Target motion track segmentation compression method based on improved particle swarm optimization

Abstract

The invention provides a target motion track segmentation compression method based on improved particle swarm optimization, and the method comprises the steps: constructing a particle swarm track segmentation approximation scheme optimization basic frame based on contour preservation, building a mapping relation between track data and particle states, defining a track contour fitting degree optimization target function, and carrying out the optimization of the target motion track. Individual trajectory segmentation scheme search based on the particle swarm optimization is realized; a particle updating strategy based on neighborhood adjustment and random jump is designed, and the track segmentation scheme searching efficiency of the particle swarm algorithm is improved; and for each trajectory in the data set, particle swarm trajectory segmentation scheme optimization with the segmentation number increasing from small to large is carried out, a trajectory segmentation compression effect evaluation index is established, and a trajectory data overall segmentation compression scheme which comprehensively considers the compression ratio and the segmentation precision is obtained. According to the method, global-oriented group intelligent track segmentation compression is realized, and compared with an existing method, the track data compression effect and segmentation approximation precision are both improved.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a segmental compression method of a target motion track based on a particle swarm algorithm. Background technique [0002] With the continuous development of global positioning technology, wireless communication technology and the wide application of mobile terminals, a large amount of moving target trajectory data emerges as the times require. Trajectory data contains a wealth of target spatiotemporal feature information. Through data mining, in-depth analysis and other technologies, high-value information such as target activity patterns, behavioral characteristics, interest habits, and abnormal changes can be found. However, the rapid increase of trajectory data has also brought many challenges to data services based on target spatio-temporal locations, including increased data transmission load, pressure on data storage, reduced data query efficiency, and reduced data analysis ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above-mentioned technical problems, the present invention proposes a segmented compression method of target trajectory based on improved particle swarm optimization, which is used to solve the difficulty in setting threshold parameters and segmental trajectory of the trajectory data compression method based on line segment simplification in the prior art. The accuracy is not high, it is difficult to effectively balance the segmentation accuracy and compression rate and other issues

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