htlee
e5d123e4c5
12h 누적 분석 결과 dark/transship이 운영 불가 수준으로 판정되어
탐지 철학을 근본부터 전환.
## dark 재설계: 넓은 탐지 + 의도적 OFF 의심 점수화
기존 "필터 제외" 방식에서 "넓게 기록 + 점수 산출 + 등급별 알람"으로 전환.
해경 베테랑 관점의 8가지 패턴을 가점 합산하여 0~100점 산출.
- P1 이동 중 OFF (gap 직전 SOG > 2kn)
- P2 민감 수역 경계 근처 OFF (영해/접속수역/특정조업수역)
- P3 반복 이력 (7일 내 재발) — 가장 강력
- P4 gap 후 이동거리 비정상 (은폐 이동)
- P5 주간 조업 시간 OFF
- P6 gap 직전 이상 행동 (teleport/급변)
- P7 무허가 선박 가점
- P8 장기 gap (3h/6h 구간별)
- 감점: gap 시작 위치가 한국 AIS 수신 커버리지 밖
완전 제외:
- 어구 AIS (GEAR_PATTERN 매칭, fleet_tracker SSOT)
- 한국 선박 (MMSI 440*, 441*) — 해경 관할 아님
등급: CRITICAL(70+) / HIGH(50~69) / WATCH(30~49) / NONE
이벤트는 HIGH 이상만 생성 (WATCH는 DB 저장만).
신규 함수:
- algorithms/dark_vessel.py: analyze_dark_pattern, compute_dark_suspicion
- scheduler.py: _is_dark_excluded, _fetch_dark_history (사이클당 1회 7일 이력 일괄 조회)
pipeline path + lightweight path 모두 동일 로직 적용.
결과는 features JSONB에 {dark_suspicion_score, dark_patterns,
dark_tier, dark_history_7d, dark_history_24h, gap_start_*} 저장.
## transship 재설계: 베테랑 함정근무자 기준
한정된 함정 자원으로 단속 출동을 결정할 수 있는 신뢰도 확보.
상수 재조정:
- SOG_THRESHOLD_KN: 2.0 → 1.0 (완전 정박만)
- PROXIMITY_DEG: 0.001 → 0.0007 (~77m)
- SUSPECT_DURATION_MIN: 60 → 45 (gap tolerance 있음)
- PAIR_EXPIRY_MIN: 120 → 180
- GAP_TOLERANCE_CYCLES: 2 신규 (GPS 노이즈 완화)
필수 조건 (모두 충족):
- 한국 EEZ 관할 수역 이내
- 환적 불가 선종 제외 (passenger/military/tanker/pilot/tug/sar)
- 어구 AIS 양쪽 제외
- 45분 이상 지속 (miss_count 2 사이클까지 용인)
점수 체계 (base 40):
- 야간(KST 20~04): +15
- 무허가 가점: +20
- COG 편차 > 45°: +20 (나란히 가는 선단 배제)
- 지속 ≥ 90분: +20
- 영해/접속수역 위치: +15
등급: CRITICAL(90+) / HIGH(70~89) / WATCH(50~69)
WATCH는 저장 없이 로그만. HIGH/CRITICAL만 이벤트.
pair_history 구조 확장:
- 기존: {(a,b): datetime}
- 신규: {(a,b): {'first_seen', 'last_seen', 'miss_count', 'last_lat/lon/cog_a/cog_b'}}
- miss_count > GAP_TOLERANCE_CYCLES면 삭제 (즉시 리셋 아님)
## event_generator 룰 교체
- dark_vessel_long 룰 제거 → dark_critical, dark_high (features.dark_tier 기반)
- transship 룰 제거 → transship_critical, transship_high (features.transship_tier 기반)
- DEDUP: ILLEGAL_TRANSSHIP 67→181, DARK_VESSEL 127→131, ZONE_DEPARTURE 127→89
## 공통 정리
- scheduler.py의 _gear_re 삭제, fleet_tracker.GEAR_PATTERN 단일 SSOT로 통합
604 lines
24 KiB
Python
604 lines
24 KiB
Python
import logging
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import time
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from datetime import datetime, timedelta, timezone
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from typing import Optional
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from zoneinfo import ZoneInfo
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from apscheduler.schedulers.background import BackgroundScheduler
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from config import settings
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from fleet_tracker import GEAR_PATTERN
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logger = logging.getLogger(__name__)
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_KST = ZoneInfo('Asia/Seoul')
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_scheduler: Optional[BackgroundScheduler] = None
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_last_run: dict = {
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'timestamp': None,
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'duration_sec': 0,
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'vessel_count': 0,
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'upserted': 0,
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'error': None,
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}
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_transship_pair_history: dict = {}
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# 한국 선박 MMSI prefix — dark 판별 완전 제외
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_KR_DOMESTIC_PREFIXES = ('440', '441')
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def _is_dark_excluded(mmsi: str, name: str) -> tuple[bool, str]:
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"""dark 탐지 대상에서 완전 제외할지. 어구/한국선만 필터.
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사용자 알람은 선박만 대상, 한국선은 해경 관할 아님.
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"""
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if any(mmsi.startswith(p) for p in _KR_DOMESTIC_PREFIXES):
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return True, 'kr_domestic'
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if name and GEAR_PATTERN.match(name):
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return True, 'gear_signal'
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return False, ''
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def _fetch_dark_history(kcg_conn, mmsi_list: list[str]) -> dict[str, dict]:
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"""최근 7일 내 is_dark=True 이력을 mmsi별로 집계.
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사이클 시작 시 한 번에 조회하여 점수 계산 시 재사용.
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"""
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if not mmsi_list:
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return {}
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try:
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cur = kcg_conn.cursor()
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cur.execute(
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"""
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SELECT mmsi,
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count(*) AS n7,
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count(*) FILTER (WHERE analyzed_at > now() - interval '24 hours') AS n24,
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max(analyzed_at) AS last_at
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FROM kcg.vessel_analysis_results
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WHERE is_dark = true
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AND analyzed_at > now() - interval '7 days'
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AND mmsi = ANY(%s)
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GROUP BY mmsi
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""",
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(list(mmsi_list),),
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)
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return {
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str(m): {'count_7d': int(n7 or 0), 'count_24h': int(n24 or 0), 'last_at': t}
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for m, n7, n24, t in cur.fetchall()
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}
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except Exception as e:
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logger.warning('fetch_dark_history failed: %s', e)
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return {}
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def get_last_run() -> dict:
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return _last_run.copy()
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def run_analysis_cycle():
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"""5분 주기 분석 사이클 — 인메모리 캐시 기반."""
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from cache.vessel_store import vessel_store
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from db import snpdb, kcgdb
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from pipeline.orchestrator import ChineseFishingVesselPipeline
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from algorithms.location import classify_zone
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from algorithms.fishing_pattern import compute_ucaf_score, compute_ucft_score
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from algorithms.dark_vessel import is_dark_vessel, analyze_dark_pattern, compute_dark_suspicion
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from algorithms.spoofing import compute_spoofing_score, count_speed_jumps, compute_bd09_offset
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from algorithms.risk import compute_vessel_risk_score
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from fleet_tracker import fleet_tracker
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from models.result import AnalysisResult
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start = time.time()
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_last_run['timestamp'] = datetime.now(timezone.utc).isoformat()
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_last_run['error'] = None
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try:
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# 1. 증분 로드 + stale 제거
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if vessel_store.last_bucket is None:
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logger.warning('last_bucket is None, skipping incremental fetch (initial load not complete)')
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df_new = None
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else:
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df_new = snpdb.fetch_incremental(vessel_store.last_bucket)
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if df_new is not None and len(df_new) > 0:
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vessel_store.merge_incremental(df_new)
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vessel_store.evict_stale(settings.CACHE_WINDOW_HOURS)
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# 정적정보 / 허가어선 주기적 갱신
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vessel_store.refresh_static_info()
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vessel_store.refresh_permit_registry()
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# 2. 분석 대상 선별 (SOG/COG 계산 포함)
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df_targets = vessel_store.select_analysis_targets()
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if len(df_targets) == 0:
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logger.info('no analysis targets, skipping cycle')
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_last_run['vessel_count'] = 0
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return
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# 3. 7단계 파이프라인 실행
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pipeline = ChineseFishingVesselPipeline()
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classifications, vessel_dfs = pipeline.run(df_targets)
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if not classifications:
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logger.info('no vessels classified, skipping')
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_last_run['vessel_count'] = 0
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return
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# 4. 등록 선단 기반 fleet 분석
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with kcgdb.get_conn() as kcg_conn:
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fleet_tracker.load_registry(kcg_conn)
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all_ais = []
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for mmsi, df in vessel_dfs.items():
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if len(df) > 0:
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last = df.iloc[-1]
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all_ais.append({
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'mmsi': mmsi,
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'name': vessel_store.get_vessel_info(mmsi).get('name', ''),
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'lat': float(last['lat']),
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'lon': float(last['lon']),
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})
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fleet_tracker.match_ais_to_registry(all_ais, kcg_conn)
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gear_signals = [v for v in all_ais if GEAR_PATTERN.match(v.get('name', '') or '')]
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fleet_tracker.track_gear_identity(gear_signals, kcg_conn)
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fleet_roles = fleet_tracker.build_fleet_clusters(vessel_dfs)
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fleet_tracker.save_snapshot(vessel_dfs, kcg_conn)
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gear_groups = []
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# 4.5 그룹 폴리곤 생성 + 저장
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try:
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from algorithms.polygon_builder import detect_gear_groups, build_all_group_snapshots
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company_vessels = fleet_tracker.get_company_vessels(vessel_dfs)
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gear_groups = detect_gear_groups(vessel_store)
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group_snapshots = build_all_group_snapshots(
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vessel_store, company_vessels,
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fleet_tracker._companies,
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)
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saved = kcgdb.save_group_snapshots(group_snapshots)
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cleaned = kcgdb.cleanup_group_snapshots(days=7)
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logger.info('group polygons: %d saved, %d cleaned, %d gear groups',
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saved, cleaned, len(gear_groups))
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except Exception as e:
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logger.warning('group polygon generation failed: %s', e)
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# 4.7 어구 연관성 분석 (멀티모델 패턴 추적)
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try:
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from algorithms.gear_correlation import run_gear_correlation
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from algorithms.gear_parent_inference import run_gear_parent_inference
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corr_result = run_gear_correlation(
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vessel_store=vessel_store,
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gear_groups=gear_groups,
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conn=kcg_conn,
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)
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logger.info(
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'gear correlation: %d scores updated, %d raw metrics, %d models',
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corr_result['updated'], corr_result['raw_inserted'],
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corr_result['models'],
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)
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inference_result = run_gear_parent_inference(
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vessel_store=vessel_store,
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gear_groups=gear_groups,
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conn=kcg_conn,
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)
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logger.info(
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'gear parent inference: %d groups, %d direct-match, %d candidates, %d promoted, %d review, %d skipped',
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inference_result['groups'],
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inference_result.get('direct_matched', 0),
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inference_result['candidates'],
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inference_result['promoted'],
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inference_result['review_required'],
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inference_result['skipped'],
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)
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except Exception as e:
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logger.warning('gear correlation failed: %s', e)
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# 5. 선박별 추가 알고리즘 → AnalysisResult 생성
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# dark 이력 일괄 조회 (7일 history) — 사이클당 1회
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now_kst_hour = datetime.now(_KST).hour
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all_chinese = vessel_store.get_chinese_mmsis()
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with kcgdb.get_conn() as hist_conn:
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dark_history_map = _fetch_dark_history(hist_conn, list(all_chinese))
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pipeline_dark_tiers = {'CRITICAL': 0, 'HIGH': 0, 'WATCH': 0, 'NONE': 0}
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pipeline_skip_counts = {'kr_domestic': 0, 'gear_signal': 0}
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results = []
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for c in classifications:
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mmsi = c['mmsi']
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df_v = vessel_dfs.get(mmsi)
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if df_v is None or len(df_v) == 0:
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continue
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last_row = df_v.iloc[-1]
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ts = last_row.get('timestamp')
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zone_info = classify_zone(last_row['lat'], last_row['lon'])
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gear_map = {'TRAWL': 'OT', 'PURSE': 'PS', 'LONGLINE': 'GN', 'TRAP': 'TRAP'}
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gear = gear_map.get(c['vessel_type'], 'OT')
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ucaf = compute_ucaf_score(df_v, gear)
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ucft = compute_ucft_score(df_v)
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# ── Dark: 넓은 탐지 + 의도적 OFF 의심 점수화 ──
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vname = vessel_store.get_vessel_info(mmsi).get('name', '') or ''
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is_permitted = vessel_store.is_permitted(mmsi)
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dark_excluded, dark_skip_reason = _is_dark_excluded(mmsi, vname)
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if dark_excluded:
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pipeline_skip_counts[dark_skip_reason] = pipeline_skip_counts.get(dark_skip_reason, 0) + 1
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dark = False
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gap_min = 0
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dark_features: dict = {
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'dark_suspicion_score': 0,
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'dark_patterns': [],
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'dark_tier': 'EXCLUDED',
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'dark_history_7d': 0,
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'dark_history_24h': 0,
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}
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else:
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gap_info = analyze_dark_pattern(df_v)
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dark = bool(gap_info.get('is_dark'))
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gap_min = int(gap_info.get('gap_min') or 0)
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history = dark_history_map.get(mmsi, {'count_7d': 0, 'count_24h': 0})
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score, patterns, tier = compute_dark_suspicion(
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gap_info, mmsi, is_permitted, history,
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now_kst_hour, classify_zone,
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)
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pipeline_dark_tiers[tier] = pipeline_dark_tiers.get(tier, 0) + 1
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dark_features = {
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'dark_suspicion_score': score,
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'dark_patterns': patterns,
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'dark_tier': tier,
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'dark_history_7d': int(history.get('count_7d', 0) or 0),
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'dark_history_24h': int(history.get('count_24h', 0) or 0),
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'gap_start_lat': gap_info.get('gap_start_lat'),
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'gap_start_lon': gap_info.get('gap_start_lon'),
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'gap_start_sog': gap_info.get('gap_start_sog'),
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'gap_start_state': gap_info.get('gap_start_state'),
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}
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spoof_score = compute_spoofing_score(df_v)
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speed_jumps = count_speed_jumps(df_v)
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bd09_offset = compute_bd09_offset(last_row['lat'], last_row['lon'])
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fleet_info = fleet_roles.get(mmsi, {})
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risk_score, risk_level = compute_vessel_risk_score(
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mmsi, df_v, zone_info, is_permitted=is_permitted,
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)
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activity = 'UNKNOWN'
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if 'state' in df_v.columns and len(df_v) > 0:
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activity = df_v['state'].mode().iloc[0]
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merged_features = {**(c.get('features', {}) or {}), **dark_features}
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results.append(AnalysisResult(
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mmsi=mmsi,
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timestamp=ts,
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vessel_type=c['vessel_type'],
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confidence=c['confidence'],
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fishing_pct=c['fishing_pct'],
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cluster_id=fleet_info.get('cluster_id', -1),
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season=c['season'],
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zone=zone_info.get('zone', 'EEZ_OR_BEYOND'),
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dist_to_baseline_nm=zone_info.get('dist_from_baseline_nm', 999.0),
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activity_state=activity,
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ucaf_score=ucaf,
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ucft_score=ucft,
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is_dark=dark,
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gap_duration_min=gap_min,
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spoofing_score=spoof_score,
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bd09_offset_m=bd09_offset,
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speed_jump_count=speed_jumps,
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cluster_size=fleet_info.get('cluster_size', 0),
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is_leader=fleet_info.get('is_leader', False),
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fleet_role=fleet_info.get('fleet_role', 'NOISE'),
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risk_score=risk_score,
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risk_level=risk_level,
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features=merged_features,
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))
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logger.info(
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'pipeline dark: tiers=%s skip=%s',
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pipeline_dark_tiers, pipeline_skip_counts,
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)
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# ── 5.5 경량 분석 — 파이프라인 미통과 412* 선박 ──
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# vessel_store._tracks의 24h 누적 궤적을 직접 활용하여 dark/spoof 신호도 산출.
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from algorithms.risk import compute_lightweight_risk_score
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pipeline_mmsis = {c['mmsi'] for c in classifications}
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lightweight_mmsis = vessel_store.get_chinese_mmsis() - pipeline_mmsis
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if lightweight_mmsis:
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now = datetime.now(timezone.utc)
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all_positions = vessel_store.get_all_latest_positions()
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lw_count = 0
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lw_dark = 0
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lw_spoof = 0
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lw_dark_tiers = {'CRITICAL': 0, 'HIGH': 0, 'WATCH': 0, 'NONE': 0}
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lw_dark_skip = {'kr_domestic': 0, 'gear_signal': 0}
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for mmsi in lightweight_mmsis:
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pos = all_positions.get(mmsi)
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if pos is None or pos.get('lat') is None:
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continue
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lat, lon = pos['lat'], pos['lon']
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sog = pos.get('sog', 0) or 0
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cog = pos.get('cog', 0) or 0
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ts = pos.get('timestamp', now)
|
|
|
|
zone_info = classify_zone(lat, lon)
|
|
if sog <= 1.0:
|
|
state = 'STATIONARY'
|
|
elif sog <= 5.0:
|
|
state = 'FISHING'
|
|
else:
|
|
state = 'SAILING'
|
|
|
|
is_permitted = vessel_store.is_permitted(mmsi)
|
|
vname = vessel_store.get_vessel_info(mmsi).get('name', '') or ''
|
|
|
|
# ── Dark: 사전 필터 (어구/한국선) ──
|
|
dark_excluded, dark_skip_reason = _is_dark_excluded(mmsi, vname)
|
|
if dark_excluded:
|
|
lw_dark_skip[dark_skip_reason] = lw_dark_skip.get(dark_skip_reason, 0) + 1
|
|
dark = False
|
|
gap_min = 0
|
|
dark_features: dict = {
|
|
'dark_suspicion_score': 0,
|
|
'dark_patterns': [],
|
|
'dark_tier': 'EXCLUDED',
|
|
'dark_history_7d': 0,
|
|
'dark_history_24h': 0,
|
|
}
|
|
spoof_score = 0.0
|
|
speed_jumps = 0
|
|
else:
|
|
df_v = vessel_store._tracks.get(mmsi)
|
|
spoof_score = 0.0
|
|
speed_jumps = 0
|
|
if df_v is not None and len(df_v) >= 2:
|
|
try:
|
|
spoof_score = compute_spoofing_score(df_v)
|
|
except Exception:
|
|
pass
|
|
try:
|
|
speed_jumps = count_speed_jumps(df_v)
|
|
except Exception:
|
|
pass
|
|
try:
|
|
gap_info = analyze_dark_pattern(df_v)
|
|
except Exception:
|
|
gap_info = {'is_dark': False, 'gap_min': 0}
|
|
else:
|
|
gap_info = {'is_dark': False, 'gap_min': 0}
|
|
|
|
dark = bool(gap_info.get('is_dark'))
|
|
gap_min = int(gap_info.get('gap_min') or 0)
|
|
|
|
history = dark_history_map.get(mmsi, {'count_7d': 0, 'count_24h': 0})
|
|
score, patterns, tier = compute_dark_suspicion(
|
|
gap_info, mmsi, is_permitted, history,
|
|
now_kst_hour, classify_zone,
|
|
)
|
|
lw_dark_tiers[tier] = lw_dark_tiers.get(tier, 0) + 1
|
|
|
|
dark_features = {
|
|
'dark_suspicion_score': score,
|
|
'dark_patterns': patterns,
|
|
'dark_tier': tier,
|
|
'dark_history_7d': int(history.get('count_7d', 0) or 0),
|
|
'dark_history_24h': int(history.get('count_24h', 0) or 0),
|
|
'gap_start_lat': gap_info.get('gap_start_lat'),
|
|
'gap_start_lon': gap_info.get('gap_start_lon'),
|
|
'gap_start_sog': gap_info.get('gap_start_sog'),
|
|
'gap_start_state': gap_info.get('gap_start_state'),
|
|
}
|
|
|
|
if dark:
|
|
lw_dark += 1
|
|
if spoof_score > 0.5:
|
|
lw_spoof += 1
|
|
|
|
risk_score, risk_level = compute_lightweight_risk_score(
|
|
zone_info, sog, is_permitted=is_permitted,
|
|
is_dark=dark, gap_duration_min=gap_min,
|
|
spoofing_score=spoof_score,
|
|
)
|
|
|
|
# BD-09 오프셋은 중국 선박이므로 제외 (412* = 중국)
|
|
results.append(AnalysisResult(
|
|
mmsi=mmsi,
|
|
timestamp=ts,
|
|
vessel_type='UNKNOWN',
|
|
confidence=0.0,
|
|
fishing_pct=0.0,
|
|
zone=zone_info.get('zone', 'EEZ_OR_BEYOND'),
|
|
dist_to_baseline_nm=zone_info.get('dist_from_baseline_nm', 999.0),
|
|
activity_state=state,
|
|
ucaf_score=0.0,
|
|
ucft_score=0.0,
|
|
is_dark=dark,
|
|
gap_duration_min=gap_min,
|
|
spoofing_score=spoof_score,
|
|
bd09_offset_m=0.0,
|
|
speed_jump_count=speed_jumps,
|
|
cluster_id=-1,
|
|
cluster_size=0,
|
|
is_leader=False,
|
|
fleet_role='NONE',
|
|
risk_score=risk_score,
|
|
risk_level=risk_level,
|
|
is_transship_suspect=False,
|
|
transship_pair_mmsi='',
|
|
transship_duration_min=0,
|
|
features=dark_features,
|
|
))
|
|
lw_count += 1
|
|
logger.info(
|
|
'lightweight analysis: %d vessels (dark=%d, spoof>0.5=%d, tiers=%s, skip=%s)',
|
|
lw_count, lw_dark, lw_spoof, lw_dark_tiers, lw_dark_skip,
|
|
)
|
|
|
|
# 6. 환적 의심 탐지 (점수 기반, 베테랑 관점 필터)
|
|
from algorithms.transshipment import detect_transshipment
|
|
|
|
results_map = {r.mmsi: r for r in results}
|
|
transship_items = detect_transshipment(
|
|
df_targets,
|
|
_transship_pair_history,
|
|
get_vessel_info=vessel_store.get_vessel_info,
|
|
is_permitted=vessel_store.is_permitted,
|
|
classify_zone_fn=classify_zone,
|
|
now_kst_hour=now_kst_hour,
|
|
)
|
|
for item in transship_items:
|
|
a = item['pair_a']
|
|
b = item['pair_b']
|
|
dur = item['duration_min']
|
|
tier = item['severity']
|
|
if tier == 'WATCH':
|
|
continue # WATCH 등급은 저장 안 함 (로그만)
|
|
for m, pair in ((a, b), (b, a)):
|
|
if m in results_map:
|
|
r_obj = results_map[m]
|
|
r_obj.is_transship_suspect = True
|
|
r_obj.transship_pair_mmsi = pair
|
|
r_obj.transship_duration_min = dur
|
|
r_obj.features = {
|
|
**(r_obj.features or {}),
|
|
'transship_tier': tier,
|
|
'transship_score': item['score'],
|
|
}
|
|
|
|
# 7. 결과 저장
|
|
upserted = kcgdb.upsert_results(results)
|
|
kcgdb.cleanup_old(hours=48)
|
|
|
|
# 8. 출력 모듈 (이벤트 생성, 위반 분류, KPI 갱신, 통계 집계, 경보)
|
|
try:
|
|
from output.violation_classifier import run_violation_classifier
|
|
from output.event_generator import run_event_generator
|
|
from output.kpi_writer import run_kpi_writer
|
|
from output.stats_aggregator import aggregate_hourly, aggregate_daily
|
|
from output.alert_dispatcher import run_alert_dispatcher
|
|
|
|
from dataclasses import asdict
|
|
results_dicts = [asdict(r) for r in results]
|
|
# 필드명 매핑 (AnalysisResult → 출력 모듈 기대 형식)
|
|
for d in results_dicts:
|
|
d['zone_code'] = d.pop('zone', None)
|
|
d['gap_duration_min'] = d.get('gap_duration_min', 0)
|
|
d['transship_suspect'] = d.pop('is_transship_suspect', False)
|
|
d['fleet_is_leader'] = d.pop('is_leader', False)
|
|
d['fleet_cluster_id'] = d.pop('cluster_id', None)
|
|
d['speed_kn'] = None # 분석 결과에 속도 없음
|
|
run_violation_classifier(results_dicts)
|
|
run_event_generator(results_dicts)
|
|
run_kpi_writer()
|
|
aggregate_hourly()
|
|
aggregate_daily()
|
|
run_alert_dispatcher()
|
|
logger.info('output modules completed')
|
|
except Exception as e:
|
|
logger.warning('output modules failed (non-fatal): %s', e)
|
|
|
|
# 9. Redis에 분석 컨텍스트 캐싱 (채팅용)
|
|
try:
|
|
from chat.cache import cache_analysis_context
|
|
|
|
results_map = {r.mmsi: r for r in results}
|
|
risk_dist = {}
|
|
zone_dist = {}
|
|
dark_count = 0
|
|
spoofing_count = 0
|
|
transship_count = 0
|
|
top_risk_list = []
|
|
|
|
for r in results:
|
|
risk_dist[r.risk_level] = risk_dist.get(r.risk_level, 0) + 1
|
|
zone_dist[r.zone] = zone_dist.get(r.zone, 0) + 1
|
|
if r.is_dark:
|
|
dark_count += 1
|
|
if r.spoofing_score > 0.5:
|
|
spoofing_count += 1
|
|
if r.is_transship_suspect:
|
|
transship_count += 1
|
|
top_risk_list.append({
|
|
'mmsi': r.mmsi,
|
|
'name': vessel_store.get_vessel_info(r.mmsi).get('name', r.mmsi),
|
|
'risk_score': r.risk_score,
|
|
'risk_level': r.risk_level,
|
|
'zone': r.zone,
|
|
'is_dark': r.is_dark,
|
|
'is_transship': r.is_transship_suspect,
|
|
'activity_state': r.activity_state,
|
|
})
|
|
|
|
top_risk_list.sort(key=lambda x: x['risk_score'], reverse=True)
|
|
|
|
cache_analysis_context({
|
|
'vessel_stats': vessel_store.stats(),
|
|
'risk_distribution': {**risk_dist, **zone_dist},
|
|
'dark_count': dark_count,
|
|
'spoofing_count': spoofing_count,
|
|
'transship_count': transship_count,
|
|
'top_risk_vessels': top_risk_list[:10],
|
|
'polygon_summary': kcgdb.fetch_polygon_summary(),
|
|
})
|
|
except Exception as e:
|
|
logger.warning('failed to cache analysis context for chat: %s', e)
|
|
|
|
elapsed = round(time.time() - start, 2)
|
|
_last_run['duration_sec'] = elapsed
|
|
_last_run['vessel_count'] = len(results)
|
|
_last_run['upserted'] = upserted
|
|
logger.info(
|
|
'analysis cycle: %d vessels, %d upserted, %.2fs',
|
|
len(results), upserted, elapsed,
|
|
)
|
|
|
|
except Exception as e:
|
|
_last_run['error'] = str(e)
|
|
logger.exception('analysis cycle failed: %s', e)
|
|
|
|
|
|
def start_scheduler():
|
|
global _scheduler
|
|
_scheduler = BackgroundScheduler()
|
|
_scheduler.add_job(
|
|
run_analysis_cycle,
|
|
'interval',
|
|
minutes=settings.SCHEDULER_INTERVAL_MIN,
|
|
id='vessel_analysis',
|
|
max_instances=1,
|
|
replace_existing=True,
|
|
)
|
|
# 파티션 유지보수 (매일 04:00)
|
|
from db.partition_manager import maintain_partitions
|
|
_scheduler.add_job(
|
|
maintain_partitions,
|
|
'cron', hour=4, minute=0,
|
|
id='partition_maintenance',
|
|
max_instances=1,
|
|
replace_existing=True,
|
|
)
|
|
_scheduler.start()
|
|
logger.info('scheduler started (interval=%dm)', settings.SCHEDULER_INTERVAL_MIN)
|
|
|
|
|
|
def stop_scheduler():
|
|
global _scheduler
|
|
if _scheduler:
|
|
_scheduler.shutdown(wait=False)
|
|
_scheduler = None
|
|
logger.info('scheduler stopped')
|