htlee
d13baf302f
- risk.py: 특정어업수역(ZONE_I~IV) 내 미허가 어선 +25점 가산 - fleet.py: DBSCAN → 고정 그리드 셀(5NM) 클러스터링 (체인 효과 차단) - max_cluster_size=20으로 거대 클러스터 방지 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
80 lines
2.0 KiB
Python
80 lines
2.0 KiB
Python
from typing import Optional, Tuple
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import pandas as pd
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from algorithms.location import classify_zone
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from algorithms.fishing_pattern import detect_fishing_segments, detect_trawl_uturn
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from algorithms.dark_vessel import detect_ais_gaps
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from algorithms.spoofing import detect_teleportation
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def compute_vessel_risk_score(
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mmsi: str,
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df_vessel: pd.DataFrame,
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zone_info: Optional[dict] = None,
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is_permitted: Optional[bool] = None,
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) -> Tuple[int, str]:
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"""선박별 종합 위반 위험도 (0~100점).
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Returns: (risk_score, risk_level)
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"""
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if len(df_vessel) == 0:
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return 0, 'LOW'
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score = 0
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# 1. 위치 기반 (최대 40점)
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if zone_info is None:
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last = df_vessel.iloc[-1]
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zone_info = classify_zone(last['lat'], last['lon'])
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zone = zone_info.get('zone', '')
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if zone == 'TERRITORIAL_SEA':
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score += 40
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elif zone == 'CONTIGUOUS_ZONE':
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score += 10
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elif zone.startswith('ZONE_'):
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# 특정어업수역 내 — 무허가면 가산
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if is_permitted is not None and not is_permitted:
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score += 25
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# 2. 조업 행위 (최대 30점)
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segs = detect_fishing_segments(df_vessel)
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ts_fishing = [s for s in segs if s.get('in_territorial_sea')]
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if ts_fishing:
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score += 20
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elif segs:
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score += 5
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uturn = detect_trawl_uturn(df_vessel)
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if uturn.get('trawl_suspected'):
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score += 10
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# 3. AIS 조작 (최대 35점)
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teleports = detect_teleportation(df_vessel)
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if teleports:
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score += 20
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gaps = detect_ais_gaps(df_vessel)
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critical_gaps = [g for g in gaps if g['gap_min'] >= 60]
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if critical_gaps:
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score += 15
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elif gaps:
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score += 5
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# 4. 허가 이력 (최대 20점)
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if is_permitted is not None and not is_permitted:
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score += 20
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score = min(score, 100)
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if score >= 70:
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level = 'CRITICAL'
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elif score >= 50:
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level = 'HIGH'
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elif score >= 30:
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level = 'MEDIUM'
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else:
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level = 'LOW'
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return score, level
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