基于多目标粒子群算法的运动饮食推荐机器人研究  

Research on Sports and Diet Recommendation Robot Based on Multi objective Particle Swarm Optimization Algorithm

作　　者：刘响 徐向前 LIU Xiang;XU Xiangqian(Xi’An Mingde Institute Of Technology,Xi’an 710124,China;Shandong Sport University,Rizhao,Shandong 276826,China)

机构地区：[1]西安明德理工学院,西安710124 [2]山东体育学院,山东日照276826

出　　处：《自动化与仪器仪表》2024年第10期258-262,共5页Automation & Instrumentation

基　　金：国家社会科学基金项目《体育强国建设背景下我国竞技体育与群众体育共生发展研究》(21BTY021)。

摘　　要：此次研究提出一种基于多目标粒子群算法的运动饮食推荐模型,以推动个性化的运动和饮食建议机器人行业的发展。研究采用多目标粒子群优化的多标记多变量模型对个体的运动和饮食计划进行寻优,实现高度个性化的推荐。研究结果表明,提出模型对特征提取的准确率在收敛后达到了83.71%,召回率稳定在75.19%,平均F1值达到了87.07%,同时该模型其他性能在测试中也表现良好。该模型能够根据用户的健康状况、运动饮食偏好等因素,为用户提供个性化的运动和饮食建议。此外,该模型还具有较高的准确性和稳定性,能够有效地处理各种复杂情况,为用户提供更加精准的建议。这种模型在运动饮食推荐领域具有广泛的应用前景,可作为当前运动饮食推荐机器人构建的核心模型。This study proposes a sports and dietary recommendation model based on multi-objective particle swarm optimization algorithm to promote the development of personalized sports and dietary recommendation robotics industry.The study adopts a multi-objective particle swarm optimization multi label multivariate model to optimize individual exercise and dietary plans,achieving highly personalized recommendations.The research results show that the accuracy of the proposed model for feature extraction reached 83.71%after convergence,the recall rate remained stable at 75.19%,and the average F1 value reached 87.07%.At the same time,the model also performed well in other performance tests.This model can provide personalized exercise and dietary recommendations for users based on their health status,exercise and dietary preferences,and other factors.In addition,the model also has high accuracy and stability,which can effectively handle various complex situations and provide users with more accurate suggestions.This model has broad application prospects in the field of sports and diet recommendation,and can be used as the core model for constructing current sports and diet recommendation robots.

关 键 词：多目标粒子群 运动饮食 机器人 深度学习 多标记多变量模型 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP23[自动化与计算机技术—控制科学与工程]