A method and device for multi-dimensional classification and statistical analysis of network fault report data

Abstract

The embodiment of the present invention discloses a method and device for multi-dimensional classification and statistical analysis of network fault reporting data. The method includes: analyzing fault reporting record files in the network according to an address resolution algorithm to obtain location information of each fault reporting record; The location information is obtained, and the number of fault reports corresponding to different times and different locations in the fault report record file is obtained by statistics. The embodiment of the present invention can supervise the network conditions in all scenarios by analyzing the fault report record files in the network, and the user sampling range is large and the sampling data is comprehensive; And the division of different locations, to count the number of corresponding fault reports, which can cover the blind areas that cannot be monitored by the network management, and can directly reflect the user experience. , which objectively reflects the potential bottleneck of the network.

Description

technical field [0001] The embodiment of the present invention relates to the field of communication technologies, and in particular to a method and device for multi-dimensional classification and statistical analysis of network failure report data. Background technique [0002] While competition in the wireless field of operators is becoming fiercer, fixed broadband has gradually become the next profit growth point. Whether it is sticking to the fixed broadband market share or opening up the market through "fixed-mobile bundling", the fixed broadband market, especially the home broadband market, has undoubtedly become a new force and field of competition. Among them, network supervision capabilities have become the escort for broadband quality. key competencies. [0003] Traditional broadband access network monitoring and management generally monitor user terminal equipment, link traffic early warning, and service configuration standardization through upper-layer network m...

AI Technical Summary

Problems solved by technology

However, factors such as probe deployment scale, user behavior habits, and sample collection cycle length are strongly related to the analysis results, which often have certain one-sidedness and limitations.

The probe method cannot effectively solve the experience problems of different users on the entire network, let alone make predictions and judgments in advance

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