摘要
随着电力通信网络的快速增长,网络中通信设备的在线状态预测对于提升运维可靠性具有重要意义。在实际场景中,设备工作数据来源复杂,往往存在数据维度高、特征稀疏且模式重复等问题,导致传统的预测方法性能非常受限。本文提出一种基于注意力机制和LSTM(长短时记忆)模块的设备状态预测模型。模型训练分2阶段进行,保证注意力机制能够通过端到端学习对原始特征进行充分降维并提取出最相关的信息进行状态预测。基于电力通信网络真实运维数据进行一系列验证实验,结果表明所提方法在设备状态预测问题中的有效性。
With the rapid growth of electric power communication networks,the importance of predicting the working state of online equipment is increasing as well.Since the running data of typical communication devices always come from heterogeneous resources,the prediction models have to be learned from features with high dimension,high sparsity as well as repetitive patterns.This problem severely restricts the performance of conventional machine learning approaches.This paper proposes a novel state prediction model based on the integration of attention mechanism and LSTM(Long Short-Term Memory).By a two-stage learning strategy,the attention mechanism can achieve both dimensionality reduction and feature extraction of original input.Meanwhile,most related features are extracted for final prediction from the end-to-end learning.Extensive experimental results on practical running data of electric power communication networks demonstrate that,the proposed method has high performance in the working state prediction problem.
作者
吴海洋
陈鹏
郭波
蒋春霞
李霁轩
朱鹏宇
WU Hai-yang;CHEN Peng;GUO Bo;JIANG Chun-xia;LI Ji-xuan;ZHU Peng-yu(Information and Communication Branch,State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 210024,China)
出处
《计算机与现代化》
2020年第10期12-16,共5页
Computer and Modernization
基金
国网江苏省电力有限公司科技项目(SGJSXT00DDJS1900167)。
