Research On Smart Home Design Scheme Based On All Joyn Framework

In recent years,with the combination of the Internet and traditional manufacturing industries,intelligent hardware devices of various functions have mushroomed and gradually entered the home.Coupled with the popularization of smartphones,they provide a quick entrance for the connection between people and things.The field of smart home will quickly become the focus of current research,and major domestic and foreign manufacturers have layout of smart home ecology.However,in order to occupy the market preemptively,the mainstream smart home products currently on the market simply implement some human-triggered functions such as "condition triggering" and "timed triggering",which are of low intelligence and cannot meet people's needs.And some smart homes are designed for highly customized home environments that have poor versatility and are not adaptable to different home environment.As a popular direction of artificial intelligence,machine learning has developed rapidly in recent years and achieved breakthroughs in many fields.At present,the combination of machine learning technology and Internet of Things technology is a new trend to solve the problem of low intelligence level of smart home products,which provides a new way of thinking for the future development of the smart home industry.To this end,this paper presents a smart home design based on AllJoyn and machine learning to realize the intelligent control of equipment and improve the intelligence of the products.The specific contribution of this article can be summarized as the following three aspects:(1)In the design of machine learning system,this paper presents an improved neural network prediction model based on LSTM,which is used to predict the status of devices in the home based on environmental data.In the design of LSTM neural network module,this paper adopts three neural network layers and three "gate" layer structures,coupled "forgotten gate" and "input gate".And introduce "peepholes" between the "entry door" and "exit door" layers.Through experimental comparison,based on the same data set,the prediction accuracy of this model is better than BP neural network,RNN and standard LSTM neural network model.(2)In the design of machine learning system,this paper presents a design method for managing and updating forecasting models to achieve the versatility of the smart home design.The machine learning system model management module is responsible for creating,acquiring and saving different family prediction models.The model updating module is responsible for updating the prediction model according to the new sample data.The experiment proves that the machine learning system supports multiple home nodes and adapts to different family environment.(3)By combining the AllJoyn framework and machine learning system,this paper presents a control scheme for smart home devices,connecting the smart devices to the machine learning system.AllJoyn framework is used to realize the communication among the smart devices in the LAN.The AllJoyn gateway proxy is designed to connect the LAN with the WAN.Using the interaction between the machine learning system and the server,the device status is predicted according to the environment data.The server sends the corresponding device control instruction to the gateway according to the predicted device status,so as to realize intelligent control of the device status.The actual scene tests prove that this smart home design can intelligently control the state of equipment,and can adapt to the new characteristics of home environment,effectively improving the intelligence level of smart home.