Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training

Yong Liu, Weiwen Zhan, Yuan Li, Xingrui Li, Jingkai Guo and Xiaoling Chen ()
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Yong Liu: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Weiwen Zhan: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Yuan Li: School of Physical Education, China University of Geosciences, Wuhan 430074, China
Xingrui Li: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Jingkai Guo: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Xiaoling Chen: School of Art and Media, China University of Geosciences, Wuhan 430074, China

Energies, 2023, vol. 16, issue 3, 1-19

Abstract: Smart grid-training systems enable trainers to achieve the high safety standards required for power operation. Effective methods for the rational segmentation of continuous fine actions can improve smart grid-training systems, which is of great significance to sustainable power-grid operation and the personal safety of operators. In this paper, a joint algorithm of a spatio-temporal convolutional neural network and multidimensional cloud model (STCNN-MCM) is proposed to complete the segmentation of fine actions during power operation. Firstly, the spatio-temporal convolutional neural network (STCNN) is used to extract action features from the multi-sensor dataset of hand actions during power operation and to predict the next moment’s action to form a multi-outcome dataset; then, a multidimensional cloud model (MCM) is designed based on the motion features of the real power operation; finally, the corresponding probabilities are obtained from the distribution of the predicted data in the cloud model through the multi-outcome dataset for action-rsegmentation point determination. The results show that STCNN-MCM can choose the segmentation points of fine actions in power operation in a relatively efficient way, improve the accuracy of action division, and can be used to improve smart grid-training systems for the segmentation of continuous fine actions in power operation.

Keywords: power-grid training; cloud model; spatio-temporal convolutional neural network; action segmentation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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