Merge pull request 'release: 2026-03-24.3 (어구그룹 탐지 수정)' (#181) from develop into main

prediction/algorithms/polygon_builder.py

@@ -108,17 +108,16 @@ def build_group_polygon(
 
 
 def detect_gear_groups(
-    vessel_dfs: dict,
     vessel_store,
     now: Optional[datetime] = None,
 ) -> list[dict]:
     """어구 이름 패턴으로 어구그룹을 탐지한다.
 
     프론트엔드 FleetClusterLayer.tsx gearGroupMap useMemo 로직 이관.
+    전체 AIS 선박(vessel_store._tracks)에서 어구 패턴을 탐지한다.
 
     Args:
-        vessel_dfs: {mmsi: DataFrame} — 각 DataFrame은 lat, lon, sog, cog, timestamp 칼럼.
-        vessel_store: VesselStore — get_vessel_info(mmsi) → {name, ...}.
+        vessel_store: VesselStore — get_all_latest_positions() + get_vessel_info().
         now: 기준 시각 (None이면 UTC now).
 
     Returns:
@@ -127,30 +126,29 @@ def detect_gear_groups(
     if now is None:
         now = datetime.now(timezone.utc)
 
-    # 선박명 → mmsi 맵 (모선 탐색용)
+    # 전체 선박의 최신 위치 가져오기
+    all_positions = vessel_store.get_all_latest_positions()
+
+    # 선박명 → mmsi 맵 (모선 탐색용, 어구 패턴이 아닌 선박만)
     name_to_mmsi: dict[str, str] = {}
-    for mmsi, df in vessel_dfs.items():
-        if df is None or len(df) == 0:
-            continue
-        info = vessel_store.get_vessel_info(mmsi)
-        name: str = (info or {}).get('name', '') or ''
-        name = name.strip()
+    for mmsi, pos in all_positions.items():
+        name = (pos.get('name') or '').strip()
         if name and not GEAR_PATTERN.match(name):
             name_to_mmsi[name] = mmsi
 
     # 1단계: 같은 모선명 어구 수집 (60분 이내만)
     raw_groups: dict[str, list[dict]] = {}
-    for mmsi, df in vessel_dfs.items():
-        if df is None or len(df) == 0:
+    for mmsi, pos in all_positions.items():
+        name = (pos.get('name') or '').strip()
+        if not name:
             continue
-        last = df.iloc[-1]
-        ts = last.get('timestamp') if hasattr(last, 'get') else last['timestamp']
 
-        # timestamp → datetime 변환
+        # staleness 체크
+        ts = pos.get('timestamp')
+        if ts is not None:
             if isinstance(ts, datetime):
                 last_dt = ts if ts.tzinfo is not None else ts.replace(tzinfo=timezone.utc)
             else:
-            # pandas Timestamp 또는 숫자(unix seconds)
                 try:
                     import pandas as pd
                     last_dt = pd.Timestamp(ts).to_pydatetime()
@@ -158,15 +156,10 @@ def detect_gear_groups(
                         last_dt = last_dt.replace(tzinfo=timezone.utc)
                 except Exception:
                     continue
-
             age_sec = (now - last_dt).total_seconds()
             if age_sec > STALE_SEC:
                 continue
 
-        info = vessel_store.get_vessel_info(mmsi)
-        name = (info or {}).get('name', '') or ''
-        name = name.strip()
-
         m = GEAR_PATTERN.match(name)
         if not m:
             continue
@@ -175,10 +168,10 @@ def detect_gear_groups(
         entry = {
             'mmsi': mmsi,
             'name': name,
-            'lat': float(last['lat']),
-            'lon': float(last['lon']),
-            'sog': float(last.get('sog', 0) if hasattr(last, 'get') else last['sog']),
-            'cog': float(last.get('cog', 0) if hasattr(last, 'get') else last['cog']),
+            'lat': pos['lat'],
+            'lon': pos['lon'],
+            'sog': pos.get('sog', 0),
+            'cog': pos.get('cog', 0),
         }
         raw_groups.setdefault(parent_name, []).append(entry)
 
@@ -191,12 +184,10 @@ def detect_gear_groups(
         anchor_lat: Optional[float] = None
         anchor_lon: Optional[float] = None
 
-        if parent_mmsi and parent_mmsi in vessel_dfs:
-            parent_df = vessel_dfs[parent_mmsi]
-            if parent_df is not None and len(parent_df) > 0:
-                parent_last = parent_df.iloc[-1]
-                anchor_lat = float(parent_last['lat'])
-                anchor_lon = float(parent_last['lon'])
+        if parent_mmsi and parent_mmsi in all_positions:
+            parent_pos = all_positions[parent_mmsi]
+            anchor_lat = parent_pos['lat']
+            anchor_lon = parent_pos['lon']
 
         if anchor_lat is None and gears:
             anchor_lat = gears[0]['lat']
@@ -240,7 +231,6 @@ def detect_gear_groups(
 
 
 def build_all_group_snapshots(
-    vessel_dfs: dict,
     vessel_store,
     company_vessels: dict[int, list[str]],
     companies: dict[int, dict],
@@ -250,8 +240,7 @@ def build_all_group_snapshots(
     Shapely 미설치 시 빈 리스트를 반환한다.
 
     Args:
-        vessel_dfs: {mmsi: DataFrame}.
-        vessel_store: VesselStore — get_vessel_info(mmsi).
+        vessel_store: VesselStore — get_all_latest_positions() + get_vessel_info().
         company_vessels: {company_id: [mmsi_list]}.
         companies: {id: {name_cn, name_en}}.
 
@@ -264,6 +253,7 @@ def build_all_group_snapshots(
 
     now = datetime.now(timezone.utc)
     snapshots: list[dict] = []
+    all_positions = vessel_store.get_all_latest_positions()
 
     # ── FLEET 타입: company_vessels 순회 ──────────────────────────
     for company_id, mmsi_list in company_vessels.items():
@@ -275,20 +265,17 @@ def build_all_group_snapshots(
         members: list[dict] = []
 
         for mmsi in mmsi_list:
-            df = vessel_dfs.get(mmsi)
-            if df is None or len(df) == 0:
+            pos = all_positions.get(mmsi)
+            if not pos:
                 continue
-            last = df.iloc[-1]
-            lat = float(last['lat'])
-            lon = float(last['lon'])
-            sog_val = last.get('sog', 0) if hasattr(last, 'get') else last['sog']
-            cog_val = last.get('cog', 0) if hasattr(last, 'get') else last['cog']
-            sog = float(sog_val)
-            cog = float(cog_val)
+            lat = pos['lat']
+            lon = pos['lon']
+            sog = pos.get('sog', 0)
+            cog = pos.get('cog', 0)
             points.append((lon, lat))
             members.append({
                 'mmsi': mmsi,
-                'name': (vessel_store.get_vessel_info(mmsi) or {}).get('name', ''),
+                'name': pos.get('name', ''),
                 'lat': lat,
                 'lon': lon,
                 'sog': sog,
@@ -321,7 +308,7 @@ def build_all_group_snapshots(
         })
 
     # ── GEAR 타입: detect_gear_groups 결과 순회 ───────────────────
-    gear_groups = detect_gear_groups(vessel_dfs, vessel_store, now=now)
+    gear_groups = detect_gear_groups(vessel_store, now=now)
 
     for group in gear_groups:
         parent_name: str = group['parent_name']
@@ -332,12 +319,10 @@ def build_all_group_snapshots(
         anchor_lat: Optional[float] = None
         anchor_lon: Optional[float] = None
 
-        if parent_mmsi and parent_mmsi in vessel_dfs:
-            parent_df = vessel_dfs.get(parent_mmsi)
-            if parent_df is not None and len(parent_df) > 0:
-                p_last = parent_df.iloc[-1]
-                anchor_lat = float(p_last['lat'])
-                anchor_lon = float(p_last['lon'])
+        if parent_mmsi and parent_mmsi in all_positions:
+            parent_pos = all_positions[parent_mmsi]
+            anchor_lat = parent_pos['lat']
+            anchor_lon = parent_pos['lon']
 
         if anchor_lat is None and gear_members:
             anchor_lat = gear_members[0]['lat']
@@ -357,12 +342,9 @@ def build_all_group_snapshots(
 
         # 폴리곤 points: 어구 좌표 + 모선 좌표
         points = [(g['lon'], g['lat']) for g in gear_members]
-        if parent_mmsi and parent_mmsi in vessel_dfs:
-            parent_df = vessel_dfs.get(parent_mmsi)
-            if parent_df is not None and len(parent_df) > 0:
-                p_last = parent_df.iloc[-1]
-                p_lat = float(p_last['lat'])
-                p_lon = float(p_last['lon'])
+        if parent_mmsi and parent_mmsi in all_positions:
+            parent_pos = all_positions[parent_mmsi]
+            p_lon, p_lat = parent_pos['lon'], parent_pos['lat']
             if (p_lon, p_lat) not in points:
                 points.append((p_lon, p_lat))
 
@@ -373,19 +355,15 @@ def build_all_group_snapshots(
         # members JSONB 구성
         members_out: list[dict] = []
         # 모선 먼저
-        if parent_mmsi and parent_mmsi in vessel_dfs:
-            parent_df = vessel_dfs.get(parent_mmsi)
-            if parent_df is not None and len(parent_df) > 0:
-                p_last = parent_df.iloc[-1]
-                p_sog = float(p_last.get('sog', 0) if hasattr(p_last, 'get') else p_last['sog'])
-                p_cog = float(p_last.get('cog', 0) if hasattr(p_last, 'get') else p_last['cog'])
+        if parent_mmsi and parent_mmsi in all_positions:
+            parent_pos = all_positions[parent_mmsi]
             members_out.append({
                 'mmsi': parent_mmsi,
                 'name': parent_name,
-                    'lat': float(p_last['lat']),
-                    'lon': float(p_last['lon']),
-                    'sog': p_sog,
-                    'cog': p_cog,
+                'lat': parent_pos['lat'],
+                'lon': parent_pos['lon'],
+                'sog': parent_pos.get('sog', 0),
+                'cog': parent_pos.get('cog', 0),
                 'role': 'PARENT',
                 'isParent': True,
             })

prediction/cache/vessel_store.py

@@ -317,6 +317,24 @@ class VesselStore:
         """Return static vessel info dict for the given MMSI, or empty dict if not found."""
         return self._static_info.get(mmsi, {})
 
+    def get_all_latest_positions(self) -> dict[str, dict]:
+        """모든 선박의 최신 위치 반환. {mmsi: {lat, lon, sog, cog, timestamp, name}}"""
+        result: dict[str, dict] = {}
+        for mmsi, df in self._tracks.items():
+            if df is None or len(df) == 0:
+                continue
+            last = df.iloc[-1]
+            info = self._static_info.get(mmsi, {})
+            result[mmsi] = {
+                'lat': float(last['lat']),
+                'lon': float(last['lon']),
+                'sog': float(last.get('sog', 0) or 0),
+                'cog': float(last.get('cog', 0) or 0),
+                'timestamp': last.get('timestamp'),
+                'name': info.get('name', ''),
+            }
+        return result
+
     # ------------------------------------------------------------------
     # Properties
     # ------------------------------------------------------------------

prediction/scheduler.py

@@ -104,9 +104,9 @@ def run_analysis_cycle():
                 from algorithms.polygon_builder import detect_gear_groups, build_all_group_snapshots
 
                 company_vessels = fleet_tracker.get_company_vessels(vessel_dfs)
-                gear_groups = detect_gear_groups(vessel_dfs, vessel_store)
+                gear_groups = detect_gear_groups(vessel_store)
                 group_snapshots = build_all_group_snapshots(
-                    vessel_dfs, vessel_store, company_vessels,
+                    vessel_store, company_vessels,
                     fleet_tracker._companies,
                 )
                 saved = kcgdb.save_group_snapshots(group_snapshots)