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基于蚁群算法的城市国民体育健身需求预测方法

Method for Forecasting Urban National Sports and Fitness Demand Based on Ant Colony Algorithm.

机构信息

Department of Physical Education, Gannan Normal University, Ganzhou 341000, China.

出版信息

Comput Intell Neurosci. 2021 Dec 24;2021:5917756. doi: 10.1155/2021/5917756. eCollection 2021.

Abstract

With the continuous development of social economy, when people are pursuing economic income, they are also gradually paying attention to their own physical health. They achieve their own physical exercise through sports such as running, fitness, and mountaineering, but these sports often require a certain venue and equipment. Therefore, in view of these sports fitness demands, the ant colony algorithm is introduced to sort out the fitness activities in the context of urban residents' supply and demand relationships, analyze the demand from both subjective and objective aspects, and explore the lack of supply of sports facilities in this paper. Analysis is conducted from cognitive and national fitness, social needs, habits, and other perspectives. It tries to guide the rational allocation and creation of resources, obtain residents' fitness awareness and support, and provide corresponding suggestions and support for residents' fitness activities. The simulation experimental results show that the ant colony algorithm is effective and can support the predictive analysis of the urban national fitness demand.

摘要

随着社会经济的不断发展,人们在追求经济收入的同时,也逐渐开始关注自身的身体健康。他们通过跑步、健身、登山等运动来实现自身的体育锻炼,但这些运动往往需要一定的场地和设备。因此,针对这些运动健身需求,引入蚁群算法对城市居民供需关系中的健身活动进行梳理,从主观和客观两个方面进行需求分析,探讨体育设施供给不足的问题。从认知和全民健身、社会需求、习惯等角度进行分析。旨在引导资源的合理配置和创造,获取居民的健身意识和支持,为居民的健身活动提供相应的建议和支持。仿真实验结果表明,蚁群算法是有效的,可以支持城市全民健身需求的预测分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac70/8719991/81c3179c55fa/CIN2021-5917756.001.jpg

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