Analysis and Research on Sustainable Development Factors of the Sports Industry Based on Chaos Theory

被引:3
|
作者
Luo, Yuankai [1 ]
Gao, Shanshan [1 ]
Yan, Xuanfei [1 ]
Cao, Yecheng [2 ]
机构
[1] Guangdong Technol Coll, Dept Phys Educ, Zhaoqing 526100, Peoples R China
[2] Shijiazhuang Univ, Dept Phys Educ, Shijiazhuang 050035, Peoples R China
关键词
D O I
10.1155/2022/1060639
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
The sports industry is an emerging industry with broad development prospects, and it is also full of competition. The sports industry has the characteristics of fluctuation, intermittence, and randomness, which are suitable for the analysis of chaos theory in order to find out the internal development law of the sports industry. In order to solve the above problems, an improved chaos theory method is proposed in this paper and the K-cluster analysis method is integrated into chaos calculation, in order to reduce the occurrence rate of the "local extreme value" and improve the accuracy of calculation results. The model uses nonlinear and irregular chaos theory to analyze the aggregation degree of sports industry, industrial spatial distribution, and the spatial governance effect and find out the best optimization decision. When selecting the optimization indicators, not only the European distance of each indicator cluster but also the spatial correlation of the indicators are considered to realize the comprehensive analysis of the sports industry and improve the accuracy of optimization. In the simulation analysis of optimization decision-making, the decision-making model based on chaos theory is compared with the previous first-order decision-making model. The results show that the improved chaos theory can control the data aggregation range of sports industry between (0 similar to 3), the data fusion degree of industrial space between 95 and 99%, and the variation range between 0 and 0.2%, which is significantly better than (0 similar to 9), 90 similar to 95%, and 0 similar to 0.4% of the genetic algorithm. Therefore, the aggregation degree, spatial governance, and decision optimization of the optimization decision-making model proposed in this paper are better than those of the previous genetic algorithm.
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页数:12
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