To address the shortcomings in action evaluation within VR simulation power training, this paper introduces a novel action recognition and evaluation method based on dynamic recognition of finger keypoints combined with an improved Dynamic Time Warping (DTW) algorithm. By constructing an action recognition model centered on hand keypoints, the proposed method integrates distance similarity and cosine similarity to account comprehensively for both numerical differences and directional consistency of action features. This approach effectively tackles the challenges of feature extraction and recognition for complex actions in VR power training. Furthermore, a scoring mechanism based on the improved DTW algorithm is proposed, which employs Gaussian-weighted feature-derivative Euclidean distance combined with cosine similarity. This method significantly reduces computational complexity while improving scoring accuracy and consistency. Experimental results indicated that the improved DTW algorithm outperformed traditional methods in terms of classification accuracy and robustness. In particular, cosine similarity demonstrated superior performance in capturing dynamic variations and assessing the consistency of fine hand movements. This study provides an essential technical reference for action evaluation in VR simulation power training and offers a scientific basis for advancing the intelligence and digitalization of power VR training environments.

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.

The visual-auditory interaction feedback can significantly influence the interactive experience of learners, highlighting the importance of using appropriate visual-auditory interaction techniques to improve the quality of virtual reality training. To investigate the effect of visual-auditory interaction feedback on learners in VR safety training, this study proposes a visual-auditory feedback interaction design method tailored for VR power safety training and validates its effectiveness through behavioral experiments. The method involves selecting representative power safety training scenarios and inviting suitable volunteers to test the feedback effects of different visual-auditory interaction approaches. The study focuses on assessing the impact of learning difficulty, learning satisfaction, and cognitive load in the training, aiming to identify the optimal visual-auditory interaction feedback combination. The results indicate that all four visual-auditory interaction feedback methods used in the experiment have significant effects (p<0.05). Notably, the combination of text and speech significantly enhances learners' satisfaction, while simultaneously reducing learning difficulty and cognitive load to the lowest levels. This research offers valuable insights and guidance for the development of interactive feedback mechanisms in virtual reality safety training. Moreover, the findings have direct implications for enhancing interactive design applications in virtual reality safety training environments.