Occupant Path Prediction Using Access Sequences for Threat Alerts
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Solution Overview
Problem
Building management systems (BMS) lack the ability to effectively predict occupant pathways, making it difficult to identify potential security threats and minimize damage after a breach has occurred.
Innovation Solution
A method and system for predicting occupant paths by generating models based on access data, including general and specific sequences of access events, to calculate weighted scores for access control points, and generating alerts if deviations from predicted paths exceed a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If building management systems monitor access control data, then security monitoring capability is improved, but the ability to predict occupant pathways and identify threats remains insufficient
Solution Approach 1:
The system performs preliminary actions by generating path prediction models before security threats can manifest. It analyzes historical access control data to create probabilistic predictions of occupant pathways in advance, enabling the system to identify deviations from normal patterns that may indicate security threats before they materialize into actual breaches
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual access control data against predicted pathways. When deviations exceed a threshold, the system generates alerts and feeds this information back into the model for continuous improvement, creating a closed-loop system that enhances prediction accuracy over time
2Measurement precision
If the system generates multiple models (general and specific) for path prediction, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the prediction task into distinct components: a general path prediction model that captures overall occupancy patterns, and specific individual models that capture personal pathways. This segmentation allows each model to specialize in different aspects of movement prediction, improving overall accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system merges multiple prediction models by combining the general path prediction model with specific individual models. The general model provides baseline predictions while individual models refine these predictions for specific occupants, creating a hierarchical structure that leverages both aggregate patterns and individual behaviors to enhance prediction accuracy
3Reliability
If the system generates alerts for path deviations, then threat identification capability is improved, but false alarms may increase
Solution Approach 1:
The system employs parameter changes by using a dynamic threshold for deviation detection rather than a fixed value. The threshold adapts based on contextual factors such as time of day, day of week, and occupancy patterns, allowing the system to be more tolerant of normal variations during high-traffic periods while being more sensitive during low-activity periods, thereby reducing false alarms while maintaining threat detection capability
Data Source
AI summary
A method for predicting a path of a specific occupant of a number of occupants including receiving user access data, the user access data including a user identifier, an access time, and an access location, generating, a first model describing general sequences of access events associated with the number of occupants and a frequency of each of the general sequences, generating a second model describing specific sequences of access events associated with the specific occupant and a frequency of each of the specific sequences, and generating a path prediction model based on the first and second models, the path prediction model including a weighted score for each of the number of access control points, the weighted score associated with a probability the specific occupant accesses the access control point based on a last accessed access control point.