Motion mode mining method based on moving target trajectory and related equipment

By pairing and distance change sequence calculation of the trajectory data of the moving target, combined with the analysis of convergence rate and separation rate, the problem of poor reliability of motion pattern mining in the prior art is solved, and more accurate and reliable motion pattern recognition is achieved.

CN119719832AActive Publication Date: 2025-03-28CENT SOUTH UNIV +1
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Patent Information

Application Number
CN202510216690.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-26
Publication Date
2025-03-28
Estimated Expiration
2045-02-26

AI Technical Summary

Technical Problem

The existing mobile target aggregation-separation mode mining method has poor reliability in identifying aggregation mode, early motion modeling, and applicability in different geographical scenarios.

Method used

By obtaining the trajectory data of each moving target in the study area, all moving targets are paired up, the distance change sequence of each moving target pair is calculated, the convergence rate and separation rate are calculated, and clustering is performed to identify the convergence and separation patterns.

Benefits of technology

It improves the accuracy and reliability of motion mode mining, can effectively capture the movement changes between moving targets, is suitable for different geographical scenarios, and can monitor the aggregation-separation mode of mobile targets in real time.

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Abstract

The invention relates to the technical field of motion pattern recognition, and provides a motion pattern mining method based on a moving target trajectory and related equipment. The method comprises the following steps: acquiring a distance change sequence of each moving target pair based on all trajectory data; calculating a convergence rate and a separation rate according to all the distance change sequences, and determining a plurality of convergence moving targets and a plurality of separation moving targets from all the moving targets according to the convergence rate and the separation rate; clustering the convergence moving targets according to the track data at the current moment to obtain a plurality of convergence clusters, and clustering the separation moving targets according to the track data at the first moment to obtain a plurality of separation clusters; and taking the movement of all the moving targets in each aggregation cluster in the time period as an aggregation mode, and taking the movement of all the moving targets in each separation cluster in the time period as a separation mode. According to the method, the universality and reliability of exercise mode mining can be improved.
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