Spatio-Temporal Object Matching for Device Association
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Solution Overview
Problem
Current techniques for associating multiple devices with a single vehicle in a fleet consume excessive computing, networking, and other resources due to inaccuracies in device association, leading to errors in service provision and resource wastage.
Innovation Solution
A tracking system that generates spatio-temporal objects from tracking data received from multiple devices, determines conditions for association, calculates matching scores based on spatial and temporal information, and decides on device association based on score thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual installation and configuration of tracking devices is performed, then device association can be established, but association errors occur and computing resources are wasted
Solution Approach 1:
The system performs self-association by automatically matching tracking devices to vehicles using spatio-temporal data analysis. The association is determined autonomously based on whether devices and vehicles are in the same location at the same time, eliminating manual configuration errors and reducing computing resource waste from incorrect associations.
Solution Approach 2:
The system collects spatio-temporal data from multiple tracking devices and vehicles in advance, then performs association analysis based on this pre-collected data. By evaluating location and time information before final association is established, the system can accurately determine device-vehicle pairs without trial-and-error manual configuration.
2Measurement precision
If comprehensive tracking data collection from multiple devices is performed, then accurate association can be achieved, but computing resources are consumed
Solution Approach 1:
The system extracts only the essential spatio-temporal features (location coordinates and timestamps) from comprehensive tracking data, rather than processing all available device data. This extraction of critical information maintains association accuracy while significantly reducing computing resource consumption for data analysis.
Solution Approach 2:
The association determination process is segmented into distinct steps: collecting spatio-temporal data from multiple devices, comparing location and time information, evaluating whether devices and vehicles are at the same location at the same time, and making association decisions. This segmentation allows efficient processing of comprehensive tracking data without overwhelming computing resources.
Data Source
AI summary
A device may receive, from a first device, first tracking data that includes first temporal data and first location data, and may receive, from a second device, second tracking data that includes second temporal data and second location data. The device may generate a first spatio-temporal object based on the first tracking data, and may generate a second spatio-temporal object based on the second tracking data. The device may calculate a matching score associated with the first spatio-temporal object and the second spatio-temporal object, and may determine whether the matching score satisfies a score threshold. The device may determine that the first device is associated with the second device based on determining that the matching score satisfies the score threshold, and may perform one or more actions based on determining that the first device is associated with the second device.