327 lines
13 KiB
Bash
327 lines
13 KiB
Bash
#!/bin/bash
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# prediction 알고리즘 진단 스냅샷 수집기 (5분 주기, 수동 종료까지 연속 실행)
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#
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# 용도: 알고리즘 재설계 후 동작 검증용. 단순 집계가 아닌 개별 판정 과정 추적.
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# 실행: nohup bash /home/apps/kcg-ai-prediction/scripts/diagnostic-snapshot.sh &
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# 종료: kill $(cat /home/apps/kcg-ai-prediction/data/diag/diag.pid)
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# 출력: /home/apps/kcg-ai-prediction/data/diag/YYYYMMDD-HHMM.txt
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set -u
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OUTDIR=/home/apps/kcg-ai-prediction/data/diag
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mkdir -p "$OUTDIR"
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echo $$ > "$OUTDIR/diag.pid"
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export PGPASSWORD=Kcg2026ai
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PSQL="psql -U kcg-app -d kcgaidb -h 211.208.115.83 -P pager=off -x"
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PSQL_TABLE="psql -U kcg-app -d kcgaidb -h 211.208.115.83 -P pager=off"
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INTERVAL_SEC=300 # 5분
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while true; do
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STAMP=$(date '+%Y%m%d-%H%M')
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OUT="$OUTDIR/$STAMP.txt"
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{
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echo "###################################################################"
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echo "# PREDICTION DIAGNOSTIC 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 "# interval: ${INTERVAL_SEC}s"
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echo "###################################################################"
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#===================================================================
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# PART 1: 종합 지표
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#===================================================================
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echo ""
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echo "================================================================="
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echo "PART 1: 종합 지표 (last 5min)"
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echo "================================================================="
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$PSQL_TABLE << 'SQL'
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SELECT count(*) total,
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count(*) FILTER (WHERE vessel_type != 'UNKNOWN') pipeline,
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count(*) FILTER (WHERE vessel_type = 'UNKNOWN') lightweight,
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count(*) FILTER (WHERE is_dark) dark,
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count(*) FILTER (WHERE transship_suspect) transship,
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count(*) FILTER (WHERE risk_level='CRITICAL') crit,
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count(*) FILTER (WHERE risk_level='HIGH') high,
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round(avg(risk_score)::numeric, 1) avg_risk,
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max(risk_score) max_risk,
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round(count(*) FILTER (WHERE is_dark)::numeric / NULLIF(count(*), 0) * 100, 1) AS dark_pct
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes';
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SQL
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#===================================================================
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# PART 2: 다크베셀 심층 진단
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#===================================================================
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echo ""
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echo "================================================================="
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echo "PART 2: DARK VESSEL 심층 진단"
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echo "================================================================="
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echo ""
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echo "--- 2-1. dark_suspicion_score 히스토그램 ---"
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$PSQL_TABLE << 'SQL'
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SELECT CASE
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WHEN (features->>'dark_suspicion_score')::int >= 90 THEN 'a_90-100 (CRITICAL_HIGH)'
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WHEN (features->>'dark_suspicion_score')::int >= 70 THEN 'b_70-89 (CRITICAL)'
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WHEN (features->>'dark_suspicion_score')::int >= 50 THEN 'c_50-69 (HIGH)'
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WHEN (features->>'dark_suspicion_score')::int >= 30 THEN 'd_30-49 (WATCH)'
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WHEN (features->>'dark_suspicion_score')::int >= 1 THEN 'e_1-29 (NONE_SCORED)'
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ELSE 'f_0 (NOT_DARK)'
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END bucket,
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count(*) cnt,
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round(avg(gap_duration_min)::numeric, 0) avg_gap_min,
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round(avg(risk_score)::numeric, 1) avg_risk
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes'
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AND is_dark = true
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GROUP BY bucket ORDER BY bucket;
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SQL
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echo ""
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echo "--- 2-2. dark_patterns 발동 빈도 (어떤 규칙이 얼마나 적용되는지) ---"
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$PSQL_TABLE << 'SQL'
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SELECT pattern,
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count(*) cnt,
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round(count(*)::numeric / NULLIF((SELECT count(*) FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes' AND is_dark), 0) * 100, 1) AS pct
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FROM kcg.vessel_analysis_results,
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LATERAL jsonb_array_elements_text(features->'dark_patterns') AS pattern
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WHERE analyzed_at > now() - interval '5 minutes'
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AND is_dark = true
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GROUP BY pattern ORDER BY cnt DESC;
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SQL
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echo ""
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echo "--- 2-3. P9 선종별 dark 분포 (신규 패턴 검증) ---"
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$PSQL_TABLE << 'SQL'
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SELECT
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CASE WHEN features->>'dark_patterns' LIKE '%fishing_vessel_dark%' THEN 'FISHING(+10)'
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WHEN features->>'dark_patterns' LIKE '%cargo_natural_gap%' THEN 'CARGO(-10)'
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ELSE 'NO_KIND_EFFECT' END AS p9_effect,
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count(*) cnt,
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round(avg((features->>'dark_suspicion_score')::int)::numeric, 1) avg_score,
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round(avg(gap_duration_min)::numeric, 0) avg_gap
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes'
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AND is_dark = true
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GROUP BY p9_effect ORDER BY cnt DESC;
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SQL
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echo ""
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echo "--- 2-4. P10 항해상태 dark 분포 (신규 패턴 검증) ---"
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$PSQL_TABLE << 'SQL'
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SELECT
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CASE WHEN features->>'dark_patterns' LIKE '%underway_deliberate_off%' THEN 'UNDERWAY_OFF(+20)'
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WHEN features->>'dark_patterns' LIKE '%anchored_natural_gap%' THEN 'ANCHORED(-15)'
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ELSE 'NO_STATUS_EFFECT' END AS p10_effect,
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count(*) cnt,
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round(avg((features->>'dark_suspicion_score')::int)::numeric, 1) avg_score
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes'
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AND is_dark = true
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GROUP BY p10_effect ORDER BY cnt DESC;
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SQL
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echo ""
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echo "--- 2-5. P11 heading/COG 불일치 (신규 패턴 검증) ---"
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$PSQL_TABLE << 'SQL'
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SELECT
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CASE WHEN features->>'dark_patterns' LIKE '%heading_cog_mismatch%' THEN 'MISMATCH(+15)'
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ELSE 'NO_MISMATCH' END AS p11_effect,
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count(*) cnt,
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round(avg((features->>'dark_suspicion_score')::int)::numeric, 1) avg_score
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes'
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AND is_dark = true
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GROUP BY p11_effect ORDER BY cnt DESC;
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SQL
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echo ""
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echo "--- 2-6. GAP 구간별 dark_tier 교차표 (임계값 100분 검증) ---"
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$PSQL_TABLE << 'SQL'
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SELECT CASE
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WHEN gap_duration_min < 100 THEN 'a_lt100 (NOT_DARK 예상)'
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WHEN gap_duration_min < 180 THEN 'b_100-179'
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WHEN gap_duration_min < 360 THEN 'c_180-359'
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WHEN gap_duration_min < 720 THEN 'd_360-719'
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ELSE 'e_gte720' 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 features->>'dark_tier' = 'CRITICAL') crit,
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count(*) FILTER (WHERE features->>'dark_tier' = 'HIGH') high,
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count(*) FILTER (WHERE features->>'dark_tier' = 'WATCH') watch,
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count(*) FILTER (WHERE features->>'dark_tier' = 'NONE') tier_none
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes'
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GROUP BY gap_bucket ORDER BY gap_bucket;
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SQL
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echo ""
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echo "--- 2-7. CRITICAL dark 상위 10건 (개별 판정 상세) ---"
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$PSQL_TABLE << 'SQL'
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SELECT mmsi, gap_duration_min, zone_code, activity_state,
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(features->>'dark_suspicion_score')::int AS score,
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features->>'dark_tier' AS tier,
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features->>'dark_patterns' AS patterns,
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risk_score, risk_level
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes'
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AND features->>'dark_tier' = 'CRITICAL'
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ORDER BY (features->>'dark_suspicion_score')::int DESC
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LIMIT 10;
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SQL
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#===================================================================
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# PART 3: 환적 탐지 심층 진단
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#===================================================================
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echo ""
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echo "================================================================="
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echo "PART 3: TRANSSHIPMENT 심층 진단"
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echo "================================================================="
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echo ""
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echo "--- 3-1. 환적 의심 건수 + 점수 분포 ---"
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$PSQL_TABLE << 'SQL'
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SELECT count(*) total_suspects,
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count(*) FILTER (WHERE (features->>'transship_score')::numeric >= 70) critical,
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count(*) FILTER (WHERE (features->>'transship_score')::numeric >= 50
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AND (features->>'transship_score')::numeric < 70) high,
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round(avg((features->>'transship_score')::numeric)::numeric, 1) avg_score,
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round(avg(transship_duration_min)::numeric, 0) avg_duration_min
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes'
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AND transship_suspect = true;
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SQL
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echo ""
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echo "--- 3-2. 환적 의심 개별 건 상세 (전체) ---"
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$PSQL_TABLE << 'SQL'
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SELECT mmsi, transship_pair_mmsi AS pair_mmsi,
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transship_duration_min AS dur_min,
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(features->>'transship_score')::numeric AS score,
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features->>'transship_tier' AS tier,
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zone_code,
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activity_state,
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risk_score
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FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes'
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AND transship_suspect = true
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ORDER BY (features->>'transship_score')::numeric DESC;
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SQL
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echo ""
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echo "--- 3-3. 환적 후보 선종 분포 (Stage 1 이종 쌍 검증) ---"
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echo " (이 쿼리는 journalctl 로그에서 추출)"
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journalctl -u kcg-ai-prediction --since '6 minutes ago' --no-pager 2>/dev/null | \
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grep -o 'transshipment:.*' | tail -1
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#===================================================================
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# PART 4: 이벤트 + KPI
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#===================================================================
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echo ""
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echo "================================================================="
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echo "PART 4: 이벤트 + KPI (시스템 출력 검증)"
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echo "================================================================="
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echo ""
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echo "--- 4-1. prediction_events (last 5min) ---"
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$PSQL_TABLE << 'SQL'
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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 '5 minutes'
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GROUP BY category, level ORDER BY cnt DESC;
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SQL
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echo ""
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echo "--- 4-2. KPI 실시간 ---"
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$PSQL_TABLE << 'SQL'
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SELECT kpi_key, value, trend, delta_pct, updated_at
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FROM kcg.prediction_kpi_realtime ORDER BY kpi_key;
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SQL
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#===================================================================
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# PART 5: signal-batch 정적정보 보강 검증
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#===================================================================
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echo ""
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echo "================================================================="
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echo "PART 5: signal-batch 정적정보 보강 검증"
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echo "================================================================="
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echo ""
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echo "--- 5-1. 직전 사이클 enrich 로그 ---"
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journalctl -u kcg-ai-prediction --since '6 minutes ago' --no-pager 2>/dev/null | \
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grep -E 'signal-batch enrich|fetch_recent_detail' | tail -2
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echo ""
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echo "--- 5-2. features 내 신규 패턴(P9/P10/P11) 적용 비율 ---"
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$PSQL_TABLE << 'SQL'
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WITH dark_vessels AS (
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SELECT features FROM kcg.vessel_analysis_results
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WHERE analyzed_at > now() - interval '5 minutes' AND is_dark = true
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)
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SELECT
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count(*) AS total_dark,
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count(*) FILTER (WHERE features->>'dark_patterns' LIKE '%fishing_vessel_dark%'
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OR features->>'dark_patterns' LIKE '%cargo_natural_gap%') AS p9_applied,
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count(*) FILTER (WHERE features->>'dark_patterns' LIKE '%underway_deliberate_off%'
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OR features->>'dark_patterns' LIKE '%anchored_natural_gap%') AS p10_applied,
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count(*) FILTER (WHERE features->>'dark_patterns' LIKE '%heading_cog_mismatch%') AS p11_applied,
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round(count(*) FILTER (WHERE features->>'dark_patterns' LIKE '%fishing_vessel_dark%'
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OR features->>'dark_patterns' LIKE '%cargo_natural_gap%')::numeric
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/ NULLIF(count(*), 0) * 100, 1) AS p9_pct,
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round(count(*) FILTER (WHERE features->>'dark_patterns' LIKE '%underway_deliberate_off%'
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OR features->>'dark_patterns' LIKE '%anchored_natural_gap%')::numeric
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/ NULLIF(count(*), 0) * 100, 1) AS p10_pct,
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round(count(*) FILTER (WHERE features->>'dark_patterns' LIKE '%heading_cog_mismatch%')::numeric
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/ NULLIF(count(*), 0) * 100, 1) AS p11_pct
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FROM dark_vessels;
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SQL
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#===================================================================
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# PART 6: 사이클 로그 (직전 6분)
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#===================================================================
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echo ""
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echo "================================================================="
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echo "PART 6: 사이클 로그 (최근 6분)"
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echo "================================================================="
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journalctl -u kcg-ai-prediction --since '6 minutes ago' --no-pager 2>/dev/null | \
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grep -E 'analysis cycle:|lightweight analysis:|pipeline dark:|event_generator:|kpi_writer:|stats_aggregator|enrich|transship|ERROR|Traceback' | \
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tail -20
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#===================================================================
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# PART 7: 해역별 + 위험도 교차 (운영 지표)
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#===================================================================
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echo ""
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echo "================================================================="
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echo "PART 7: 해역별 × 위험도 교차표"
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echo "================================================================="
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$PSQL_TABLE << 'SQL'
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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_level='CRITICAL') crit,
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count(*) FILTER (WHERE risk_level='HIGH') high,
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round(avg(risk_score)::numeric, 1) 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 '5 minutes'
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GROUP BY zone_code ORDER BY total DESC;
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SQL
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echo ""
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echo "================================================================="
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echo "END OF SNAPSHOT $STAMP"
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echo "================================================================="
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} > "$OUT" 2>&1
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echo "[diag] $(date '+%H:%M:%S') saved: $OUT ($(wc -l < "$OUT") lines)"
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sleep $INTERVAL_SEC
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done
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