htlee
7dd46f2078
Codex Lab 환경(iran-airstrike-replay-codex)에서 검증 완료된 어구 모선 자동 추론 + 검토 워크플로우 전체를 이식. ## Python (prediction/) - gear_parent_inference(1,428줄): 다층 점수 모델 (correlation + name + track + prior bonus) - gear_parent_episode(631줄): Episode 연속성 (Jaccard + 공간거리) - gear_name_rules: 모선 이름 정규화 + 4자 미만 필터 - scheduler: 추론 호출 단계 추가 (4.8) - fleet_tracker/kcgdb: SQL qualified_table() 동적화 - gear_correlation: timestamp 필드 추가 ## DB (database/migration/ 012~015) - 후보 스냅샷, resolution, episode, 라벨 세션, 제외 관리 테이블 9개 + VIEW 2개 ## Backend (Java) - 12개 DTO/Controller (ParentInferenceWorkflowController 등) - GroupPolygonService: parent_resolution LEFT JOIN + 15개 API 메서드 ## Frontend - ParentReviewPanel: 모선 검토 대시보드 - vesselAnalysis: 10개 신규 API 함수 + 6개 타입 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
391 lines
15 KiB
Python
391 lines
15 KiB
Python
import logging
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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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from apscheduler.schedulers.background import BackgroundScheduler
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from config import settings
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logger = logging.getLogger(__name__)
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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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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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import re as _re
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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
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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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_gear_re = _re.compile(r'^.+_(?=\S*\d)\S+(?:[_ ]\S*)*[_ ]*$|^\d+$|^.+%$')
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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_re.match(v.get('name', ''))]
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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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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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except Exception as e:
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logger.warning('gear correlation failed: %s', e)
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# 4.8 어구 모선 추론 (episode continuity + 다층 점수 모델)
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try:
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from algorithms.gear_parent_inference import run_gear_parent_inference
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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 parent inference failed: %s', e)
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# 5. 선박별 추가 알고리즘 → AnalysisResult 생성
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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, gap_min = is_dark_vessel(df_v)
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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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is_permitted = vessel_store.is_permitted(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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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=c.get('features', {}),
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))
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# ── 5.5 경량 분석 — 파이프라인 미통과 412* 선박 ──
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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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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)
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zone_info = classify_zone(lat, lon)
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if sog <= 1.0:
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state = 'STATIONARY'
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elif sog <= 5.0:
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state = 'FISHING'
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else:
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state = 'SAILING'
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is_permitted = vessel_store.is_permitted(mmsi)
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risk_score, risk_level = compute_lightweight_risk_score(
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zone_info, sog, is_permitted=is_permitted,
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)
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# BD-09 오프셋은 중국 선박이므로 제외 (412* = 중국)
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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='UNKNOWN',
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confidence=0.0,
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fishing_pct=0.0,
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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=state,
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ucaf_score=0.0,
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ucft_score=0.0,
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is_dark=False,
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gap_duration_min=0,
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spoofing_score=0.0,
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bd09_offset_m=0.0,
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speed_jump_count=0,
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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='NONE',
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risk_score=risk_score,
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risk_level=risk_level,
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is_transship_suspect=False,
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transship_pair_mmsi='',
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transship_duration_min=0,
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))
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lw_count += 1
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logger.info('lightweight analysis: %d vessels', lw_count)
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# 6. 환적 의심 탐지 (pair_history 모듈 레벨로 사이클 간 유지)
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from algorithms.transshipment import detect_transshipment
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results_map = {r.mmsi: r for r in results}
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transship_pairs = detect_transshipment(df_targets, _transship_pair_history)
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for mmsi_a, mmsi_b, dur in transship_pairs:
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if mmsi_a in results_map:
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results_map[mmsi_a].is_transship_suspect = True
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results_map[mmsi_a].transship_pair_mmsi = mmsi_b
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results_map[mmsi_a].transship_duration_min = dur
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if mmsi_b in results_map:
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results_map[mmsi_b].is_transship_suspect = True
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results_map[mmsi_b].transship_pair_mmsi = mmsi_a
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results_map[mmsi_b].transship_duration_min = dur
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# 7. 결과 저장
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upserted = kcgdb.upsert_results(results)
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kcgdb.cleanup_old(hours=48)
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# 8. Redis에 분석 컨텍스트 캐싱 (채팅용)
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try:
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from chat.cache import cache_analysis_context
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results_map = {r.mmsi: r for r in results}
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risk_dist = {}
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zone_dist = {}
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dark_count = 0
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spoofing_count = 0
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transship_count = 0
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top_risk_list = []
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for r in results:
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risk_dist[r.risk_level] = risk_dist.get(r.risk_level, 0) + 1
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zone_dist[r.zone] = zone_dist.get(r.zone, 0) + 1
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if r.is_dark:
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dark_count += 1
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if r.spoofing_score > 0.5:
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spoofing_count += 1
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if r.is_transship_suspect:
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transship_count += 1
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top_risk_list.append({
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'mmsi': r.mmsi,
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'name': vessel_store.get_vessel_info(r.mmsi).get('name', r.mmsi),
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'risk_score': r.risk_score,
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'risk_level': r.risk_level,
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'zone': r.zone,
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'is_dark': r.is_dark,
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'is_transship': r.is_transship_suspect,
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'activity_state': r.activity_state,
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})
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top_risk_list.sort(key=lambda x: x['risk_score'], reverse=True)
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cache_analysis_context({
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'vessel_stats': vessel_store.stats(),
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'risk_distribution': {**risk_dist, **zone_dist},
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'dark_count': dark_count,
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'spoofing_count': spoofing_count,
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'transship_count': transship_count,
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'top_risk_vessels': top_risk_list[:10],
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'polygon_summary': kcgdb.fetch_polygon_summary(),
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})
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except Exception as e:
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logger.warning('failed to cache analysis context for chat: %s', e)
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elapsed = round(time.time() - start, 2)
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_last_run['duration_sec'] = elapsed
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_last_run['vessel_count'] = len(results)
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_last_run['upserted'] = upserted
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logger.info(
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'analysis cycle: %d vessels, %d upserted, %.2fs',
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len(results), upserted, elapsed,
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)
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except Exception as e:
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_last_run['error'] = str(e)
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logger.exception('analysis cycle failed: %s', e)
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def start_scheduler():
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global _scheduler
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_scheduler = BackgroundScheduler()
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_scheduler.add_job(
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run_analysis_cycle,
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'interval',
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minutes=settings.SCHEDULER_INTERVAL_MIN,
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id='vessel_analysis',
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max_instances=1,
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replace_existing=True,
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)
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# 파티션 유지보수 (매일 04:00)
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from db.partition_manager import maintain_partitions
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_scheduler.add_job(
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maintain_partitions,
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'cron', hour=4, minute=0,
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id='partition_maintenance',
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max_instances=1,
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replace_existing=True,
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)
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_scheduler.start()
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logger.info('scheduler started (interval=%dm)', settings.SCHEDULER_INTERVAL_MIN)
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def stop_scheduler():
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global _scheduler
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if _scheduler:
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_scheduler.shutdown(wait=False)
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_scheduler = None
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logger.info('scheduler stopped')
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