Fault Diagnosis Method and Apparatus for Big-Data Network System

摘要

A fault diagnosis method for a big-data network system includes extracting fault information from historical data in the network system, to form training sample data, which is trained to obtain a deep sum product network model that can be used to perform fault diagnosis; and diagnosing a fault of the network system based on the deep sum product network model. The embodiments of the present application resolve a problem that it is difficult to diagnose a fault of a big-data network system.

主权项

1. A fault diagnosis method for a network system, comprising:
obtaining historical data of the network system, wherein the historical data is heterogeneous data, wherein the heterogeneous data comprises structured data and non-structured data, wherein the historical data comprises fault information, and wherein the fault information is used to describe a cause and a symptom of multiple faults of the network system; obtaining the fault information from a structured field of the structured data and data content of the non-structured data, to determine multiple groups of values of fault-related random variables, wherein one group of values of the fault-related random variables is used to indicate an association relationship between a symptom and a cause of one fault of the network system, wherein the fault-related random variables comprise a random variable of a first category and a random variable of a second category, wherein the random variable of the first category is used to represent a symptom of a fault of the network system, and wherein the random variable of the second category is used to represent a cause of the fault of the network system; using the multiple groups of values of the fault-related random variables as training sample data, to train a deep sum product network model; assigning a value to the random variable of the first category according to a symptom of a current fault of the network system; determining a marginal probability or a conditional probability of the random variable of the second category by using the deep sum product network model and according to the assigned value of the random variable of the first category; and deducing a cause of the current fault according to the marginal probability or the conditional probability of the random variable of the second category.