Alarm Probability Scoring for False Alarm Response Prioritization
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Solution Overview
Problem
False alarms in security systems lead to unnecessary dispatching of emergency services, wasting resources and delaying responses to real alarm situations due to the lack of effective methods to assess the likelihood of an alarm event being real or false.
Innovation Solution
A central monitoring station uses an alarm probability measure determined by end user feedback and heuristics based on sensor data, historical data, weather data, crime data, and locale data to prioritize responses to potential alarm events, distinguishing between real and false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alarm systems dispatch emergency services for all detected alarm events, then all potential security breaches are responded to, but resources are wasted on false alarms and response times to real alarms are delayed
Solution Approach 1:
The system changes the parameter of alarm evaluation from binary (alarm/not alarm) to probabilistic (false alarm probability score). By calculating and comparing probability scores against thresholds, the system dynamically determines whether to dispatch emergency services, thereby reducing unnecessary dispatches while maintaining reliable response to genuine threats.
Solution Approach 2:
The system implements feedback loops where alarm verification results (whether alarms were false or real) are fed back into the system to refine probability calculations. This continuous learning process improves the accuracy of false alarm probability assessments over time, enabling better resource allocation decisions.
2Loss of energy
If alarm systems verify all alarm events before dispatching emergency services, then false alarms are reduced, but response time to real alarm situations is delayed
Solution Approach 1:
The system performs preliminary verification actions selectively based on calculated false alarm probabilities. For alarms with high false alarm probability scores, automated verification (checking sensor data patterns, historical information) is performed beforehand. For low-probability alarms, the system skips verification and dispatches immediately, thus reducing response time delays while still catching false alarms.
Solution Approach 2:
The verification process is made dynamic rather than static. The system adjusts the level of verification based on the calculated false alarm probability for each specific alarm event. High-probability false alarms undergo extensive verification, while low-probability alarms receive minimal or no verification, optimizing the balance between resource efficiency and response speed.
3Ease of operation
If alarm systems use simple detection methods, then the system is easy to operate and maintain, but the ability to distinguish real alarms from false alarms is poor
Solution Approach 1:
The system introduces an intermediary computational layer (probability calculation engine) between simple sensor detection and emergency service dispatch decisions. This intermediary automatically processes sensor data, historical information, and verification results to generate false alarm probability scores, eliminating the need for complex manual assessment while improving detection precision.
Data Source
AI summary
Alarm system technology, in which an alarm event is detected at a property monitored by an alarm system when the alarm system was set in an armed state. Based on detection of the alarm event, an alarm probability score that indicates a likelihood of the alarm event being an emergency situation is determined and the alarm event is handled based on the determined alarm probability score.