htlee
a68dfb21b2
- 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>
85 lines
2.0 KiB
Python
85 lines
2.0 KiB
Python
from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from typing import Optional
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@dataclass
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class AnalysisResult:
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"""vessel_analysis_results 테이블 28컬럼 매핑."""
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mmsi: str
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timestamp: datetime
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# 분류 결과
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vessel_type: str = 'UNKNOWN'
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confidence: float = 0.0
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fishing_pct: float = 0.0
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cluster_id: int = -1
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season: str = 'UNKNOWN'
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# ALGO 01: 위치
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zone: str = 'EEZ_OR_BEYOND'
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dist_to_baseline_nm: float = 999.0
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# ALGO 02: 활동 상태
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activity_state: str = 'UNKNOWN'
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ucaf_score: float = 0.0
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ucft_score: float = 0.0
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# ALGO 03: 다크 베셀
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is_dark: bool = False
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gap_duration_min: int = 0
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# ALGO 04: GPS 스푸핑
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spoofing_score: float = 0.0
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bd09_offset_m: float = 0.0
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speed_jump_count: int = 0
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# ALGO 05+06: 선단
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cluster_size: int = 0
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is_leader: bool = False
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fleet_role: str = 'NOISE'
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# ALGO 07: 위험도
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risk_score: int = 0
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risk_level: str = 'LOW'
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# 특징 벡터
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features: dict = field(default_factory=dict)
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# 메타
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analyzed_at: Optional[datetime] = None
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def __post_init__(self):
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if self.analyzed_at is None:
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self.analyzed_at = datetime.now(timezone.utc)
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def to_db_tuple(self) -> tuple:
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import json
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return (
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self.mmsi,
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self.timestamp,
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self.vessel_type,
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self.confidence,
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self.fishing_pct,
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self.cluster_id,
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self.season,
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self.zone,
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self.dist_to_baseline_nm,
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self.activity_state,
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self.ucaf_score,
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self.ucft_score,
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self.is_dark,
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self.gap_duration_min,
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self.spoofing_score,
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self.bd09_offset_m,
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self.speed_jump_count,
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self.cluster_size,
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self.is_leader,
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self.fleet_role,
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self.risk_score,
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self.risk_level,
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json.dumps(self.features),
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self.analyzed_at,
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)
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