Communication network field maintenance model migration method based on time delay optimization

Abstract

The invention discloses a communication network field maintenance model migration method based on time delay optimization, which comprises the following steps of: firstly, respectively calculating type similarity, name similarity and interpretation information similarity based on similarity service demand analysis, reducing the influence of irrelevant information, improving the service matching accuracy, and improving the service matching efficiency; and finally, calculating according to the weight of each similarity to obtain a comprehensive similarity. Secondly, migration modes under different conditions are selected according to the similarity of the service requirements, model migration is carried out in a direct migration mode, a backbone network freezing mode and a multi-stage migration mode, and the problems that the intelligent operation and maintenance environment of the communication network is complex and the migration efficiency is low are solved; finally, various indexes such as task time delay, edge node resources and task resource requirements are considered, a multi-priority queue model and an improved discrete particle swarm algorithm are used, migration task unloading and maintenance task unloading in a communication network field maintenance scene are completed, and the purposes of minimizing task time delay and improving task completion efficiency are achieved.

Description

technical field [0001] The invention relates to the technical field of on-site maintenance of a communication network, in particular to a method for migrating an on-site maintenance model of a communication network based on time delay optimization. Background technique [0002] With the continuous expansion of the communication network and the increasingly complex structure, the problems such as the heavy workload of on-site maintenance of the communication network, many management problems, and the lack of automatic support tools have become increasingly prominent. In response to these problems, the on-site maintenance system of the communication network has also begun to adopt the mobile edge computing architecture to improve the quality and efficiency of on-site maintenance of the communication network. In this scenario, the models used for on-site maintenance of the communication network are trained by different edge nodes, and the models are relatively independent. How...

AI Technical Summary

Problems solved by technology

Technical solution 3 introduces the distance between the training parameters of the migration layer and the initial parameters, so as to take into account the model migration and training of each migration layer during the model training process, but only pays attention to the relationship between the parameters, without the source model and the target model research on service needs

In addition, technical solutions 1, 2, and 3 do not take into account the impact of model migration on the execution of ordinary maintenance tasks in the on-site maintenance scenario of the communication network, and none of the solutions are suitable for the on-site maintenance scenario of the communication network

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