• Dongyue Wang.
• Department of sports and Public Arts, Zhengzhou University of Aeronautics, Zhengzhou 450046, China. Electronic address: hnxxwdy@126.com.
• Prev Med. 2023 Aug 1; 173: 107579107579.

AbstractIn recent years, cloud computing technology has shown exponential growth, and the upgrading of hardware and the improvement of computing performance have brought significant changes to the Internet of Things industry. With the changes of the times and the emergence of many new demands, data platforms under cloud computing platforms must make corresponding changes according to the new demands. Among them, the construction of cross regional data centers is particularly important, especially in commercial environments. How to reduce the cost of data centers on cloud computing platforms while ensuring business quality has become a crucial issue. Based on the above situation, this article has optimized the bandwidth cost of the data center and solved the problem of big data transmission based on delayed big data windows and multi delay windows. A mathematical model for optimizing bandwidth cost under multi delay windows is proposed. This article also studied sports action simulation, which plays an important role in sports research, film animation, and virtual reality. Simulation actions are usually implemented based on data capture methods. However, these methods typically do not have interactivity with the environment. To enhance the authenticity and interactive ability of simulation action information collection, this article adopts reinforcement learning method for training and design, and applies a system of functions such as collecting human sports data processing. This article applies cloud computing data platforms and sports information collection to sports action simulation, making progress in the development of sports action simulation.Copyright © 2023 Elsevier Inc. All rights reserved.

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