Movement space-time trajectory analysis method in sense network environment

Abstract

The invention relates to the technical field of movement behavioral analysis and prediction in a sense network environment, and specifically to a movement space-time trajectory analysis method in the sense network environment. The movement space-time trajectory analysis method in the sense network environment comprises data reception of receiving trajectory movement position data generated by a positioning device and resolving the data format into a data format applicable to data treatment; semantic treatment of performing clustering operation on the semantic trajectory data; space-time correlation of performing characteristic analysis and statistics on clustered semantic trajectory data in a time domain and a space domain, and performing time-space correlation analysis in combination with the time domain and the space domain; correlation similarity analysis of calculating space-time correlation similarity of the semantic trajectory and performing analysis and calculation on the correlation among different space domains and different movement objects; outputting a result. The movement space-time trajectory analysis method in the sense network environment solves the problem of continuous treatment and mutual correlation of time and space dimensions in a traditional transactional database, and meets the need from a sense network application service to real-time analysis of trajectory movement data.

Description

technical field [0001] The invention relates to the technical field of mobile behavior analysis and prediction in a sensory network environment, and specifically relates to a mobile spatiotemporal trajectory analysis method in a sensory network environment. Background technique [0002] At present, with the popularization of mobile location acquisition devices such as GPS and the development of wireless transmission and ubiquitous computing technology, researches in the field of spatio-temporal trajectory analysis such as behavior pattern mining, real-time location services, and mobile location prediction based on location information of moving objects are becoming more and more important. It has aroused the attention of academia and industry, and its related applications are becoming more and more extensive. At present, there are relevant basic applications in intelligent traffic management and scheduling, monitoring and processing of sudden group events, perception of ecol...

AI Technical Summary

Problems solved by technology

However, the existing trajectory movement analysis methods either only consider the distribution characteristics of the spatial dimension, or only consider the spatial distribution characteristics of the movement position according to the order of time, and there is no mature analysis method for the space-time correlation trajectory

At the same time, there is a lack of relevant analysis and research methods based on background knowledge (such as regional demographic distribution, semantic representation of mobile location points, etc.), time-space correlation similarity clustering, and mobile model representation in semantic space. The adoption of mobile services presents a huge challenge

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