AUTHOR=Hou Mingda , Mu Xilin , Liu Shuyong 

TITLE=Research on the application and promotion of the carbon neutral concept based on the attention mechanism in football under the end-to-end architecture

JOURNAL=Frontiers in Ecology and Evolution

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2023.1272707

DOI=10.3389/fevo.2023.1272707

ISSN=2296-701X

ABSTRACT=With the increasingly serious problems of global warming and environmental pollution, carbon neutrality has become an important concept and action guideline for coping with climate change in today's world. As an important part of society, sports should also undertake the social responsibility of environmental protection. This study aims to explore how to apply a carbon neutral scheme based on artificial intelligence attention mechanism in the field of football to help football achieve the goal of carbon neutrality. In this study, we first introduce the basic concept of an end-to-end architecture that allows all aspects of football to be integrated and optimized to achieve a comprehensive carbon-neutral goal. The end-to-end architecture provides a unified optimization platform for carbon emission reduction in football teaching activities, so that the various components involved can cooperate with each other, and comprehensively consider the trade-off of carbon emission reduction and teaching effect; secondly, we use the attention mechanisms to improve the effectiveness and interpretability of carbon-neutral strategy approaches. The attention mechanism can help the model to automatically focus on features or regions closely related to the goal of carbon neutrality, so as to achieve more accurate and effective carbon neutral strategy recommendations. In football, through the attention mechanism, we can better grasp the situation of carbon emissions, and accurately identify the key carbon emission links, so as to provide targeted suggestions for carbon emission reduction; finally, we also use the LSTM method to deal with football Time-series data in motion. Football involves complex action sequences, and the LSTM method can effectively capture the long-term dependencies in the time series, so as to better analyze and optimize the process of carbon emissions. By modeling and analyzing time series data in football, we can better understand the dynamics of carbon emissions and find more effective carbon neutral strategies. At the same 1 Sample et al.time, it also provides a reference for the application of the concept of carbon neutrality in the field of football, and provides a theoretical support for promoting the sustainable development of football.