htlee
812a78f636
- gear_correlation.py: 적응형 EMA + freeze + shadow + 배치 최적화 - 5개 글로벌 모델 병렬 추적 (default/aggressive/conservative/proximity-heavy/visit-pattern) - 어구 중심 점수 체계: 어구 비활성 시 FREEZE, 선박 shadow 추적 - 유형별 메트릭: 어구-선박(proximity+visit+activity), 선박-선박(DTW+SOG+COG) - DB: correlation_param_models + raw_metrics(일별 파티션) + scores + system_config - partition_manager: 일별 파티션 생성/정리 (system_config hot-reload) - track_similarity: SOG상관 + COG동조 + 근접비 3개 메트릭 추가 - scheduler Step 4.7 통합, fleet_tracker MMSI 점수 이전 - chat/tools: query_gear_correlation 도구 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
352 lines
13 KiB
Python
352 lines
13 KiB
Python
"""등록 선단 기반 추적기."""
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import logging
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import re
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import time
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from datetime import datetime, timezone
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from typing import Optional
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import pandas as pd
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logger = logging.getLogger(__name__)
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# 어구 이름 패턴
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GEAR_PATTERN = re.compile(r'^(.+?)_(\d+)_(\d*)$')
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GEAR_PATTERN_PCT = re.compile(r'^(.+?)%$')
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_REGISTRY_CACHE_SEC = 3600
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class FleetTracker:
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def __init__(self) -> None:
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self._companies: dict[int, dict] = {} # id → {name_cn, name_en}
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self._vessels: dict[int, dict] = {} # id → {permit_no, name_cn, ...}
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self._name_cn_map: dict[str, int] = {} # name_cn → vessel_id
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self._name_en_map: dict[str, int] = {} # name_en(lowercase) → vessel_id
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self._mmsi_to_vid: dict[str, int] = {} # mmsi → vessel_id (매칭된 것만)
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self._gear_active: dict[str, dict] = {} # mmsi → {name, parent_mmsi, ...}
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self._last_registry_load: float = 0.0
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def load_registry(self, conn) -> None:
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"""DB에서 fleet_companies + fleet_vessels 로드. 1시간 캐시."""
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if time.time() - self._last_registry_load < _REGISTRY_CACHE_SEC:
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return
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cur = conn.cursor()
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cur.execute('SELECT id, name_cn, name_en FROM kcg.fleet_companies')
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self._companies = {r[0]: {'name_cn': r[1], 'name_en': r[2]} for r in cur.fetchall()}
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cur.execute(
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"""SELECT id, company_id, permit_no, name_cn, name_en, tonnage,
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gear_code, fleet_role, pair_vessel_id, mmsi
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FROM kcg.fleet_vessels"""
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)
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self._vessels = {}
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self._name_cn_map = {}
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self._name_en_map = {}
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self._mmsi_to_vid = {}
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for r in cur.fetchall():
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vid = r[0]
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v: dict = {
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'id': vid,
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'company_id': r[1],
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'permit_no': r[2],
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'name_cn': r[3],
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'name_en': r[4],
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'tonnage': r[5],
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'gear_code': r[6],
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'fleet_role': r[7],
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'pair_vessel_id': r[8],
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'mmsi': r[9],
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}
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self._vessels[vid] = v
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if r[3]:
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self._name_cn_map[r[3]] = vid
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if r[4]:
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self._name_en_map[r[4].lower().strip()] = vid
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if r[9]:
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self._mmsi_to_vid[r[9]] = vid
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cur.close()
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self._last_registry_load = time.time()
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logger.info(
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'fleet registry loaded: %d companies, %d vessels',
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len(self._companies),
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len(self._vessels),
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)
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def match_ais_to_registry(self, ais_vessels: list[dict], conn) -> None:
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"""AIS 선박을 등록 선단에 매칭. DB 업데이트.
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ais_vessels: [{mmsi, name, lat, lon, sog, cog}, ...]
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"""
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cur = conn.cursor()
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matched = 0
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for v in ais_vessels:
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mmsi = v.get('mmsi', '')
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name = v.get('name', '')
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if not mmsi or not name:
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continue
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# 이미 매칭됨 → last_seen_at 업데이트
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if mmsi in self._mmsi_to_vid:
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cur.execute(
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'UPDATE kcg.fleet_vessels SET last_seen_at = NOW() WHERE id = %s',
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(self._mmsi_to_vid[mmsi],),
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)
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continue
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# NAME_EXACT 매칭
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vid: Optional[int] = self._name_cn_map.get(name)
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if not vid:
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vid = self._name_en_map.get(name.lower().strip())
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if vid:
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cur.execute(
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"""UPDATE kcg.fleet_vessels
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SET mmsi = %s, match_confidence = 0.95, match_method = 'NAME_EXACT',
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last_seen_at = NOW(), updated_at = NOW()
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WHERE id = %s AND (mmsi IS NULL OR mmsi = %s)""",
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(mmsi, vid, mmsi),
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)
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self._mmsi_to_vid[mmsi] = vid
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matched += 1
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conn.commit()
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cur.close()
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if matched > 0:
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logger.info('AIS→registry matched: %d vessels', matched)
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def track_gear_identity(self, gear_signals: list[dict], conn) -> None:
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"""어구/어망 정체성 추적.
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gear_signals: [{mmsi, name, lat, lon}, ...] — 이름이 XXX_숫자_숫자 패턴인 AIS 신호
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"""
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cur = conn.cursor()
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now = datetime.now(timezone.utc)
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for g in gear_signals:
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mmsi = g['mmsi']
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name = g['name']
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lat = g.get('lat', 0)
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lon = g.get('lon', 0)
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# 모선명 + 인덱스 추출
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parent_name: Optional[str] = None
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idx1: Optional[int] = None
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idx2: Optional[int] = None
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m = GEAR_PATTERN.match(name)
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if m:
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parent_name = m.group(1).strip()
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idx1 = int(m.group(2))
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idx2 = int(m.group(3)) if m.group(3) else None
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else:
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m2 = GEAR_PATTERN_PCT.match(name)
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if m2:
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parent_name = m2.group(1).strip()
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# 모선 매칭
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parent_mmsi: Optional[str] = None
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parent_vid: Optional[int] = None
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if parent_name:
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vid = self._name_cn_map.get(parent_name)
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if not vid:
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vid = self._name_en_map.get(parent_name.lower())
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if vid:
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parent_vid = vid
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parent_mmsi = self._vessels[vid].get('mmsi')
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match_method: Optional[str] = 'NAME_PARENT' if parent_vid else None
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confidence = 0.9 if parent_vid else 0.0
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# 기존 활성 행 조회
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cur.execute(
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"""SELECT id, name FROM kcg.gear_identity_log
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WHERE mmsi = %s AND is_active = TRUE""",
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(mmsi,),
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)
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existing = cur.fetchone()
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if existing:
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if existing[1] == name:
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# 같은 MMSI + 같은 이름 → 위치/시간 업데이트
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cur.execute(
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"""UPDATE kcg.gear_identity_log
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SET last_seen_at = %s, lat = %s, lon = %s
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WHERE id = %s""",
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(now, lat, lon, existing[0]),
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)
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else:
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# 같은 MMSI + 다른 이름 → 이전 비활성화 + 새 행
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cur.execute(
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'UPDATE kcg.gear_identity_log SET is_active = FALSE WHERE id = %s',
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(existing[0],),
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)
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cur.execute(
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"""INSERT INTO kcg.gear_identity_log
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(mmsi, name, parent_name, parent_mmsi, parent_vessel_id,
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gear_index_1, gear_index_2, lat, lon,
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match_method, match_confidence, first_seen_at, last_seen_at)
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VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)""",
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(mmsi, name, parent_name, parent_mmsi, parent_vid,
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idx1, idx2, lat, lon,
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match_method, confidence, now, now),
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)
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else:
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# 새 MMSI → 같은 이름이 다른 MMSI로 있는지 확인
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cur.execute(
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"""SELECT id, mmsi FROM kcg.gear_identity_log
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WHERE name = %s AND is_active = TRUE AND mmsi != %s""",
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(name, mmsi),
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)
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old_mmsi_row = cur.fetchone()
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if old_mmsi_row:
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# 같은 이름 + 다른 MMSI → MMSI 변경
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cur.execute(
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'UPDATE kcg.gear_identity_log SET is_active = FALSE WHERE id = %s',
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(old_mmsi_row[0],),
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)
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logger.info('gear MMSI change: %s → %s (name=%s)', old_mmsi_row[1], mmsi, name)
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# 어피니티 점수 이전 (이전 MMSI → 새 MMSI)
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try:
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cur.execute(
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"UPDATE kcg.gear_correlation_scores "
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"SET target_mmsi = %s, updated_at = NOW() "
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"WHERE target_mmsi = %s",
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(mmsi, old_mmsi_row[1]),
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)
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if cur.rowcount > 0:
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logger.info(
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'transferred %d affinity scores: %s → %s',
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cur.rowcount, old_mmsi_row[1], mmsi,
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)
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except Exception as e:
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logger.warning('affinity score transfer failed: %s', e)
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cur.execute(
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"""INSERT INTO kcg.gear_identity_log
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(mmsi, name, parent_name, parent_mmsi, parent_vessel_id,
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gear_index_1, gear_index_2, lat, lon,
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match_method, match_confidence, first_seen_at, last_seen_at)
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VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)""",
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(mmsi, name, parent_name, parent_mmsi, parent_vid,
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idx1, idx2, lat, lon,
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match_method, confidence, now, now),
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)
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conn.commit()
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cur.close()
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def build_fleet_clusters(self, vessel_dfs: dict[str, pd.DataFrame]) -> dict[str, dict]:
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"""등록 선단 기준으로 cluster 정보 구성.
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Returns: {mmsi → {cluster_id, cluster_size, is_leader, fleet_role}}
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cluster_id = company_id (등록 선단 기준)
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"""
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results: dict[str, dict] = {}
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# 회사별로 현재 AIS 수신 중인 선박 그룹핑
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company_vessels: dict[int, list[str]] = {}
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for mmsi, vid in self._mmsi_to_vid.items():
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v = self._vessels.get(vid)
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if not v or mmsi not in vessel_dfs:
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continue
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cid = v['company_id']
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company_vessels.setdefault(cid, []).append(mmsi)
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for cid, mmsis in company_vessels.items():
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if len(mmsis) < 2:
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# 단독 선박 → NOISE
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for mmsi in mmsis:
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v = self._vessels.get(self._mmsi_to_vid.get(mmsi, -1), {})
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results[mmsi] = {
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'cluster_id': -1,
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'cluster_size': 1,
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'is_leader': False,
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'fleet_role': v.get('fleet_role', 'NOISE'),
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}
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continue
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# 2척 이상 → 등록 선단 클러스터
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for mmsi in mmsis:
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vid = self._mmsi_to_vid[mmsi]
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v = self._vessels[vid]
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results[mmsi] = {
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'cluster_id': cid,
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'cluster_size': len(mmsis),
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'is_leader': v['fleet_role'] == 'MAIN',
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'fleet_role': v['fleet_role'],
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}
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# 매칭 안 된 선박 → NOISE
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for mmsi in vessel_dfs:
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if mmsi not in results:
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results[mmsi] = {
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'cluster_id': -1,
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'cluster_size': 0,
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'is_leader': False,
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'fleet_role': 'NOISE',
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}
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return results
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def save_snapshot(self, vessel_dfs: dict[str, pd.DataFrame], conn) -> None:
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"""fleet_tracking_snapshot 저장."""
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now = datetime.now(timezone.utc)
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cur = conn.cursor()
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company_vessels: dict[int, list[str]] = {}
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for mmsi, vid in self._mmsi_to_vid.items():
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v = self._vessels.get(vid)
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if not v or mmsi not in vessel_dfs:
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continue
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company_vessels.setdefault(v['company_id'], []).append(mmsi)
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for cid, mmsis in company_vessels.items():
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active = len(mmsis)
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total = sum(1 for v in self._vessels.values() if v['company_id'] == cid)
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lats: list[float] = []
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lons: list[float] = []
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for mmsi in mmsis:
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df = vessel_dfs.get(mmsi)
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if df is not None and len(df) > 0:
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last = df.iloc[-1]
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lats.append(float(last['lat']))
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lons.append(float(last['lon']))
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center_lat = sum(lats) / len(lats) if lats else None
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center_lon = sum(lons) / len(lons) if lons else None
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cur.execute(
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"""INSERT INTO kcg.fleet_tracking_snapshot
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(company_id, snapshot_time, total_vessels, active_vessels,
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center_lat, center_lon)
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VALUES (%s, %s, %s, %s, %s, %s)""",
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(cid, now, total, active, center_lat, center_lon),
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)
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conn.commit()
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cur.close()
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logger.info('fleet snapshot saved: %d companies', len(company_vessels))
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def get_company_vessels(self, vessel_dfs: dict[str, 'pd.DataFrame']) -> dict[int, list[str]]:
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"""현재 AIS 수신 중인 등록 선단의 회사별 MMSI 목록 반환.
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Returns: {company_id: [mmsi, ...]}
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"""
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result: dict[int, list[str]] = {}
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for mmsi, vid in self._mmsi_to_vid.items():
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v = self._vessels.get(vid)
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if not v or mmsi not in vessel_dfs:
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continue
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result.setdefault(v['company_id'], []).append(mmsi)
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return result
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# 싱글턴
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fleet_tracker = FleetTracker()
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