Busy hour and busy area prediction method and device

Abstract

The embodiment of the invention provides a busy hour and busy area prediction method and device, and the method comprises the steps: obtaining historical telephone traffic data of a target cell in thesame specified time period in a preset period, and determining the slope for representing the data change trend of a data type in the specified time period according to the historical telephone traffic data of the data type for each data type; judging whether the target cell meets a preset expansion condition or not according to the historical telephone traffic data and the slope corresponding toeach data category; and if so, determining the target cell as a busy time busy cell. The trend prediction is performed by comparing the data of the target cell in the same specified time period in the preset period, the data stability is high, in addition, whether the target cell meets the preset expansion standard or not is judged according to the historical telephone traffic data and the slope,and the user experience is improved. The invention is high in predictability, high in accuracy and short in time delay for busy time and busy area trends, and effectively avoids the problem that theuser experience is poor due to congestion caused by the fact that capacity expansion cannot be implemented in time after the capacity expansion list is actually obtained.

Description

technical field [0001] The present invention relates to the technical field of mobile communication, in particular to a method and device for predicting busy areas during busy hours. Background technique [0002] At present, in order to avoid the impact of busy areas on the user's business experience, the existing prediction platforms and methods used in the promotion and use of the predictive time dimension for the pre-judgment and expansion of the cell are days, and the cell dimension is the cell. The average value is used as the prediction condition to form a weekly expansion granularity list, and follow-up corresponding countermeasures are taken through the expansion granularity list to alleviate the problem of network congestion; [0003] However, on the one hand, the time dimension of the existing algorithm for predicting busy hours and busy areas is only at the day level, and the lag time is one week, and the time is extended, and the actual expansion list is not avai...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the embodiments of the present invention is to provide a method and device for predicting busy areas during busy hours, so as to solve the problem of using the seven-day averaging algorithm in the prior art without task trend calculation methods, rough algorithms, insufficient precision, extended time, and after actually obtaining the expansion list. It is too late to implement capacity expansion, resulting in congestion and poor user experience

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