kcg-ai-monitoring/prediction/algorithms/fishing_pattern.py

from __future__ import annotations

import pandas as pd
from algorithms.location import haversine_nm, classify_zone  # noqa: F401 (haversine_nm re-exported for callers)

# Yan et al. (2022) 어구별 조업 속도 임계값
GEAR_SOG_THRESHOLDS: dict[str, tuple[float, float]] = {
    'PT': (2.5, 4.5),       # 쌍끌이저인망
    'OT': (2.0, 4.0),       # 단선저인망
    'GN': (0.5, 2.5),       # 자망·유망
    'SQ': (0.0, 1.0),       # 오징어채낚기
    'TRAP': (0.3, 1.5),     # 통발
    'PS': (3.0, 6.0),       # 선망
    'TRAWL': (2.0, 4.5),    # (alias)
    'PURSE': (3.0, 6.0),    # (alias)
    'LONGLINE': (0.5, 2.5),
}
TRANSIT_SOG_MIN = 5.0
ANCHORED_SOG_MAX = 0.5


def classify_vessel_state(sog: float, cog_delta: float = 0.0,
                          gear_type: str = 'PT') -> str:
    """UCAF: 어구별 상태 분류."""
    if sog <= ANCHORED_SOG_MAX:
        return 'ANCHORED'
    if sog >= TRANSIT_SOG_MIN:
        return 'TRANSIT'
    sog_min, sog_max = GEAR_SOG_THRESHOLDS.get(gear_type, (1.0, 5.0))
    if sog_min <= sog <= sog_max:
        # PT(쌍끌이): 공조 시 COG 변화가 적어야 함 — 큰 COG 변화는 비조업
        if gear_type == 'PT' and cog_delta > 30.0:
            return 'UNKNOWN'
        return 'FISHING'
    return 'UNKNOWN'


def compute_ucaf_score(df_vessel: pd.DataFrame, gear_type: str = 'PT') -> float:
    """UCAF 점수: 어구별 조업 상태 비율 (0~1)."""
    if len(df_vessel) == 0:
        return 0.0
    sog_min, sog_max = GEAR_SOG_THRESHOLDS.get(gear_type, (1.0, 5.0))
    in_range = df_vessel['sog'].between(sog_min, sog_max).sum()
    return round(in_range / len(df_vessel), 4)


def compute_ucft_score(df_vessel: pd.DataFrame) -> float:
    """UCFT 점수: 조업 vs 항행 이진 신뢰도 (0~1)."""
    if len(df_vessel) == 0:
        return 0.0
    fishing = (df_vessel['sog'].between(0.5, 5.0)).sum()
    transit = (df_vessel['sog'] >= TRANSIT_SOG_MIN).sum()
    total = fishing + transit
    if total == 0:
        return 0.0
    return round(fishing / total, 4)


def detect_fishing_segments(df_vessel: pd.DataFrame,
                            window_min: int = 15,
                            gear_type: str = 'PT') -> list[dict]:
    """연속 조업 구간 추출."""
    if len(df_vessel) < 2:
        return []

    segments: list[dict] = []
    in_fishing = False
    seg_start_idx = 0

    records = df_vessel.to_dict('records')
    for i, rec in enumerate(records):
        sog = rec.get('sog', 0)
        state = classify_vessel_state(sog, gear_type=gear_type)

        if state == 'FISHING' and not in_fishing:
            in_fishing = True
            seg_start_idx = i
        elif state != 'FISHING' and in_fishing:
            start_ts = records[seg_start_idx].get('timestamp')
            end_ts = rec.get('timestamp')
            if start_ts and end_ts:
                dur_sec = (pd.Timestamp(end_ts) - pd.Timestamp(start_ts)).total_seconds()
                dur_min = dur_sec / 60
                if dur_min >= window_min:
                    zone_info = classify_zone(
                        records[seg_start_idx].get('lat', 0),
                        records[seg_start_idx].get('lon', 0),
                    )
                    seg_end = i - 1
                    # Count speed anomalies within segment (SOG > TRANSIT_SOG_MIN during fishing)
                    speed_anomalies = 0
                    for idx in range(seg_start_idx, seg_end + 1):
                        if records[idx].get('sog', 0) > TRANSIT_SOG_MIN:
                            speed_anomalies += 1
                    segments.append({
                        'start_idx': seg_start_idx,
                        'end_idx': seg_end,
                        'duration_min': round(dur_min, 1),
                        'zone': zone_info.get('zone', 'UNKNOWN'),
                        'in_territorial_sea': zone_info.get('zone') == 'TERRITORIAL_SEA',
                        'speed_anomaly_count': speed_anomalies,
                    })
            in_fishing = False

    # 트랙 끝까지 조업 중이면 마지막 세그먼트 추가
    if in_fishing and len(records) > seg_start_idx:
        start_ts = records[seg_start_idx].get('timestamp')
        end_ts = records[-1].get('timestamp')
        if start_ts and end_ts:
            dur_sec = (pd.Timestamp(end_ts) - pd.Timestamp(start_ts)).total_seconds()
            dur_min = dur_sec / 60
            if dur_min >= window_min:
                zone_info = classify_zone(
                    records[seg_start_idx].get('lat', 0),
                    records[seg_start_idx].get('lon', 0),
                )
                seg_end = len(records) - 1
                # Count speed anomalies within segment (SOG > TRANSIT_SOG_MIN during fishing)
                speed_anomalies = 0
                for idx in range(seg_start_idx, seg_end + 1):
                    if records[idx].get('sog', 0) > TRANSIT_SOG_MIN:
                        speed_anomalies += 1
                segments.append({
                    'start_idx': seg_start_idx,
                    'end_idx': seg_end,
                    'duration_min': round(dur_min, 1),
                    'zone': zone_info.get('zone', 'UNKNOWN'),
                    'in_territorial_sea': zone_info.get('zone') == 'TERRITORIAL_SEA',
                    'speed_anomaly_count': speed_anomalies,
                })

    return segments


def detect_trawl_uturn(df_vessel: pd.DataFrame,
                       uturn_threshold_deg: float = 150.0,
                       min_uturn_count: int = 3) -> dict:
    """U-turn 왕복 패턴 감지 (저인망 특징)."""
    if len(df_vessel) < 2:
        return {'uturn_count': 0, 'trawl_suspected': False}

    uturn_count = 0
    cog_vals = df_vessel['cog'].values
    sog_vals = df_vessel['sog'].values

    for i in range(1, len(cog_vals)):
        delta = abs((cog_vals[i] - cog_vals[i - 1] + 180) % 360 - 180)
        if delta >= uturn_threshold_deg and sog_vals[i] < TRANSIT_SOG_MIN:
            uturn_count += 1

    return {
        'uturn_count': uturn_count,
        'trawl_suspected': uturn_count >= min_uturn_count,
    }