Method for predicting movement track of special crowd and electronic equipment

Abstract

The embodiment of the invention discloses a method for predicting the movement track of a special crowd and electronic equipment, and the method comprises the steps: obtaining the activity behavior characteristics of a user of a target crowd, and obtaining the track activity data of a base station coverage area of a plurality of specified frequency bands of a plurality of specified time periods of the user; on the basis of the communication identifiers of the users in the target crowd, associating the activity behavior characteristics of the users in the target crowd with the base station coverage area track activity data of the users in a plurality of specified time periods; taking the activity behavior characteristics of the user and the specified time period as the input of a movement track prediction model, taking the activity behavior characteristics of the user and the base station coverage area corresponding to the specified time period as the output of the movement track prediction model, and training the movement track prediction model; and according to wireless measurement information and known position information, establishing a moving track prediction model, and reversely positioning the position of a user only containing the wireless measurement information, so that the position continuity requirement is met, and therefore real-time and accurate prediction is achieved.

Description

technical field [0001] The embodiments of the present invention relate to the field of 5G technology, and in particular, to a method for predicting movement trajectories of special groups of people and electronic equipment. Background technique [0002] At present, the prediction of the user's running trajectory is based on the continuous trajectory modeling. However, when predicting the user's trajectory in the mobile communication scenario, because the user's trajectory is very discrete, only a dozen users can be obtained on average per day. Trajectory points, it is difficult for existing models to retrieve the user's historical behavior based on such discrete trajectories, and to establish an effective prediction model. Therefore, in the prior art, a network learning model such as a multi-layer dynamic Bayesian network is used to predict the user's movement trajectory, which cannot achieve real-time and accurate prediction. Contents of the invention [0003] Embodiment...

AI Technical Summary

Problems solved by technology

[0003] Embodiments of the present invention provide a method and electronic equipment for predicting the movement trajectory of special groups of people, which are used to solve the problem that the existing technology cannot realize real-time and accurate prediction

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