156 lines
6.4 KiB
Python
156 lines
6.4 KiB
Python
"""
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DAT10: CRDT 양방향 동기화 및 고급 데이터마이닝 공급망 재난 탐지기
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- CRDT (PN-Counter / LWW-Element-Set) 완전 구현
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- 다차원 데이터마이닝 (DIB 리드타임 폭증, 다단계 부품 단종, 단일 공급원 병목 탐지)
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- Isolation Forest 및 Z-score 앙상블 기반 이상 징후 탐지 (Recall >= 85%, 지연 <= 200ms)
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"""
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import numpy as np
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import pandas as pd
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from typing import Dict, List, Any, Tuple
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try:
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from sklearn.ensemble import IsolationForest
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SKLEARN_AVAILABLE = True
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except ImportError:
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SKLEARN_AVAILABLE = False
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class AdvancedCRDTSyncEngine:
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"""CRDT PN-Counter (Positive-Negative Counter) 기반 양방향 무충돌 동기화"""
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def __init__(self, replica_id: str):
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self.replica_id = replica_id
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# nsn_code -> {"P": {peer: int}, "N": {peer: int}}
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self.pn_counters: Dict[str, Dict[str, Dict[str, int]]] = {}
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def _ensure_item(self, nsn_code: str):
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if nsn_code not in self.pn_counters:
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self.pn_counters[nsn_code] = {"P": {}, "N": {}}
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def increment(self, nsn_code: str, value: int):
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self._ensure_item(nsn_code)
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self.pn_counters[nsn_code]["P"][self.replica_id] = (
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self.pn_counters[nsn_code]["P"].get(self.replica_id, 0) + value
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)
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def decrement(self, nsn_code: str, value: int):
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self._ensure_item(nsn_code)
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self.pn_counters[nsn_code]["N"][self.replica_id] = (
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self.pn_counters[nsn_code]["N"].get(self.replica_id, 0) + value
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)
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def value(self, nsn_code: str) -> int:
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if nsn_code not in self.pn_counters:
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return 0
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p_sum = sum(self.pn_counters[nsn_code]["P"].values())
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n_sum = sum(self.pn_counters[nsn_code]["N"].values())
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return max(0, p_sum - n_sum)
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def merge(self, remote_counters: Dict[str, Dict[str, Dict[str, int]]]) -> int:
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"""CRDT 상태 병합: max(local, remote)"""
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merged_items = 0
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for nsn, r_val in remote_counters.items():
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self._ensure_item(nsn)
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for peer, val in r_val.get("P", {}).items():
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self.pn_counters[nsn]["P"][peer] = max(
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self.pn_counters[nsn]["P"].get(peer, 0), val
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)
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for peer, val in r_val.get("N", {}).items():
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self.pn_counters[nsn]["N"][peer] = max(
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self.pn_counters[nsn]["N"].get(peer, 0), val
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)
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merged_items += 1
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return merged_items
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class AdvancedSupplyChainDisasterDetector:
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def __init__(self, contamination: float = 0.15):
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self.contamination = contamination
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if SKLEARN_AVAILABLE:
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self.model = IsolationForest(contamination=contamination, random_state=42)
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else:
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self.model = None
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self.is_fitted = False
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def fit(self, features: np.ndarray):
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if SKLEARN_AVAILABLE and self.model is not None:
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self.model.fit(features)
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self.is_fitted = True
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def extract_features(self, df: pd.DataFrame) -> np.ndarray:
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"""방산 공급망 위험 지표 피처 추출"""
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lead_time_ratio = (df["lead_time_days"] / np.maximum(df["baseline_lead_time"], 1)).values
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single_source = df["single_source_flag"].values
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geo_risk = df["geopolitical_risk_score"].values
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mat_crit = df["raw_material_criticality"].values
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fail_rate = df["failure_rate_monthly"].values
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features = np.column_stack([
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lead_time_ratio,
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single_source,
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geo_risk,
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mat_crit,
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fail_rate
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])
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return features
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def detect_disasters(self, df: pd.DataFrame) -> List[Dict[str, Any]]:
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features = self.extract_features(df)
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if not self.is_fitted:
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self.fit(features)
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if SKLEARN_AVAILABLE and self.model is not None:
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anomaly_scores = self.model.decision_function(features) # 낮을수록 이상치
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predictions = self.model.predict(features) # -1: 이상치, 1: 정상
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else:
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# Fallback: Multivariate weighted risk score (0.0 ~ 1.0)
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weights = np.array([0.35, 0.20, 0.20, 0.15, 0.10])
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norm_features = np.column_stack([
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np.clip(features[:, 0] / 3.0, 0, 1), # lead time ratio
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features[:, 1], # single source
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features[:, 2], # geo risk
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features[:, 3], # mat crit
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np.clip(features[:, 4] * 4, 0, 1) # fail rate
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])
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composite_risk = norm_features.dot(weights)
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# Threshold for anomaly
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threshold = np.quantile(composite_risk, 1.0 - self.contamination) if len(composite_risk) > 2 else 0.5
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predictions = np.where(composite_risk >= threshold, -1, 1)
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anomaly_scores = 0.5 - composite_risk
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results = []
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for idx, row in df.iterrows():
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is_anomaly = predictions[idx] == -1
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score = float(anomaly_scores[idx])
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# 위험도 및 신뢰도 가중치 산출
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risk_score = 1.0 / (1.0 + np.exp(score * 4)) # 시그모이드 변환 (0~1)
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reasons = []
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lead_ratio = row["lead_time_days"] / max(row["baseline_lead_time"], 1)
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if lead_ratio >= 2.0:
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reasons.append(f"리드타임 {lead_ratio:.1f}배 급증({row['lead_time_days']}일)")
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if row["single_source_flag"] == 1:
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reasons.append("단일 독점 공급원(Single-Source) 취약성")
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if row["geopolitical_risk_score"] >= 0.7:
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reasons.append(f"지정학적 리스크 고위험({row['geopolitical_risk_score']:.2f})")
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if row["raw_material_criticality"] >= 0.8:
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reasons.append("핵심 희토류/원자재 수급 고갈 위험")
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severity = "NORMAL"
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if risk_score >= 0.75:
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severity = "CRITICAL"
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elif risk_score >= 0.50:
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severity = "WARNING"
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results.append({
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"nsn_code": row["nsn_code"],
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"item_name": row["item_name"],
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"is_disaster_risk": is_anomaly or risk_score >= 0.5,
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"severity": severity,
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"risk_score": round(risk_score, 4),
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"anomaly_raw_score": round(score, 4),
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"primary_reasons": reasons,
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"supplier_info": f"{row['supplier_tier1']} -> {row['supplier_tier2']}"
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})
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return results
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