一种基于智能家居的用户行为预测方法

随着移动通信技术、物联网技术和传感器技术等的快速发展,智能家居行业发展迅速。由于人们生活水平的提高,对智能家居可以提供的智能服务需求正在增加。然而,现有的智能家居系统只能根据预设的控制方法和规则简单地重复运行,并且根据用户的日常生活习惯,不能随时提供满足其个性化需求的服务。试图为智能家居提供个性化服务,使智能家居的服务能够更加灵活、智能和人性化,报告了智能家居和关联规则挖掘的研究现状,对提高Apriori算法的效率进行了研究,设计了原型系统中的数据采集和预处理,网关以及行为识别和预测3个功能模块的总体实现方案。实验结果表明,采用关联规则数据挖掘的方法可以预测智能家居环境下用户未来的行为,同时基于散列技术的Apriori算法提高了智能家居下用户行为预测过程中的效率。

A User Behavior Prediction Method Based on Smart Home

With the rapid development of mobile communication technology,Internet of Things technology and sensor technology,the smart home industry is developing rapidly.Due to the improvement of people's living standards,the demand for smart services that smart homes can provide is increasing.However,the existing smart home system can only be repeatedly repeated according to preset control methods and rules,and according to the daily habits of the user,services that meet their individual needs cannot be provided at any time.We try to provide personalized services for smart homes,so that the smart home services can be more flexible,intelligent and user-friendly.The research status of smart home and association rules mining is reported,and the efficiency of improving Apriori algorithm is studied.On the basis,we design the overall implementation of the three functional modules of the prototype system including data acquisition and previous treatment,gateway and behavioral recognition and prediction.The experiment shows that the association rule data mining method can predict the future behavior of users in the environment,and the Apriori algorithm based on hash technology improves the efficiency of user behavior prediction in the smart home.