Target value area analysis method, device and apparatus based on big data and medium

Abstract

The embodiment of the invention discloses a target value area analysis method and device, and apparatus based on big data and a medium. The method comprises the steps of obtaining network operation and maintenance O-domain big data and operation analysis B-domain big data; performing association analysis on the O-domain big data and the B-domain big data, positioning a target value area, and obtaining a target network grid corresponding to the target value area; based on the O-domain big data and the B-domain big data, performing user behavior analysis on the target value area, and constructing a user portrait; constructing a user flow model based on O-domain and B-domain big data; and performing service traffic prediction on the user traffic model based on the target network grid and theuser portrait, rasterizing the predicted service traffic, identifying a target value user from the traffic grid, and performing network planning on the target value area according to the target network grid and the target value user. By utilizing the embodiment of the invention, O-domain and B-domain big data can be integrated, and network planning optimization and marketing can be better supported.

Description

technical field [0001] The present invention relates to the technical field of wireless network optimization for mobile communication, in particular to a big data-based target value area analysis method, device, equipment and medium. Background technique [0002] With the large-scale development of 4G network, the scale of base stations continues to expand, and the traffic of data services increases rapidly. Compared with the beginning of the year, the traffic of 4G network has increased by 139.17%. At the same time, customers have put forward higher service quality requirements for the network. At the same time, the imbalance of service demand in the 4G era is more significant. 30% of the areas in the network generate 80% of the data traffic; 5% of the cells generate 30% of the revenue; 10% of the users generate 40% of the reverse traffic . Weak coverage in local high-value areas has caused a large number of data services to flow back to 2G and 3G networks, seriously affec...

AI Technical Summary

Problems solved by technology

The B domain data generated by market development is difficult to form an effective support for network planning and construction

It is easy to cause base stations to be built in "value depressions" and terminals to be placed in "coverage blind spots"

[0007] 2. Traditional value areas rely on subjective judgments

[0008] The discovery of indoor network problems relies on customer complaints and building sweeping tests. The value attributes of indoor buildings mainly rely on the subjective judgment of optimization personnel. Traditional analysis methods cannot adapt to work needs and lack data support methods

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