The standard of manual operation in smart grid, which require accurate manipulation, is high, especially in experimental, practice, and training systems based on virtual reality (VR). In the VR training system, data gloves are often used to obtain the accurate dataset of hand movements. Previous works rarely considered the multi-sensor datasets, which collected from the data gloves, to complete the action evaluation of VR training systems. In this paper, a vectorized graph convolutional deep learning model is proposed to evaluate the accuracy of test actions. First, the kernel of vectorized spatio-temporal graph convolutional of the data glove is constructed with different weights for different finger joints, and the data dimensionality reduction is also achieved. Then, different evaluation strategies are proposed for different actions. Finally, a convolution deep learning network for vectorized spatio-temporal graph is built to obtain the similarity between test actions and standard ones. The evaluation results of the proposed algorithm are compared with the subjective ones labeled by experts. The experimental results verify that the proposed action evaluation method based on the vectorized spatio-temporal graph convolutional is efficient for the manual operation accuracy evaluation in VR training systems of smart grids.

Natural gas power generation has the advantages of flexible operation, short start–stop times, and fast ramp rates. It has a strong peaking capacity and speed compared to coal power generation, and can greatly reduce emissions of harmful substances such as sulphur dioxide. However, in practice, the accurate identification of borehole fluvial facies in the exploration area is one of the most important conditions affecting the success of gas field exploration. An insufficient number of drilling points in the exploration area and the accurate identification of lithological data features are key to the correct identification of borehole fluvial facies, and understanding how to achieve accurate identification of borehole fluvial facies when there are insufficient training data is the focus and challenge of research within the field of natural gas energy exploration. This paper proposes a borehole fluvial facies identification method applicable to the sparse sample size of drilling points, using the Sulige gas field in the Ordos Basin of China as the research object, with the drilling lithology data in the field as the sample data and the data augmentation and classification of the images through generative adversarial networks. The trained model was then validated on the Hangjinqi gas field with the same geological properties. Finally, this paper compares the recognition accuracy of borehole fluvial facies with that of other deep learning algorithms. It was verified that this research method can be applied to oil and gas exploration areas where the number of wells drilled is small and there are limited data, and that this method achieves accurate identification of borehole fluvial facies in the exploration area, which can help to improve the efficiency of oil and gas resources drilling identification to ensure the healthy development of the power and energy industry.

Smart grid training system needs to evaluate actions during power grid operations in order to complete training for relevant personnel. The commonly used action evaluation methods are difficult for evaluating fine actions during power grid operations, and the evaluation results are subjective. The use of an effective method to evaluate the actions of the power grid operation is important for improving the smart grid training system, enhancing the skills of the trainers, and ensuring the personal safety of operators. This paper proposes a cloud attention mechanism and an evaluation method of grid-related actions based on a multidimensional adaptive entropy cloud model to complete the evaluation of fine actions in the grid’s operation process. Firstly, the OpenCV technique is used to obtain the data related to hand actions during grid operation and to extract the action features to complete the construction of multiscale date sets; then, the adaptive entropy weight matrix at different scales is constructed based on multiscale data sets using the cloud attention mechanism, and the basic cloud model is generated from original hand-action feature data; finally, the multidimensional adaptive entropy cloud model is constructed by the adaptive entropy weight matrix and the basic cloud model, and the multidimensional adaptive entropy cloud model obtained is compared with the multidimensional adaptive entropy cloud model generated based on the standard action features in the same space to obtain the evaluation level of the hand action. The results show that the evaluation method of grid-related actions based on the multidimensional adaptive entropy cloud model can solve the mutual mapping problem between quantitative indicators and qualitative evaluation results in the evaluation of grid operation processes relatively well, and it effectively solves the subjectivity of the weight assignment of evaluation indicators, which can be used for the evaluation of fine actions in the grid’s operation processes.

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.