Method for positioning fault between information network and communication network

Abstract

The invention discloses a method for positioning a fault between an information network and a communication network. The method comprises the steps of deleting the redundant information in a cause-and-effect graph fault propagation model to generate a bipartite graph fault propagation model; removing information network-associated alarms from the bipartite graph fault propagation model to establish the direct relationship between fault source of the communication network and the associated fault event of the information network; determining a final objective function for a network fault positioning problem; based on the Lagrangian Relaxation and Subgradient method, solving the optimal solution of the final objective function. According to the technical scheme of the invention, the mutual influence mechanism between the information network and the communication network is analyzed, and then a bipartite graph-based network fault positioning model is provided. Meanwhile, the optimal solution problem of the model is solved, so that the network fault positioning efficiency is improved. Therefore, fault positioning results are more accurate.

Description

technical field [0001] The invention relates to the technical field of communication networks, in particular to a method for locating faults between information communication networks. Background technique [0002] At present, the methods for fault location between information and communication networks are mainly divided into the following two types: [0003] Method 1: It is typical to use rule reasoning to locate faults on the network. The general technical scheme of this method is to form examples of faults and their correlations that occurred in the past, and store them in the library in the form of cases. When a new fault occurs, find the same or similar situation from the case library, and use the correlation of the case to find the same or similar situation. to analyze new failures, and new experiences are added to the database. Finally, use the rule analysis system to compare and match the faults generated in the network with the existing rules in the rule base to ...

AI Technical Summary

Problems solved by technology

[0005] However, with the increase of rules, a large number of rules may cause matching conflicts, and the system operation overhead is difficult to meet the requirements. When new node failures or new information are encountered, combination explosion may occur

In addition, method 1 only starts with fault analysis at a single level, and lacks consideration of the inter-layer dependencies of faults, so the accuracy is not good; moreover, when the entire network is relatively large and complex, the convergence of the algorithm is difficult to be guaranteed

[0006] The correctness of the learning results of the neural network depends on the size of the learning samples, and complex systems generally cannot meet the needs of the neural network for training samples; it can be seen that the positioning efficiency of the second method also needs to be improved

Images