摘要
为了实现电站通信网络故障快速定位,本文设计了一种基于卷积神经网络(Convolutional Neural Network,CNN)的电站通信网络故障诊断系统。本研究通过故障诊断系统的模型结构和可视化功能模块的设计,阐述了模型和故障诊断可视化的关键技术和流程,搭建并优化系统实现环境。最后文章所提出的模型与基线方法进行比较,证明所提出模型在精度等各个指标中均表现优异。系统可以利用基于CNN的故障诊断算法快速定位和诊断网络故障,实现网络拓扑的可视化过程和故障诊断。
In order to realize the rapid location of power station communication network faults,this paper designs a fault diagnosis system for power plant communication networks based on Convolutional Neural Networks(CNN).Designed the model structure and visualization module of the fault diagnosis system,elaborated on the key technologies and processes for model implementation and fault diagnosis visualization,and built and optimized the system implementation environment.Finally,the overall operation of the system was introduced and demonstrated.The system proposed in this article can use CNN based fault diagnosis algorithms to quickly locate and diagnose network faults,realizing the visualization process of network topology and fault diagnosis process.It supports the management and classification of different networks,and has advantages such as efficiency and flexibility.
作者
梁敬鑫
LIANG Jingxin(Huadian Fuxin Zhouning Pumped Storage Energy Co.,Ltd.,Lianjiang Fujian 355400,China)
出处
《信息与电脑》
2024年第12期182-184,共3页
Information & Computer
关键词
网络拓扑
网络管理
卷积神经网络
故障诊断
可视化
network topology
network management
convolutional neural networks
fault diagnosis
visualization
