Network fault diagnosis methods and systems

Abstract

The invention provides network fault diagnosis methods and systems. A gradient boosting decision tree classifier prediction model is trained through adoption of a symptom data set and a fault data setin network history data. Network fault diagnosis is carried out through utilization of the trained gradient boosting decision tree classifier prediction model. Network fault diagnosis precision can be effectively improved. Network fault diagnosis time can be effectively reduced. The method and the system are applicable to diversified production scenes.

Description

technical field [0001] The present invention relates to the technical field of communication Internet, in particular to a network fault diagnosis method, system, computer equipment and computer-readable storage medium. Background technique [0002] With the rapid development of information technology, the scale of the network system is constantly expanding and the complexity is getting higher and higher. When a certain part of the network fails, it will cause a series of symptoms. If the fault point cannot be detected in time and accurately, the entire network System functions, reliable operation, and safe production will all be affected, and even lead to network paralysis. Therefore, timely and effective diagnosis of network faults is very important. [0003] The early methods relying on expert knowledge of network faults have been difficult to ensure the stability of current large-scale and high-complexity networks. Therefore, in large and complex networks, intelligent d...

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Problems solved by technology

However, the conditional independence assumption of Naive Bayes usually violates the real data situation in practi

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