Security Alert: {alert_data['severity'].upper()}

{alert_data['message']}

Timestamp: {alert_data['timestamp']}

Event Details: {alert_data['details']}

""" 58:T5df,from collections import deque from datetime import datetime class PriorityQueue: def __init__(self): self.high_priority = deque() self.medium_priority = deque() self.low_priority = deque() def add_alert(self, alert): if alert['severity'] == 'critical': self.high_priority.appendleft(alert) elif alert['severity'] == 'high': self.medium_priority.appendleft(alert) else: self.low_priority.appendleft(alert) def get_next_alert(self): for queue in [self.high_priority, self.medium_priority, self.low_priority]: if queue: return queue.pop() return None class PriorityManager: def __init__(self): self.queue = PriorityQueue() self.priority_rules = { 'after_hours': (18, 8), 'camera_priority': ['entrance', 'parking'] } def calculate_priority(self, alert): base_score = {'critical': 3, 'high': 2, 'medium': 1}.get(alert['severity'], 0) # Time-based priority hour = datetime.now().hour if not self.priority_rules['after_hours'][0] <= hour <= self.priority_rules['after_hours'][1]: base_score += 1 # Location-based priority if any(loc in alert['source'] for loc in self.priority_rules['camera_priority']): base_score += 2 return base_score 59:T717,from sklearn.cluster import DBSCAN from services.database import SystemEvent import numpy as np from datetime import datetime, timedelta class AlertEngine: def __init__(self): self.alert_rules = self.load_alert_rules() self.anomaly_model = DBSCAN(eps=0.5, min_samples=5) de