Base station traffic prediction method and device, electronic equipment and storage medium

By employing clustering and hidden Markov models in base station traffic prediction, combined with sliding time windows and weighted moving averages, the problem of poor accuracy in base station traffic prediction in existing technologies is solved, and accurate description and prediction of base station traffic development trends are achieved.

CN122132862APending Publication Date: 2026-06-02CHINA MOBILE GROUP DESIGN INST +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA MOBILE GROUP DESIGN INST
Filing Date
2024-12-02
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing technologies lack in-depth analysis of the development status of mobile communication services in base station traffic prediction, resulting in poor prediction accuracy. In particular, when dealing with isolated points and low-probability events in the training set, they adopt purely mathematical processing or complete elimination, ignoring the integration of data with business development.

Method used

By determining the current prediction samples and training set of the target base station, and based on the state transition probability of clusters and smoothed traffic data, a hidden Markov model is used for traffic prediction. By combining sliding time windows and weighted moving average calculations, the traffic data is smoothed and clustered, revealing the development trend of the traffic system.

Benefits of technology

It improves the accuracy of base station traffic prediction by closely integrating data logic with business development logic, reflecting the development trend of traffic data, revealing the potential evolution path of the traffic system, and enhancing the accuracy of prediction.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application provides a base station traffic prediction method, apparatus, electronic device, and storage medium, relating to the field of mobile communication technology. The method includes: determining a current prediction sample and training set for a target base station; determining smoothed traffic data of the current prediction sample based on the state transition probabilities between the clusters to which the traffic segment containing the last historical sample belongs, and the average traffic data of samples within each cluster; and determining the traffic data of the current prediction sample based on the smoothed traffic data of the current prediction sample and the traffic data of multiple adjacent historical samples preceding the current prediction sample. The method and apparatus provided in this application improve the accuracy of mobile communication base station traffic prediction.
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