import numpy as np from sklearn.ensemble import IsolationForest def detect_anomalies(scada_stream): # Extract P, T, Q features features = extract_features( scada_stream['pressure'], scada_stream['temperature'], scada_stream['flow_rate'] ) # Run isolation forest model model = IsolationForest( contamination=0.03, n_estimators=200 ) scores = model.predict(features) # Flag anomalous readings anomalies = scores == -1 confidence = calc_confidence( features[anomalies] ) if confidence > 0.83: trigger_alert( asset=scada_stream.asset_id, conf=confidence )