Adaptive Security Sensor Configuration for False Alarm Reduction
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Solution Overview
Problem
Security systems often issue false alarms due to suboptimal static configuration settings that are difficult to set up and become less effective over time, requiring expertise and manual updates.
Innovation Solution
A security system controller that utilizes machine learning to analyze sensor data and automatically adjust configuration settings, such as cross-zone pairs and delay times, to reduce false alarms based on learned patterns and activity patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static configuration settings are manually configured, then false alarms can be reduced through expert setup, but the system requires substantial expertise and the settings become less effective over time
Solution Approach 1:
The security system automatically monitors sensor activation patterns and self-adjusts configuration settings without requiring expert intervention. The system analyzes historical data, identifies false alarm patterns, and dynamically modifies parameters such as entry delays, exit delays, and cross-zone pairings to reduce false alarms autonomously
Solution Approach 2:
The system transitions from static configuration settings to dynamic adaptive settings that automatically adjust based on learned patterns. Configuration parameters are no longer fixed but evolve over time as the system learns from operational data, enabling continuous optimization of false alarm reduction
2Ease of operation
If static configuration settings are used, then the system is easy to operate, but the settings become less effective over time and require manual updates
Solution Approach 1:
The system continuously monitors sensor activation patterns and uses this feedback to automatically adjust configuration settings. By analyzing the effectiveness of current settings in reducing false alarms, the system dynamically optimizes parameters without requiring manual reconfiguration, maintaining both ease of operation and configuration effectiveness
Solution Approach 2:
The security system performs self-optimization by automatically analyzing operational data and adjusting its own configuration settings. This eliminates the need for manual updates while maintaining high effectiveness, allowing the system to adapt to changing patterns autonomously
3Reliability
If machine learning is used to automatically adjust configuration settings, then false alarms are reduced through dynamic adaptation, but the system complexity increases
Solution Approach 1:
The patent replaces manual configuration mechanisms with automated machine learning algorithms. Instead of requiring expert technicians to manually analyze patterns and adjust settings, the system uses computational algorithms to automatically learn from sensor data and optimize configuration parameters, reducing reliance on human expertise despite increased computational complexity
Data Source
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AI summary
A security system includes a security system controller and a plurality of security sensors that are operably coupled with the security system controller. The security system includes a plurality of configuration settings that define when particular alarms are to be issued by the security system based at least in part on events that are detected by the security sensors. The security system controller receives each of the events detected by the security sensors. The security system controller stores event data that includes an event type, a time value indicating when the event occurred, and an identifier of the security sensor that detected the event. The security system controller analyzes the stored event data to identify and output one or more changes to one or more of the plurality of configuration settings in order to reduce false alarms issued by a security system.