家电科技2023，Vol.6Issue(6)：34-37,4.DOI:10.19784/j.cnki.issn1672-0172.2023.06.004
基于BayesNN和Transformer双模型融合的主动空气服务技术
Active air service technology based on Bayesian-Transformer hybrid neural model
摘要
Abstract
In the field of smart home,accurately predicting the potential use behavior of users is a key technology to improve the user experience.Improper air conditioning operation will reduce the effect of air conditioning,increase energy consumption and even reduce the service life of air conditioning.At present,air conditioning risk identification technology is limited,cannot cope with complex scenarios,and lacks personalized recommendations.A technical framework based on Bayesian-Transformer hybrid model(BTHNM)is presented.By integrating user historical behavior data,motion sensing data and environmental data,the BTHNM model can accurately predict potential risky operations and give reasonable operation suggestions.Compared with traditional rule methods,BTHNM model can capture the deep correlation between user behavior,time and environment,and the accuracy and timeliness of risk event identification are greatly improved.Through comparative analysis of experiments,the accuracy of risk event identification verified by BTHNM model is 40％higher than that of traditional rule method,and the accuracy of suggestion is 41％higher.关键词
Transformer/贝叶斯网络/试验研究/特征提取/事件识别/操作推荐Key words
Transformer/Bayesian network/Experimental research/Feature extraction/Event recognition/Operation recommendation分类
信息技术与安全科学引用本文复制引用
吕闯,邓苏鸣,林沿铮,樊其锋,高峰..基于BayesNN和Transformer双模型融合的主动空气服务技术[J].家电科技,2023,6(6):34-37,4.基金项目
广东美的制冷设备有限公司国内主动智能预警服务(RA00021359). （RA00021359）
