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

在智能家居领域,精准预测用户潜在的使用行为是提升用户体验的一项关键技术。不当的空调操作会降低空调使用效果、增加能耗甚至降低空调使用寿命。目前,空调风险识别技术有限,不能应对复杂场景,并缺少个性化建议。提出了基于Bayesian-Transformer混合模型(BTHNM)的技术框架,通过整合用户历史行为数据、体感数据和环境数据,利用BTHNM模型可以准确地预测潜在的风险操作并给出合理的操作建议。BTHNM模型相较传统规则方法,能够捕获用户行为、时间、环境之间的深度关联关系,风险事件识别的准确率和及时性均有大幅提升。通过实验对比分析,BTHNM模型验证风险事件识别正确率比传统规则方法提升40%,建议准确率提升41%。

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 ef fect 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.