User track prediction method and system based on multi-relation fusion analysis

Abstract

The invention relates to a user track prediction method and system based on multi-relation fusion analysis. According to the method, a trajectory relation graph is constructed according to a historical trajectory similarity relation and a position proximity relation of users, and then a future trajectory of a target user is predicted by using a trajectory prediction model integrated with an attention mechanism based on a relation between the users in the trajectory relation graph. According to the method, the time regularity characteristic of user travel is fully considered, the social behavior information in the crowd is fully mined, and the prediction precision is obviously improved; according to the method, the influence is calculated by using the trajectory relation graph integrated with the attention mechanism, and the difference of the influence of different users on the trajectory is integrated, so that the method is more in line with the actual situation; according to the method, the trajectory behavior information of pedestrians adjacent in position is utilized, the trajectory conditions of users with similar historical trajectories are also considered, two influencing crowds are combined together to construct the trajectory relation graph, and the problem of how to model various types of information is solved.

Description

technical field [0001] The present invention relates to the technical field of computer data mining and analysis, in particular to the technical field of stream data mining, and specifically refers to a trajectory prediction method and system based on multi-relationship fusion analysis. Background technique [0002] Due to its complexity, the subject of human motion trajectory prediction has been a hot topic of research by scholars at home and abroad in recent years. With the rise and popularization of artificial intelligence technology, intelligent applications such as intelligent security systems, self-driving cars, and robot navigation systems have gradually entered the public eye. If it is possible to understand and predict the trajectories of people in complex real-world scenes, and dynamically predict the future location of each individual in real time, smart applications can improve the service accuracy and availability of the above-mentioned smart applications based ...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problems of inaccurate prediction caused by the division of granularity required by existing user trajectory prediction algorithms, insufficient utilization of user influence information, and relatively simple methods of measuring influence, the present invention provides a user trajectory prediction method based on multi-relationship fusion analysis and system

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