gc/kcg-ai-monitoring
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코드 이슈 풀 리퀘스트 Actions 위키 활동
474e672683
kcg-ai-monitoring/prediction/output/stats_aggregator.py
htlee 474e672683 feat: S3 prediction 신규 출력 모듈 5종 + scheduler 통합 
분석 사이클 완료 후 자동 실행되는 출력 파이프라인:
- event_generator: 분석결과 → 이벤트 자동 생성 (7개 룰, 카테고리별 dedup)
- violation_classifier: 위반 유형 라벨링 (EEZ/DARK/MMSI/TRANSSHIP/GEAR/RISK)
- kpi_writer: 실시간 KPI 6개 갱신 (오늘 기준 카운트)
- stats_aggregator: hourly/daily/monthly 사전 집계 (UPSERT)
- alert_dispatcher: CRITICAL/HIGH 이벤트 자동 알림 생성

scheduler.py에 출력 모듈 통합 (분석 8단계 완료 후 실행, non-fatal)
DB 연동 테스트 통과 (alerts 8건 생성, KPI tracking_active=2)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-07 13:00:50 +09:00

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8.9 KiB
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"""
통계 사전 집계 — prediction_stats_hourly/daily/monthly 갱신.
hourly: 매 분석 사이클마다 (최근 48h 보존)
daily: 매일 01:00 또는 분석 사이클 후
monthly: daily 합산
"""
import json
import logging
from datetime import date, datetime, timedelta, timezone
from typing import Optional
from config import qualified_table
from db.kcgdb import get_conn
logger = logging.getLogger(__name__)
STATS_HOURLY = qualified_table('prediction_stats_hourly')
STATS_DAILY = qualified_table('prediction_stats_daily')
STATS_MONTHLY = qualified_table('prediction_stats_monthly')
VAR_TABLE = qualified_table('vessel_analysis_results')
EVENTS_TABLE = qualified_table('prediction_events')
ENF_TABLE = qualified_table('enforcement_records')
def _jsonb(d: dict) -> str:
return json.dumps(d, ensure_ascii=False)
def aggregate_hourly(target_hour: Optional[datetime] = None) -> dict:
"""현재 시간 기준 hourly 집계."""
now = target_hour or datetime.now(timezone.utc)
hour_start = now.replace(minute=0, second=0, microsecond=0)
hour_end = hour_start + timedelta(hours=1)
with get_conn() as conn:
cur = conn.cursor()
# 탐지 수
cur.execute(
f"SELECT COUNT(*) FROM {VAR_TABLE} WHERE analyzed_at >= %s AND analyzed_at < %s",
(hour_start, hour_end)
)
total = cur.fetchone()[0] or 0
# 위험 레벨별
cur.execute(
f"""SELECT risk_level, COUNT(*) FROM {VAR_TABLE}
WHERE analyzed_at >= %s AND analyzed_at < %s AND risk_level IS NOT NULL
GROUP BY risk_level""",
(hour_start, hour_end)
)
by_risk = dict(cur.fetchall())
# 이벤트 수
cur.execute(
f"SELECT COUNT(*) FROM {EVENTS_TABLE} WHERE occurred_at >= %s AND occurred_at < %s",
(hour_start, hour_end)
)
events = cur.fetchone()[0] or 0
# CRITICAL 이벤트
cur.execute(
f"""SELECT COUNT(*) FROM {EVENTS_TABLE}
WHERE occurred_at >= %s AND occurred_at < %s AND level = 'CRITICAL'""",
(hour_start, hour_end)
)
critical = cur.fetchone()[0] or 0
cur.execute(
f"""INSERT INTO {STATS_HOURLY}
(stat_hour, total_detections, by_risk_level, event_count, critical_count, updated_at)
VALUES (%s, %s, %s, %s, %s, %s)
ON CONFLICT (stat_hour) DO UPDATE SET
total_detections = EXCLUDED.total_detections,
by_risk_level = EXCLUDED.by_risk_level,
event_count = EXCLUDED.event_count,
critical_count = EXCLUDED.critical_count,
updated_at = EXCLUDED.updated_at""",
(hour_start, total, _jsonb(by_risk), events, critical, now)
)
# 48시간 이전 정리
cutoff = now - timedelta(hours=48)
cur.execute(f"DELETE FROM {STATS_HOURLY} WHERE stat_hour < %s", (cutoff,))
conn.commit()
result = {'hour': hour_start.isoformat(), 'detections': total, 'events': events}
logger.info(f'stats_aggregator hourly: {result}')
return result
def aggregate_daily(target_date: Optional[date] = None) -> dict:
"""지정 날짜 기준 daily 집계."""
d = target_date or date.today()
day_start = datetime(d.year, d.month, d.day, tzinfo=timezone.utc)
day_end = day_start + timedelta(days=1)
now = datetime.now(timezone.utc)
with get_conn() as conn:
cur = conn.cursor()
# 총 탐지
cur.execute(
f"SELECT COUNT(*) FROM {VAR_TABLE} WHERE analyzed_at >= %s AND analyzed_at < %s",
(day_start, day_end)
)
total = cur.fetchone()[0] or 0
# 위반 유형별 (unnest)
cur.execute(
f"""SELECT unnest(violation_categories) AS vt, COUNT(*)
FROM {VAR_TABLE}
WHERE analyzed_at >= %s AND analyzed_at < %s AND violation_categories IS NOT NULL
GROUP BY vt""",
(day_start, day_end)
)
by_violation = dict(cur.fetchall())
# 위험 레벨별
cur.execute(
f"""SELECT risk_level, COUNT(*) FROM {VAR_TABLE}
WHERE analyzed_at >= %s AND analyzed_at < %s AND risk_level IS NOT NULL
GROUP BY risk_level""",
(day_start, day_end)
)
by_risk = dict(cur.fetchall())
# 이벤트
cur.execute(
f"SELECT COUNT(*) FROM {EVENTS_TABLE} WHERE occurred_at >= %s AND occurred_at < %s",
(day_start, day_end)
)
event_count = cur.fetchone()[0] or 0
cur.execute(
f"""SELECT COUNT(*) FROM {EVENTS_TABLE}
WHERE occurred_at >= %s AND occurred_at < %s AND level = 'CRITICAL'""",
(day_start, day_end)
)
critical = cur.fetchone()[0] or 0
# 단속
cur.execute(
f"SELECT COUNT(*) FROM {ENF_TABLE} WHERE enforced_at >= %s AND enforced_at < %s",
(day_start, day_end)
)
enf_count = cur.fetchone()[0] or 0
# 오탐
cur.execute(
f"""SELECT COUNT(*) FROM {EVENTS_TABLE}
WHERE occurred_at >= %s AND occurred_at < %s AND status = 'FALSE_POSITIVE'""",
(day_start, day_end)
)
fp = cur.fetchone()[0] or 0
# AI 정확도
accuracy = round((1 - fp / max(event_count, 1)) * 100, 2) if event_count > 0 else None
cur.execute(
f"""INSERT INTO {STATS_DAILY}
(stat_date, total_detections, by_violation_type, by_risk_level,
event_count, critical_event_count, enforcement_count,
false_positive_count, ai_accuracy_pct, updated_at)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
ON CONFLICT (stat_date) DO UPDATE SET
total_detections = EXCLUDED.total_detections,
by_violation_type = EXCLUDED.by_violation_type,
by_risk_level = EXCLUDED.by_risk_level,
event_count = EXCLUDED.event_count,
critical_event_count = EXCLUDED.critical_event_count,
enforcement_count = EXCLUDED.enforcement_count,
false_positive_count = EXCLUDED.false_positive_count,
ai_accuracy_pct = EXCLUDED.ai_accuracy_pct,
updated_at = EXCLUDED.updated_at""",
(d, total, _jsonb(by_violation), _jsonb(by_risk),
event_count, critical, enf_count, fp, accuracy, now)
)
conn.commit()
result = {'date': d.isoformat(), 'detections': total, 'events': event_count, 'accuracy': accuracy}
logger.info(f'stats_aggregator daily: {result}')
return result
def aggregate_monthly(target_month: Optional[date] = None) -> dict:
"""지정 월 기준 monthly 집계 (daily 합산)."""
d = target_month or date.today().replace(day=1)
month_start = d.replace(day=1)
if month_start.month == 12:
month_end = month_start.replace(year=month_start.year + 1, month=1)
else:
month_end = month_start.replace(month=month_start.month + 1)
now = datetime.now(timezone.utc)
with get_conn() as conn:
cur = conn.cursor()
cur.execute(
f"""SELECT
COALESCE(SUM(total_detections), 0),
COALESCE(SUM(event_count), 0),
COALESCE(SUM(critical_event_count), 0),
COALESCE(SUM(enforcement_count), 0),
COALESCE(SUM(false_positive_count), 0)
FROM {STATS_DAILY}
WHERE stat_date >= %s AND stat_date < %s""",
(month_start, month_end)
)
row = cur.fetchone()
total_det, evt, crit, enf, fp = row
accuracy = round((1 - fp / max(evt, 1)) * 100, 2) if evt > 0 else None
cur.execute(
f"""INSERT INTO {STATS_MONTHLY}
(stat_month, total_detections, total_enforcements,
event_count, critical_event_count, false_positive_count,
ai_accuracy_pct, updated_at)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
ON CONFLICT (stat_month) DO UPDATE SET
total_detections = EXCLUDED.total_detections,
total_enforcements = EXCLUDED.total_enforcements,
event_count = EXCLUDED.event_count,
critical_event_count = EXCLUDED.critical_event_count,
false_positive_count = EXCLUDED.false_positive_count,
ai_accuracy_pct = EXCLUDED.ai_accuracy_pct,
updated_at = EXCLUDED.updated_at""",
(month_start, total_det, enf, evt, crit, fp, accuracy, now)
)
conn.commit()
result = {'month': month_start.isoformat(), 'detections': total_det, 'enforcements': enf}
logger.info(f'stats_aggregator monthly: {result}')
return result
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