Application of research on carbon neutral strategy of physical education teaching based on attention mechanism in digital energy transformation - Yuan, Tianlei; Cai, Feng; Han, HuiJun (Frontiers in Environmental Science, 2024)
With the global goal of carbon neutrality being emphasized, the implementation of carbon-neutral strategies has become a crucial task across various domains. As an integral part of social activities, physical education also necessitates considerations on how to reduce carbon emissions and implement carbon-neutral strategies within the teaching process. This study focuses on physical education and explores carbon-neutral strategies based on an end-to-end architecture with an attention mechanism. Firstly, we introduce an end-to-end framework that enables the integration and optimization of various aspects within the teaching process to achieve comprehensive carbon-neutral objectives. This framework serves as a unified optimization platform, facilitating the collaboration of different components involved in teaching activities and balancing the reduction of carbon emissions with teaching effectiveness. Secondly, we employ Convolutional Neural Networks (CNN) as the foundational model within the end-to-end architecture. Through training the CNN model, we automate the analysis of carbon emissions during the teaching process and provide corresponding carbon-neutral recommendations for different segments. Most importantly, we incorporate an attention mechanism to enhance the effectiveness and interpretability of the carbon-neutral strategy. The attention mechanism assists the model in automatically focusing on features or regions closely related to carbon-neutral objectives, thereby achieving more accurate and efficient carbon-neutral strategy recommendations. Finally, we conduct training and testing on the proposed model using a dataset constructed from carbon-neutral scenarios in physical education (the country where physical education occurred and digital energy have been scrutinized). The results demonstrate that the improved model surpasses a 90% threshold in mainstream evaluation metrics such as Action Recognition Accuracy (ARA), Action Recognition Recall (ARR), and Action Optimization Rate (AOR). The enhanced model exhibits notable improvements in inference speed and accuracy.