Mechanism data dual-drive combined performance degradation fault root cause positioning method

Abstract

The invention discloses a mechanism data dual-drive combined performance degradation positioning method. The problem of root cause positioning of communication drive test performance degradation in different scenes is solved. The method comprises two modules, the causal relationship learning module designs a causal relationship learning model, considers the isomerism of node relationships, and clarifies the equation representation of the node relationships in a causal relationship graph; the causal inference module carries out causal inference based on the intervention index and the distribution index, and carries out inference of a final fault root cause based on the intervention deviation and the distribution abnormity condition. According to the method, an efficient algorithm with interpretability is adopted, the root cause positioning accuracy of a traditional method is greatly improved under a current network test environment data set test, meanwhile, the recall rate is high, and generalizability is achieved. In addition, practical application of enterprise maintenance engineers is facilitated, scheme analysis and conclusions can be issued to an operation and maintenance base layer, the operation and maintenance efficiency is improved, and the operation and maintenance cost is reduced.

Description

technical field [0001] The invention belongs to the field of intelligent operation and maintenance (AIOPS), and in particular relates to a method for locating the root cause of performance degradation faults combined with mechanism data and dual drives. Background technique [0002] With the continuous development of communication-related technologies and the continuous expansion of application fields, more and more types and quantities of mobile devices are connected to mobile networks, and the role of mobile networks in production and life is becoming more and more important. At the same time, with the application of mobile networks in production and life, network failures affect user experience, even cause huge losses and threaten social stability and security. Therefore, mobile network operation and maintenance has important practical significance. [0003] The root cause location of network performance degradation is an important part of network operation and maintenanc...

AI Technical Summary

Problems solved by technology

[0004] Currently, complex network fault location still relies on expert experience and lacks automated means

Moreover, the expert experience method has problems such as fuzzy experience method, difficulty in inheriting, low flexibility, lack of joint thinking, etc.

In terms of actual requirements and data conditions, firstly, the fault location method of the telecommunication network needs to satisfy the interpretability, so as to assist the engineer to locate the root cause; secondly, there are heterogeneous graph relationships between the nodes in the causality graph, while the academia The existing causal relationship learning methods default to the isomorphism of the relationship between nodes; in addition, the data has serious problems such as missing attributes and lack of label data.

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