htlee
83b3d80c6d
- prediction/: FastAPI 7단계 분류 파이프라인 + 6개 탐지 알고리즘 - snpdb 궤적 조회 → 인메모리 캐시(13K척) → 분류 → kcgdb 저장 - APScheduler 5분 주기, Python 3.9 호환 - 버그 수정: @property last_bucket, SQL INTERVAL 바인딩, rollback, None 가드 - 보안: DB 비밀번호 하드코딩 제거 → env 환경변수 필수 - deploy/kcg-prediction.service: systemd 서비스 (redis-211, 포트 8001) - deploy.yml: prediction CI/CD 배포 단계 추가 (192.168.1.18:32023) - backend: PredictionProxyController (health/status/trigger 프록시) - backend: AppProperties predictionBaseUrl + AuthFilter 인증 예외 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
import pandas as pd
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from algorithms.location import haversine_nm, bd09_to_wgs84, compute_bd09_offset # noqa: F401
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MAX_FISHING_SPEED_KNOTS = 25.0
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def detect_teleportation(df_vessel: pd.DataFrame,
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max_speed_knots: float = MAX_FISHING_SPEED_KNOTS) -> list[dict]:
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"""연속 AIS 포인트 간 물리적 불가능 이동 탐지."""
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if len(df_vessel) < 2:
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return []
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anomalies = []
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records = df_vessel.sort_values('timestamp').to_dict('records')
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for i in range(1, len(records)):
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prev, curr = records[i - 1], records[i]
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dist_nm = haversine_nm(prev['lat'], prev['lon'], curr['lat'], curr['lon'])
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dt_hours = (
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pd.Timestamp(curr['timestamp']) - pd.Timestamp(prev['timestamp'])
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).total_seconds() / 3600
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if dt_hours <= 0:
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continue
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implied_speed = dist_nm / dt_hours
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if implied_speed > max_speed_knots:
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anomalies.append({
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'idx': i,
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'dist_nm': round(dist_nm, 2),
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'implied_kn': round(implied_speed, 1),
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'type': 'TELEPORTATION',
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'confidence': 'HIGH' if implied_speed > 50 else 'MED',
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})
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return anomalies
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def count_speed_jumps(df_vessel: pd.DataFrame, threshold_knots: float = 10.0) -> int:
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"""연속 SOG 급변 횟수."""
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if len(df_vessel) < 2:
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return 0
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sog = df_vessel['sog'].values
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jumps = 0
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for i in range(1, len(sog)):
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if abs(sog[i] - sog[i - 1]) > threshold_knots:
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jumps += 1
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return jumps
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def compute_spoofing_score(df_vessel: pd.DataFrame) -> float:
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"""종합 GPS 스푸핑 점수 (0~1)."""
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if len(df_vessel) < 2:
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return 0.0
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score = 0.0
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n = len(df_vessel)
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# 순간이동 비율
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teleports = detect_teleportation(df_vessel)
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if teleports:
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score += min(0.4, len(teleports) / n * 10)
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# SOG 급변 비율
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jumps = count_speed_jumps(df_vessel)
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if jumps > 0:
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score += min(0.3, jumps / n * 5)
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# BD09 오프셋 (중국 좌표 사용 의심)
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mid_idx = len(df_vessel) // 2
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row = df_vessel.iloc[mid_idx]
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offset = compute_bd09_offset(row['lat'], row['lon'])
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if offset > 300: # 300m 이상
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score += 0.3
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elif offset > 100:
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score += 0.1
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return round(min(score, 1.0), 4)
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