Fault location visualization system and method based on operation and maintenance data prediction

Abstract

The invention discloses a fault positioning visualization system and method based on operation and maintenance data prediction. According to the method, an existing log collection framework is used for collecting machine and application log information in a network cluster to generate operation and maintenance big data, and then an artificial intelligence method is used for processing the operation and maintenance big data, so that network faults can be predicted in advance and displayed in a visual mode. The method has the advantages that the fault can be efficiently and accurately recognized; the network fault can be predicted in advance; sufficient time is provided for operation and maintenance personnel to carry out operation and maintenance work in time; and the work efficiency of theoperation and maintenance personnel is effectively improved in an assisting mode.

Description

technical field [0001] The invention relates to a fault location visualization system and method based on operation and maintenance data prediction, and relates to the technical field of computer network and intelligent operation and maintenance. Background technique [0002] With the maturity of network technology, the coverage of wireless networks is gradually increasing, and at the same time, there are more and more smart devices around people, so the number of devices connected to the network has also increased dramatically. On this basis, the quality of the information service provided by the network is a key factor affecting user experience. For example, if there is an access control authentication system in the network, if there is a failure, the user's work efficiency of the access control system may be greatly reduced. Based on the above two points, the current network puts forward higher requirements for the accuracy and timeliness of fault location, so network fau...

AI Technical Summary

Problems solved by technology

[0004] Although there are so many frameworks for network fault analysis and location, these solutions also have many shortcomings and deficiencies in the current environment

First of all, most solutions mainly rely on the threshold method and error log information extraction to detect and locate faults in detecting anomalies. This method is difficult to detect errors in time and is prone to false positives. In the growing network environment, the risk of its existence is getting higher and higher. getting bigger

Moreover, the threshold of the framework is generally specified by experts. With the frequent updates of the system upgrade, the workload of re-understanding the system setting parameters is too large, and the accuracy is not high. The second problem is that the number of connected devices has increased dramatically, making our log The information is that the amount of data is too large. Even if the current framework is used for processing, the amount of information it covers is too large, and it is difficult to assist the operation and maintenance personnel to understand the current network status in time. In this way, when the operation and maintenance personnel cannot understand the machine status in time, Many faults could not be handled in time, and the operation and maintenance personnel fell into a passive operation and maintenance state of waiting for user feedback before performing operation and maintenance

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