Predictive Geofence Crossing Detection
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Solution Overview
Problem
Current geographical information systems lack the ability to predictively alert for geofence crossings, which are crucial for preventing hazardous materials from entering high-density population areas, as they rely on real-time data and do not account for dynamic geofence movements or complex alert processes.
Innovation Solution
A predictive geofence system that uses spatial data to forecast future positions of objects based on their historical paths and map data, generating alerts when a geofence crossing is predicted, and can handle moving and changing geofences by determining new perimeters and tracking centroid movements, with customizable alert methods including logical formulas and artificial neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time data monitoring is used for geofence crossing detection, then immediate alerts can be generated, but the system cannot predict future crossings or account for dynamic geofence movements
Solution Approach 1:
The system performs preliminary calculations by predicting future positions of objects based on current position, speed, and direction data. It pre-determines whether a geofence crossing will occur within a specified time horizon, allowing alerts to be generated before the actual crossing event, thus enabling preventive action rather than reactive response
Solution Approach 2:
The system handles dynamic geofences by continuously updating geofence perimeter data and recalculating crossing predictions. It accounts for changing geofence shapes and positions by comparing predicted object positions against updated geofence boundaries, maintaining detection accuracy in dynamic environments
2Adaptability or versatility
If the system handles moving and changing geofences with new perimeter calculations, then adaptability is improved, but computational complexity increases
Solution Approach 1:
The system segments the geofence perimeter into discrete coordinate points and processes these points individually for prediction calculations. By dividing the continuous geofence boundary into manageable segments, the system can efficiently handle dynamic shape changes without requiring complex continuous mathematical operations
Solution Approach 2:
The system manages computational complexity by changing key parameters such as the prediction time horizon and updating geofence data at controlled intervals. It adjusts the frequency of geofence perimeter updates based on dynamics, allowing flexible handling of dynamic geofences while managing processing loads through parameter optimization
3Measurement precision
If comprehensive spatial data analysis is performed for prediction, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs partial analysis by focusing computational resources on calculating only the relevant portion of future positions that could potentially cross geofence boundaries. Instead of analyzing all possible future positions indefinitely, it limits calculations to a specified time horizon and only evaluates positions within the prediction window, reducing overall computational energy consumption while maintaining necessary accuracy
Data Source
AI summary
A predictive geofence system predicts a geofence crossing for a distance-horizon and/or a time-horizon. The predictive geofence system includes a predictive geofence platform that predicts future positions of objects, and generates an alert if the predicted future positions of the objects result in a geofence crossing or the predicted future positions cross a geofence in less than a set time.