一种面向电力运维作业的LSTM动作识别方法

doi:10.16182/j.issn1004731x.joss.19-FZ0336

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (12): 2837-2844.doi: 10.16182/j.issn1004731x.joss.19-FZ0336

一种面向电力运维作业的LSTM动作识别方法

刘培贞¹, 贾玉祥^1,3,*, 夏时洪²

1. 郑州大学信息工程学院,河南郑州 450001;
2. 中国科学院计算技术研究所,北京 100190;
3. 闽江学院物联网产业化与智能生产协同创新中心,福建福州 350108

收稿日期:2019-05-24 修回日期:2019-07-17 发布日期:2019-12-13
作者简介:刘培贞(1999-),女,河南信阳,本科,研究方向为运动仿真。
基金资助:
国家自然科学基金(61402419), 闽江学院物联网产业化与智能生产协同创新中心开放基金(IIC1707)

An LSTM-based Motion Recognition Method for Power Operation and Maintenance

Liu Peizhen¹, Jia Yuxiang^1,3,*, Xia Shihong²

1.School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China;
2.Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
3. Collaborative Innovation Center of IoT Industrialization and Intelligent Production, Minjiang University, Fuzhou 350108, China

Received:2019-05-24 Revised:2019-07-17 Published:2019-12-13

摘要/Abstract

摘要： 电力运维安全是备受社会关注的课题。为了避免因运维人员的操作失误而产生严重后果,提出了一种基于长短期记忆网络LSTM（Long Short Term Memory）的、面向电力运维作业的动作识别方法,该方法涵盖了从数据采集、数据处理到分类识别的整个过程,可对人员工作过程中的操作行为进行识别和监督。基于新构建的电力运维作业数据集将方法中用到的深度学习算法LSTM与传统机器学习算法KNN进行仿真对比实验,结果表明,LSTM的表现更佳,在时间窗口为120帧时,LSTM的准确率达到91.32%,比KNN高出约2个百分点。

关键词: 电力运维安全, LSTM, 动作识别方法, 仿真实验

Abstract: The security of power operation and maintenance has always been a subject of great social concern. In order to avoid serious consequences caused by the fault of staffs, a motion recognition method for power operation and maintenance jobs based on LSTM (Long Short-Term Memory) is proposed, which covers the whole process from data collection, data processing to motion classification and recognition, then it can recognise and supervise the behavior of staffs who are at work. In addition, a simulation experiment is conducted between the deep learning algorithm LSTM and the traditional machine learning algorithm KNN based on the newly constructed data set of power operation and maintenance jobs. The results show that LSTM achieves better performance than KNN. When the time window is 120 frames, the accuracy based on LSTM reaches 91.32%, which is about 2 percentage points higher than KNN.

Key words: power operation and maintenance security, LSTM, motion recognition method, simulation experiment

中图分类号:

TP391.9

刘培贞, 贾玉祥, 夏时洪. 一种面向电力运维作业的LSTM动作识别方法[J]. 系统仿真学报, 2019, 31(12): 2837-2844.

Liu Peizhen, Jia Yuxiang, Xia Shihong. An LSTM-based Motion Recognition Method for Power Operation and Maintenance[J]. Journal of System Simulation, 2019, 31(12): 2837-2844.

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一种面向电力运维作业的LSTM动作识别方法

An LSTM-based Motion Recognition Method for Power Operation and Maintenance

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