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.

In order to realize the intelligent management of a power materials warehouse, the Internet of Things based on wireless sensor networks (WSNs) is a promising effective solution. Considering the limited battery capacity of sensor nodes, the optimization of the topology control and the determination of the amount of collected data are critical for prolonging the survival time of WSNs and increasing the satisfaction of the warehouse supplier. Therefore, in this paper, an optimization problem on sensor association and acquisition data satisfaction is proposed, and the subproblem of the sensor association is modeled as the knapsack problem. To cope with it, the block coordinate descent method is used to obtain the suboptimal solution. A sensor association scheme based on the ant colony algorithm (ACO) is proposed, and the upper and lower bounds of this optimization problem are also obtained. After this, a cluster head selection algorithm is given to find the optimal cluster head. Finally, the experimental simulations show that the algorithms proposed in this paper can effectively improve the energy utilization of WSNs to ensure the intelligent management of a power materials warehouse.