Fault anomaly classification method and device and readable storage medium

Abstract

The invention relates to a fault exception classification method and device and a readable medium, and the method comprises the steps: obtaining exception data in response to a fault exception classification request; performing first classification processing on the abnormal data to obtain a classification result of refined fault abnormity; performing second classification processing on the classification result of the refined fault exception to obtain a specified fault exception classification result; and performing exception category conversion on the fault exception classification result to obtain visually displayed fault exception data. According to the technical scheme, the fault abnormity is rapidly filtered, so that developers can more accurately position the fault abnormity, and the fault is visually displayed.

Description

technical field [0001] The invention relates to the field of computers, in particular to a fault abnormal classification method, device and readable storage medium. Background technique [0002] Usually, after the application program is released, due to the impact of various reasons on the hardware environment, software environment, and human operations, the application program has an abnormal situation. This abnormality is commonly called downtime. Collect abnormal fault information and report it, and determine it according to the reported abnormal fault information. [0003] When analyzing data for abnormal faults, it is often necessary to sort the reported problems one by one, and usually, the sorting basis may be different crash functions. [0004] In the prior art, the way of classifying fault exceptions is too fine-grained, which is not conducive to the unified classification of fault exceptions. In this way, the classification will not reflect the main fault points o...

AI Technical Summary

Problems solved by technology

[0004] In the existing technology, the way of classifying fault exceptions is too fine-grained, which is not conducive to the unified classification of fault exceptions. In this way, the classification will not reflect the main fault points of the application due to inaccurate classification.

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