Alarm root cause identification method based on causal network mining and graph attention network

Abstract

The invention discloses an alarm root cause identification method based on causal network mining and a graph attention network, and solves the problem of rapid and accurate fault positioning of a large-scale complex communication network. Starting from the reality of network equipment alarms, a maximum and minimum hill climbing method (MMHC) is used for mining causal trigger relationships among the alarms, and on the basis, a graph attention network is used for accurately positioning the alarms. The model has certain fault tolerance for the mined alarm relationship, and the weight influence ofdifferent neighbor nodes is adjusted through an Attention mechanism, so that the identification of the root cause alarms is more accurate, and 93% of identification accuracy is achieved.

Description

technical field

[0001] The invention belongs to the field of intelligent operation and maintenance (AIOPS), and in particular relates to an alarm root cause identification method based on causal network mining and graph attention network (GAT). Background technique

[0002] In a large-scale network operation and maintenance environment, when a network device fails, a large amount of alarm information will be generated, and due to the correlation between devices, it is very likely to trigger an alarm for the device associated with it in a short time. In the current Huawei wireless field scenario, the occurrence of a fault often triggers multiple alarm events, so that the equipment and business processes related to the fault will generate alarm information. At the same time, these alarm information (alarm streams) are likely to be superimposed together, submerging the real fault alarms, making fault identification very difficult. Therefore, it is of great practical significanc...

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