docs: prediction 2차 개편 릴리즈 노트 + hourly snapshot 스크립트
- RELEASE-NOTES [Unreleased] 섹션에 dark 의심 점수화 + transship 재설계 변경사항 추가 - prediction/scripts/hourly-analysis-snapshot.sh: 시간별 상태 스냅샷 수집 (25개 섹션)
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## [Unreleased]
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### 추가
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- **Dark Vessel 의심 점수화** — 기존 "gap≥30분→dark" 이분법에서 8가지 패턴 기반 0~100점 점수 산출 + CRITICAL/HIGH/WATCH/NONE 등급 분류
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- P1 이동 중 OFF / P2 민감 수역 / P3 반복 이력(7일) / P4 거리 비정상 / P5 주간 조업 OFF / P6 직전 이상행동 / P7 무허가 / P8 장기 gap
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- 한국 AIS 수신 커버리지 밖은 자연 gap 가능성으로 감점
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- 어구(gear) AIS, 한국 선박(440/441) dark 판정 완전 제외
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- `features` JSONB에 `dark_suspicion_score`, `dark_patterns`, `dark_tier`, `dark_history_7d` 등 저장
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- **Transshipment 베테랑 관점 재설계** — 점수 기반 40~130점 판정 (CRITICAL/HIGH/WATCH)
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- SOG 2.0→1.0kn, 근접 110→77m, 지속 60→45분 + gap tolerance 2사이클
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- 한국 EEZ 관할 수역 이내 필수, 어구/여객/군함/유조 제외
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- 야간·무허가·COG편차·장기지속·영해위치 가점
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- pair_history 구조 확장: `{'first_seen', 'last_seen', 'miss_count'}` (GPS 노이즈 내성)
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### 변경
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- **stats_aggregator hourly**: UTC→KST hour boundary 전환, `by_category`/`by_zone` JSONB 집계 추가
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- **event_generator 룰 전면 재편**:
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- EEZ_INTRUSION: 실측 zone_code(TERRITORIAL_SEA/CONTIGUOUS_ZONE/ZONE_*) 기반 신규 3룰
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- HIGH_RISK_VESSEL: risk.py CRITICAL 임계값과 동일(70점) 연동, 50~69점 MEDIUM 분리
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- DARK_VESSEL: features.dark_tier 기반 CRITICAL/HIGH 룰 (기존 gap>60 룰 교체)
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- ILLEGAL_TRANSSHIP: features.transship_tier 기반 CRITICAL/HIGH 룰 (기존 단순 룰 교체)
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- break 제거 → mmsi당 복수 카테고리 동시 매칭 가능
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- dedup 윈도우 prime 값 분산 (60/120/360→67/127/367 등, 정시 일제 만료 회피)
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- **lightweight path 신호 보강**: vessel_store 24h 궤적으로 dark/spoofing/speed_jump 산출
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- `compute_lightweight_risk_score`에 dark gap + spoofing 가점 추가 (max 60→100)
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- `_gear_re` 중복 제거 → `fleet_tracker.GEAR_PATTERN` SSOT 통합
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- `AnalysisResult.to_db_tuple` features sanitize: 중첩 dict/list 지원
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### 수정
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- `prediction_stats_hourly.by_category`/`by_zone` 영구 NULL → 채움
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- `prediction_stats_hourly.critical_count` 영구 0 → CRITICAL 이벤트 수 반영
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- `prediction_events` 카테고리 2종(ZONE_DEPARTURE/ILLEGAL_TRANSSHIP)만 → 6종 이상
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- KPI `dark_vessel`/`eez_violation` 영구 0 → 정상 집계
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- 이벤트 홀수/짝수시 4~22배 진폭 → dedup prime 분산으로 완화
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- dark 과다 판정 해소: 핫픽스(한국 수신 영역 필터) + 2차(의심 점수화)
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- transship 과다 판정 해소: 사이클당 2,400~12,600 → CRITICAL/HIGH/WATCH 점수 기반
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## [2026-04-08]
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### 추가
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340
prediction/scripts/hourly-analysis-snapshot.sh
Executable file
340
prediction/scripts/hourly-analysis-snapshot.sh
Executable file
@ -0,0 +1,340 @@
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#!/bin/bash
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# prediction 시간당 상태 스냅샷 수집기
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# 실행 환경: redis-211 서버 (prediction 서비스 호스트)
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# cron: 0 * * * * /home/apps/kcg-ai-prediction/scripts/hourly-analysis-snapshot.sh
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#
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# 출력: /home/apps/kcg-ai-prediction/data/hourly-analysis/YYYYMMDD-HHMM.txt
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# 수집 대상:
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# 1. vessel_analysis_results 전체 분포 (pipeline vs lightweight, dark/spoof/risk)
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# 2. zone_code 분포 + dark 교차 집계
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# 3. dark vessel gap_duration_min 분포
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# 4. dark vessel activity_state 분포
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# 5. dark vessel 상세 샘플 20건 (mmsi/zone/gap/lat/lon)
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# 6. prediction_events 카테고리×level 분포
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# 7. prediction_stats_hourly 최근 2건
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# 8. prediction_kpi_realtime 전체
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# 9. risk_score 히스토그램
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# 10. 직전 1시간 사이클 로그 (journalctl)
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set -u
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OUTDIR=/home/apps/kcg-ai-prediction/data/hourly-analysis
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mkdir -p "$OUTDIR"
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STAMP=$(date '+%Y%m%d-%H%M')
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OUT="$OUTDIR/$STAMP.txt"
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export PGPASSWORD=Kcg2026ai
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PSQL="psql -U kcg-app -d kcgaidb -h 211.208.115.83 -P pager=off"
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{
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echo "# prediction hourly snapshot"
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echo "# generated: $(date '+%Y-%m-%d %H:%M:%S %Z')"
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echo "# host: $(hostname)"
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echo ""
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$PSQL << 'SQL'
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\echo === 1. VESSEL_ANALYSIS overview (last 1h) ===
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SELECT count(*) total,
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count(*) FILTER (WHERE vessel_type != 'UNKNOWN') pipeline_path,
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count(*) FILTER (WHERE vessel_type = 'UNKNOWN') lightweight_path,
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count(*) FILTER (WHERE is_dark) dark,
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count(*) FILTER (WHERE spoofing_score > 0.5) spoof_hi,
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count(*) FILTER (WHERE spoofing_score > 0) spoof_any,
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count(*) FILTER (WHERE risk_score >= 70) crit_score,
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count(*) FILTER (WHERE risk_level='CRITICAL') crit_lvl,
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count(*) FILTER (WHERE risk_level='HIGH') high_lvl,
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max(risk_score) max_risk,
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round(avg(risk_score)::numeric, 2) avg_risk,
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count(*) FILTER (WHERE transship_suspect) transship
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour';
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\echo
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\echo === 2. ZONE × DARK distribution ===
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SELECT zone_code,
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count(*) total,
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count(*) FILTER (WHERE is_dark) dark,
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count(*) FILTER (WHERE risk_score >= 70) crit,
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round(avg(risk_score)::numeric, 1) avg_risk
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour'
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GROUP BY zone_code ORDER BY total DESC;
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\echo
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\echo === 3. DARK GAP distribution (all vessels in last 1h) ===
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SELECT CASE
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WHEN gap_duration_min < 30 THEN 'a_lt30'
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WHEN gap_duration_min < 60 THEN 'b_30-59'
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WHEN gap_duration_min < 120 THEN 'c_60-119'
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WHEN gap_duration_min < 360 THEN 'd_120-359'
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WHEN gap_duration_min < 1440 THEN 'e_360-1439'
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ELSE 'f_gte1440' END gap_bucket,
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count(*) total,
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count(*) FILTER (WHERE is_dark) dark,
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count(*) FILTER (WHERE is_dark AND vessel_type='UNKNOWN') dark_lightweight,
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count(*) FILTER (WHERE is_dark AND vessel_type!='UNKNOWN') dark_pipeline
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour'
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GROUP BY gap_bucket ORDER BY gap_bucket;
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\echo
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\echo === 4. DARK vessels by activity_state ===
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SELECT activity_state, count(*), round(avg(gap_duration_min)::numeric, 0) avg_gap_min
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour' AND is_dark
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GROUP BY activity_state ORDER BY count DESC;
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\echo
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\echo === 5. DARK sample top 20 by gap (mmsi/zone/gap/state) ===
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SELECT mmsi, zone_code, activity_state, gap_duration_min, risk_score
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FROM (
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SELECT DISTINCT ON (mmsi) mmsi, zone_code, activity_state, gap_duration_min,
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risk_score, analyzed_at
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour' AND is_dark
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ORDER BY mmsi, analyzed_at DESC
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) latest
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ORDER BY gap_duration_min DESC LIMIT 20;
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\echo
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\echo === 6. PREDICTION_EVENTS last 1h by category×level ===
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SELECT category, level, count(*) cnt
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FROM kcg.prediction_events
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WHERE created_at > now() - interval '1 hour'
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GROUP BY category, level ORDER BY cnt DESC;
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\echo
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\echo === 7. STATS_HOURLY latest 3 rows ===
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SELECT stat_hour, total_detections, event_count, critical_count,
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by_category::text, by_zone::text
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FROM kcg.prediction_stats_hourly
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ORDER BY stat_hour DESC LIMIT 3;
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\echo
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\echo === 8. KPI REALTIME ===
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SELECT kpi_key, value, trend, delta_pct, updated_at
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FROM kcg.prediction_kpi_realtime ORDER BY kpi_key;
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\echo
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\echo === 9. RISK_SCORE histogram (last 1h) ===
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SELECT CASE
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WHEN risk_score < 10 THEN 'a_0-9'
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WHEN risk_score < 30 THEN 'b_10-29'
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WHEN risk_score < 50 THEN 'c_30-49'
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WHEN risk_score < 70 THEN 'd_50-69'
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WHEN risk_score < 90 THEN 'e_70-89'
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ELSE 'f_90-100' END bucket,
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count(*) cnt,
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count(*) FILTER (WHERE vessel_type='UNKNOWN') lightweight
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour'
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GROUP BY bucket ORDER BY bucket;
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\echo
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\echo === 10. TRANSSHIP, SPOOFING, FLEET 요약 ===
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SELECT
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count(*) FILTER (WHERE transship_suspect) transship_ct,
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count(*) FILTER (WHERE spoofing_score > 0.7) spoof_gt070,
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count(*) FILTER (WHERE spoofing_score > 0.5 AND spoofing_score <= 0.7) spoof_050_070,
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count(*) FILTER (WHERE speed_jump_count > 0) speed_jumps,
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count(*) FILTER (WHERE fleet_is_leader) fleet_leader,
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count(DISTINCT fleet_cluster_id) FILTER (WHERE fleet_cluster_id IS NOT NULL AND fleet_cluster_id > 0) fleet_clusters
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour';
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\echo
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\echo === 10-1. FLEET_ROLE distribution ===
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SELECT fleet_role, count(*), count(DISTINCT mmsi) uniq_mmsi
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour'
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GROUP BY fleet_role ORDER BY count DESC;
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\echo
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\echo === 10-2. TRANSSHIPMENT duration histogram ===
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SELECT CASE
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WHEN transship_duration_min < 5 THEN 'a_0-4'
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WHEN transship_duration_min < 15 THEN 'b_5-14'
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WHEN transship_duration_min < 30 THEN 'c_15-29'
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WHEN transship_duration_min < 60 THEN 'd_30-59'
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WHEN transship_duration_min < 120 THEN 'e_60-119'
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ELSE 'f_gte120' END bucket, count(*)
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour' AND transship_suspect
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GROUP BY bucket ORDER BY bucket;
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\echo
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\echo === G1. PIPELINE vessel_type (어구 타입) distribution ===
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SELECT vessel_type,
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count(*),
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count(*) FILTER (WHERE fishing_pct > 50) active_fishing,
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round(avg(fishing_pct)::numeric, 1) avg_fish_pct,
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round(avg(ucaf_score)::numeric, 3) avg_ucaf,
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round(avg(ucft_score)::numeric, 3) avg_ucft,
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round(avg(risk_score)::numeric, 1) avg_risk
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour'
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GROUP BY vessel_type ORDER BY count DESC;
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\echo
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\echo === G2. ACTIVITY_STATE distribution (전체) ===
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SELECT activity_state, count(*),
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count(*) FILTER (WHERE vessel_type != 'UNKNOWN') pipeline,
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round(avg(risk_score)::numeric, 1) avg_risk
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour'
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GROUP BY activity_state ORDER BY count DESC;
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\echo
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\echo === G3. GEAR_GROUP_PARENT_RESOLUTION status + confidence ===
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SELECT status, count(*),
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round(avg(confidence)::numeric, 3) avg_conf,
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round(avg(top_score)::numeric, 3) avg_top,
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round(avg(score_margin)::numeric, 3) avg_margin,
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round(avg(stable_cycles)::numeric, 1) avg_stable
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FROM kcg.gear_group_parent_resolution
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GROUP BY status ORDER BY count DESC;
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\echo
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\echo === G3-1. PARENT_RESOLUTION decision_source ===
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SELECT coalesce(decision_source, '(null)') ds, status, count(*)
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FROM kcg.gear_group_parent_resolution
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GROUP BY ds, status ORDER BY count DESC LIMIT 20;
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\echo
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\echo === G4. GEAR_GROUP_EPISODES (active) ===
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SELECT status, continuity_source, count(*),
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round(avg(continuity_score)::numeric, 3) avg_cont,
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round(avg(current_member_count)::numeric, 1) avg_members,
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round(avg(EXTRACT(EPOCH FROM (now() - first_seen_at))/3600)::numeric, 1) avg_age_h
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FROM kcg.gear_group_episodes
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WHERE last_seen_at > now() - interval '24 hours'
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GROUP BY status, continuity_source ORDER BY count DESC;
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\echo
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\echo === G5. GEAR_CORRELATION_SCORES (current_score) 분포 ===
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SELECT CASE
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WHEN current_score < 0.3 THEN 'a_lt0.3'
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WHEN current_score < 0.5 THEN 'b_0.3-0.5'
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WHEN current_score < 0.7 THEN 'c_0.5-0.7'
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WHEN current_score < 0.85 THEN 'd_0.7-0.85'
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ELSE 'e_gte0.85' END bucket,
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count(*),
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count(DISTINCT group_key) uniq_groups,
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count(DISTINCT target_mmsi) uniq_targets,
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round(avg(streak_count)::numeric, 1) avg_streak
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FROM kcg.gear_correlation_scores
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WHERE updated_at > now() - interval '1 hour'
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GROUP BY bucket ORDER BY bucket;
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\echo
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\echo === G5-1. CORRELATION freeze_state ===
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SELECT freeze_state, count(*), round(avg(current_score)::numeric, 3) avg_score
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FROM kcg.gear_correlation_scores
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WHERE updated_at > now() - interval '1 hour'
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GROUP BY freeze_state ORDER BY count DESC;
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\echo
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\echo === G6. GROUP_POLYGON_SNAPSHOTS (last 1h, by type × zone) ===
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SELECT group_type,
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coalesce(zone_id, '(null)') zone,
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count(*),
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round(avg(area_sq_nm)::numeric, 2) avg_area_nm,
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round(avg(member_count)::numeric, 1) avg_members
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FROM kcg.group_polygon_snapshots
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WHERE snapshot_time > now() - interval '1 hour'
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GROUP BY group_type, zone_id ORDER BY count DESC LIMIT 20;
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\echo
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\echo === G7. IS_PERMITTED breakdown (lightweight path 기준) ===
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SELECT
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count(*) FILTER (WHERE vessel_type != 'UNKNOWN') pipeline_ct,
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count(*) FILTER (WHERE vessel_type = 'UNKNOWN') lightweight_ct,
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count(DISTINCT mmsi) FILTER (WHERE risk_score >= 20) risk_gte20_uniq,
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count(DISTINCT mmsi) FILTER (WHERE risk_score >= 50) risk_gte50_uniq,
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count(DISTINCT mmsi) FILTER (WHERE risk_score >= 70) risk_gte70_uniq
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour';
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\echo
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\echo === G8. VIOLATION_CATEGORIES (last 1h, unnest) ===
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SELECT unnest(violation_categories) vcat, count(*)
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '1 hour' AND violation_categories IS NOT NULL
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GROUP BY vcat ORDER BY count DESC LIMIT 20;
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\echo
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\echo === G9. PREDICTION_EVENTS 24h hourly trend (KST) ===
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SELECT date_trunc('hour', occurred_at AT TIME ZONE 'Asia/Seoul') hr,
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count(*) tot,
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count(*) FILTER (WHERE category='DARK_VESSEL') dark,
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count(*) FILTER (WHERE category='ILLEGAL_TRANSSHIP') transship,
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count(*) FILTER (WHERE category='EEZ_INTRUSION') eez,
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count(*) FILTER (WHERE category='HIGH_RISK_VESSEL') high_risk,
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count(*) FILTER (WHERE category='ZONE_DEPARTURE') zone_dep,
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count(*) FILTER (WHERE level='CRITICAL') critical
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FROM kcg.prediction_events
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WHERE created_at > now() - interval '24 hours'
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GROUP BY hr ORDER BY hr DESC LIMIT 25;
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\echo
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\echo === G10. PREDICTION_ALERTS (last 1h) ===
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SELECT channel, delivery_status, count(*),
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round(avg(ai_confidence)::numeric, 3) avg_conf
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FROM kcg.prediction_alerts
|
||||
WHERE sent_at > now() - interval '1 hour'
|
||||
GROUP BY channel, delivery_status ORDER BY count DESC;
|
||||
|
||||
SQL
|
||||
|
||||
echo ""
|
||||
echo "=== 11. DARK SAMPLE latest position (snpdb t_vessel_tracks_5min) ==="
|
||||
# Cross-database 불가 → 두 단계: kcgaidb에서 mmsi 추출 → snpdb에 별도 쿼리
|
||||
DARK_MMSIS=$(PGPASSWORD=Kcg2026ai psql -U kcg-app -d kcgaidb -h 211.208.115.83 -tA -c "
|
||||
SELECT string_agg(quote_literal(mmsi), ',')
|
||||
FROM (SELECT DISTINCT ON (mmsi) mmsi, gap_duration_min, analyzed_at
|
||||
FROM kcg.vessel_analysis_results
|
||||
WHERE analyzed_at > now() - interval '1 hour' AND is_dark
|
||||
ORDER BY mmsi, analyzed_at DESC) v
|
||||
WHERE v.mmsi IN (
|
||||
SELECT mmsi FROM (SELECT DISTINCT ON (mmsi) mmsi, gap_duration_min, analyzed_at
|
||||
FROM kcg.vessel_analysis_results
|
||||
WHERE analyzed_at > now() - interval '1 hour' AND is_dark
|
||||
ORDER BY mmsi, analyzed_at DESC) x
|
||||
ORDER BY gap_duration_min DESC LIMIT 20
|
||||
);" 2>/dev/null)
|
||||
|
||||
if [ -n "$DARK_MMSIS" ]; then
|
||||
PGPASSWORD='snp#8932' psql -U snp -d snpdb -h 211.208.115.83 -P pager=off -c "
|
||||
SELECT DISTINCT ON (mmsi) mmsi, time_bucket,
|
||||
round(ST_Y(ST_EndPoint(track_geom))::numeric, 4) lat,
|
||||
round(ST_X(ST_EndPoint(track_geom))::numeric, 4) lon
|
||||
FROM signal.t_vessel_tracks_5min
|
||||
WHERE mmsi IN ($DARK_MMSIS) AND time_bucket > now() - interval '24 hours'
|
||||
ORDER BY mmsi, time_bucket DESC;
|
||||
" 2>&1 | head -30
|
||||
else
|
||||
echo "(no dark vessels in last 1h)"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "=== 12. PREDICTION_EVENTS occurred_at distribution by 10-min buckets ==="
|
||||
PGPASSWORD=Kcg2026ai psql -U kcg-app -d kcgaidb -h 211.208.115.83 -P pager=off -c "
|
||||
SELECT date_trunc('hour', occurred_at) + (date_part('minute', occurred_at)::int / 10 * interval '10 minutes') bucket,
|
||||
category, count(*)
|
||||
FROM kcg.prediction_events
|
||||
WHERE created_at > now() - interval '1 hour'
|
||||
GROUP BY bucket, category
|
||||
ORDER BY bucket DESC, count DESC LIMIT 30;
|
||||
" 2>&1
|
||||
|
||||
echo ""
|
||||
echo "=== 13. CYCLE LOG (last 65 min) ==="
|
||||
journalctl -u kcg-ai-prediction --since '65 minutes ago' --no-pager 2>/dev/null | \
|
||||
grep -E 'lightweight analysis|event_generator:|stats_aggregator hourly|kpi_writer:|analysis cycle:|ERROR|Traceback' | \
|
||||
tail -60
|
||||
|
||||
echo ""
|
||||
echo "=== END ==="
|
||||
} > "$OUT" 2>&1
|
||||
|
||||
echo "[snapshot] saved: $OUT"
|
||||
불러오는 중...
Reference in New Issue
Block a user