Construction method of deep neural network model and fault diagnosis method and system

Abstract

The invention discloses a construction method of a deep neural network model and a fault diagnosis method and system, and relates to the technical field of communication. The construction method comprises the following steps: determining an alarm root derivation rule based on a service path, and in a service topology of a target network, taking adjacent service nodes in the service path from a source end to a destination end as a relationship between a client layer and a service layer; a unified diagnosis factor matrix used for diagnosing faults in the service path is constructed based on theexpert fault diagnosis data, the unified diagnosis factor matrix comprises a root node and alarms and performance state indexes on service nodes associated with the root node, and the root node is a service node generating a source alarm; and constructing a deep neural network model by taking the unified diagnosis factor matrix as an input and the probability vector of the fault cause type as an output, and training and verifying by using sample data. According to the invention, based on comprehensive and effective alarm and performance state indexes, the constructed model can quickly and accurately perform fault diagnosis and positioning.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a method for constructing a deep neural network model, a fault diagnosis method and a system. Background technique [0002] With the continuous increase of network services brought about by the overall development of society, the scale of the network is becoming larger and larger, the network environment is becoming more and more complex, and the types and times of failures are also increasing, which makes major network operators invest more and more in troubleshooting. bigger. [0003] Traditional manual troubleshooting must first obtain critical or root-cause alarms from a large number of alarms, and then query other relevant information on the service topology link based on these alarms (such as secondary or derivative alarms, performance, status, configuration, etc.) , and finally judge the cause of the fault and determine the location of the fault. [0004] Traditio...

AI Technical Summary

Problems solved by technology

However, the current deep learning solutions all have different degrees of defects, and it is difficult to achieve the desired effect when put into the network application.

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