Security Sensor Event Analysis for Dynamic Alarm Configuration
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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 may become less effective over time, requiring expertise and manual adjustments.
Innovation Solution
A security system controller analyzes event data from sensors to identify changes in configuration settings that reduce false alarms, using machine learning to optimize settings automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static configuration settings are used to reduce false alarms, then alarm accuracy is improved, but system complexity and setup difficulty increase
Solution Approach 1:
The system automatically analyzes sensor event data and adjusts configuration settings without requiring manual expert intervention. The controller performs self-optimization by identifying patterns in false alarm events and autonomously modifying cross-zone pairs, entry delays, and exit delays to reduce false alarms while maintaining security effectiveness.
Solution Approach 2:
The system continuously monitors sensor events and uses this feedback to dynamically adjust configuration settings. By analyzing the temporal and spatial patterns of sensor activations, the system learns from past events and optimizes alarm parameters to distinguish between false alarms and genuine threats, improving accuracy over time.
2Reliability
If manual configuration settings are setup by experts, then initial alarm accuracy is improved, but settings become less effective over time
Solution Approach 1:
The system transitions from static configuration settings to dynamic, adaptive settings that automatically adjust based on learned patterns from sensor events. Configuration parameters such as cross-zone pairs, entry delays, and exit delays are no longer fixed but are continuously optimized based on the analysis of event data, allowing the system to adapt to changing environmental conditions and usage patterns.
Solution Approach 2:
The system performs self-optimization by automatically analyzing sensor event data and adjusting configuration settings without requiring periodic manual intervention by experts. The controller autonomously identifies patterns in false alarm events and modifies configuration parameters to maintain optimal alarm accuracy as conditions change over time.
3Reliability
If static configuration settings are used, then setup expertise is required, but this increases installation time and cost
Solution Approach 1:
The system eliminates the need for expert configuration by automatically analyzing sensor event data and optimizing alarm parameters. During installation, the system can quickly initialize with default settings and then autonomously learn and adjust configuration parameters based on observed sensor patterns, dramatically reducing installation time and eliminating the need for specialized expertise.
Solution Approach 2:
The system performs preliminary analysis of sensor events and pre-optimizes configuration settings automatically during or immediately after installation. By proactively analyzing event patterns and adjusting parameters before false alarms occur, the system eliminates the need for time-consuming manual configuration and expert tuning sessions.
Data Source
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.