obs": data.get("settlement_today_obs") or [],
+            "hourly": data.get("hourly") or {},
+            "mgm_hourly": (data.get("mgm") or {}).get("hourly", []),
+            "forecast_daily": (data.get("forecast") or {}).get("daily", []),
+        },
+        "models": {
+            k: v
+            for k, v in (data.get("multi_model") or {}).items()
+            if not _is_excluded_model_name(k)
+        },
+        "deb": data.get("deb") or {},
+        "multi_model_daily": data.get("multi_model_daily") or {},
+        "probabilities": data.get("probabilities") or {"mu": None, "distribution": []},
+        "dynamic_commentary": data.get("dynamic_commentary") or {"summary": "", "notes": []},
+        "intraday_meteorology": data.get("intraday_meteorology")
+        or _build_intraday_meteorology(data),
+        "vertical_profile_signal": data.get("vertical_profile_signal") or {},
+        "taf": data.get("taf") or {},
+        "market_scan": market_scan,
+        "risk": data.get("risk"),
+        "settlement_station": data.get("settlement_station") or {},
+        "airport_primary": data.get("airport_primary") or {},
+        "official_nearby": data.get("official_nearby") or [],
+        "official_network_source": data.get("official_network_source"),
+        "official_network_status": data.get("official_network_status") or {},
+        "network_lead_signal": data.get("network_lead_signal") or {},
+        "network_spread_signal": data.get("network_spread_signal") or {},
+        "center_station_candidate": data.get("center_station_candidate"),
+        "airport_vs_network_delta": data.get("airport_vs_network_delta"),
+        "airport_current": data.get("airport_current") or {},
+        "nearby_source": data.get("nearby_source") or ("mgm" if str(data.get("name") or "").lower() in TURKISH_MGM_CITIES else "metar_cluster"),
+        "ai_analysis": data.get("ai_analysis") or "",
+        "errors": {},
+    }
+
+
+def _build_intraday_meteorology(data: Dict[str, Any]) -> Dict[str, Any]:
+    from web.analysis_service import _build_intraday_meteorology as build_intraday
+
+    return build_intraday(data)