Department of Physical Education, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, Zhejiang 312000, China.
Comput Intell Neurosci. 2022 Aug 1;2022:2594430. doi: 10.1155/2022/2594430. eCollection 2022.
Table tennis is China 's national game and the proudest sport in China's sports field. During the research and technology service work of the Chinese table tennis team for many years, it has accumulated a large amount of valuable data on the analysis of skills and tactics of training and matches, match video, training monitoring, and so on. This paper discusses the relevant theory of swarm intelligence algorithm processing big data on the table tennis training competition knowledge interaction platform system, as well as the technical support of Nginx and Tomcat, and determines the technical basis of the table tennis training competition knowledge interaction platform. Through the establishment of the firefly algorithm model, the resource search ability is enhanced, and the traditional firefly algorithm is improved. From the results of the system performance test, it can be found that the improved swarm intelligence algorithm adopted in this paper improves the global convergence, and the load balancing degree gradually decreases with the increase of time. The improved firefly algorithm shows good performance when the bandwidth is low, and the resource occupancy rate is greatly reduced. When the bandwidth is 20, it is reduced by 12.55%. It solves the shortcomings of long time and low success rate, so as to verify the convenience of the system operation and the power of functions and make the platform more intelligent and efficient.
乒乓球是中国的国球，是中国体育界最引以为豪的运动。中国乒乓球队多年来的科研和技术服务工作，积累了大量关于乒乓球训练比赛知识互动平台系统中技能和战术分析、比赛视频、训练监测等方面的宝贵数据。本文探讨了群智能算法处理大数据在乒乓球训练比赛知识交互平台系统中的相关理论，以及 Nginx 和 Tomcat 的技术支持，确定了乒乓球训练比赛知识交互平台的技术基础。通过建立萤火虫算法模型，增强了资源搜索能力，对传统萤火虫算法进行了改进。从系统性能测试结果可以看出，本文采用的改进群智能算法提高了全局收敛性，并且随着时间的增加，负载平衡程度逐渐降低。改进的萤火虫算法在带宽较低时表现出良好的性能，资源占用率大大降低。当带宽为 20 时，它减少了 12.55%。它解决了时间长和成功率低的缺点，从而验证了系统操作的便利性和功能的强大性，使平台更加智能和高效。
