Cell capacity optimizing method, device, apparatus, and computer storage medium

Abstract

The invention discloses a cell capacity optimizing method, a device, apparatus, and a computer storage medium. The cell capacity optimizing method comprises of: obtaining a plurality of sets of key data related to the cell load of a plurality of cells in the area to be optimized in a plurality of network public opinion data; determining a target cell in a plurality of cells in which the cell loadis to be in a congested state according to the plurality of sets of key data and the plurality of network public opinion data; optimizing the capacity of the target cell according to the cell type towhich the target cell belongs. According to the embodiment of the present invention, the target cell in which cell load is to be in a congested state can be predicted according to the network public opinion data, thereby performing capacity optimization on the target cell in time.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and in particular relates to a cell capacity optimization method, device, equipment and computer storage medium. Background technique [0002] At present, the existing method for optimizing the capacity of a cell is to periodically collect capacity indicators through an operation and maintenance center (OMC), and aggregate the collected capacity indicators at a weekly Capacity expansion standard The capacity index of each cell is manually reviewed. When the capacity index reaches the expansion threshold, the hardware equipment of the cell is added to expand the cell by increasing the carrier frequency. [0003] Under normal circumstances, the carrier frequency expansion standard can be determined according to the cell classification of large, medium and small packets, and then according to the carrier frequency expansion standard, it can be judged whether the capacity index of each c...

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Problems solved by technology

[0008] Although the above method can be used to achieve the problem of capacity optimization for cells with too high load to a certain extent, because the current data collection interface is relatively traditional and single, the capacity indicators collected by OMC are the only basis for capacity expansion, resulting in relatively large reference dimensions. Single, making the judgment of the capacity of the cell inaccurate

In addition, by manually monitoring and analyzing the load status of the cell, and then judging whether to expand the capacity of the cell, it is impossible to find out the rationality of the capacity setting of the cell in time, resulting in a lag in finding the capacity problem of the cell, and it is also impossible to predict the capacity of the cell demand, the trend of capacity problems in the community cannot be analyzed in a targeted manner

Moreover, the aggregation granularity of the above method is too large, which can easily cover up instantaneous capacity problems, such as large-scale commercial activities and other temporary behaviors that are time-sensitive

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