Security Controller State-Transition Analysis for Abnormal Event Detection
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Solution Overview
Problem
Security systems often fail to detect abnormal events when users forget to arm or disarm them, leaving premises vulnerable.
Innovation Solution
A security controller monitors sensor states and maintains a history of transitions, calculating scores based on normality of state changes and new states to determine if an alert is needed, sending notifications even when the system is unarmed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the security system requires users to manually arm and disarm it, then the system can provide comprehensive security monitoring, but users may forget to arm/disarm the system, leaving premises vulnerable
Solution Approach 1:
The system automatically monitors sensor states and detects abnormal events without requiring manual arming or disarming by users. The security controller independently analyzes sensor data, determines abnormal conditions, and sends alerts to users, enabling the system to serve itself in detecting security breaches.
Solution Approach 2:
The system continuously monitors sensor states and maintains a history of transitions in advance, preparing to detect abnormal events before they occur. By tracking state changes and comparing them against historical patterns, the system is ready to identify security breaches as they happen, even when the system is in a disarmed state.
2Measurement precision
If the security system monitors all sensor states continuously, then abnormal events can be detected accurately, but the system complexity increases
Solution Approach 1:
The system extracts and focuses on specific aspects of sensor state transitions that are most indicative of abnormal events. By analyzing only the changes in sensor states rather than all possible sensor readings, the system achieves accurate detection while reducing processing complexity.
Solution Approach 2:
The system pre-processes and stores historical sensor state transitions in advance, creating a reference database that simplifies real-time analysis. By having historical data ready, the system can quickly compare current state changes against past patterns without complex real-time calculations.
3Loss of information
If the system sends alerts for all sensor state changes, then user awareness of system activity is maintained, but false alerts increase causing user annoyance
Solution Approach 1:
The system uses feedback from historical sensor state transitions to determine whether current state changes are normal or abnormal. By comparing current events against past patterns and only alerting on truly abnormal conditions, the system maintains user awareness while minimizing false alerts through intelligent feedback-based filtering.
Data Source
AI summary
Systems, apparatuses, and methods are described for detecting a state transition for sensors of a monitoring system, and determining whether the state transition is abnormal such that further action is warranted. The determination may be based on a history of state transitions in the monitoring system, and may be based on whether the same state transition has previously occurred at a corresponding time in the history, whether the new state has previously occurred following the corresponding time in the history, and other factors.