Fault diagnosis method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a fault diagnosis method and device, electronic equipment and a storage medium. The method comprises the following steps: extracting semantic features according to a pre-acquired first log sequence of preset time of a target system; if a rule matched with the semantic feature of the first log sequence does not exist in a rule base, determining that the first log sequence is matched with the second log sequence; and if yes, classifying the first log sequence according to a pre-established classifier, outputting a first classification result, the first classification result being whether the first log sequence represents a fault, and if yes, the first classification result comprising first fault information. According to the method, if the rule matched with the semantic feature of the first log sequence does not exist in the rule base, the first log sequence is classified through the classifier, fault information is output in real time, and therefore the fault diagnosis efficiency is improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of computers, in particular to a fault diagnosis method, device, electronic equipment and storage medium. Background technique [0002] With the popularity of cloud computing and container cloud, a large number of IT (Information Technology, information technology) application systems are gradually deployed in virtualized and containerized environments. With the continuous enrichment of various business scenarios and the blowout growth of business volume, it brings huge challenges to the maintainability of the system and applications. Especially in the telecommunications industry, operators themselves have built a lot of application systems to provide consumers with various special services, and some system functions involve sub-functions of multiple business systems, requiring multi-system collaboration to work properly. The evolution of architecture has intensified the complexity...

AI Technical Summary

Problems solved by technology

[0006] Once the unknown features do not match the known characteristics of the decision tree, and the results cannot be retrieved in the decision tree, manual step-by-step troubleshooting is required to locate the fault, fix the problem, and re-import the obtained decision information into the decision tree, resulting in overall inefficiency

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