Research And Implementation On Key Technologies Of Community Health Management In IoT Environment

With the development of the times and the improvement of social living level,people’s life quality are constantly improving in many aspects,such as health,living,leisure,medical treatment,travel,etc.Meanwhile,the development of informatization has driven progress in various industries.The Internet has shortened the links between enterprises,rural areas,towns,schools,homes,hospitals,communities,and factories.Communities are the most direct and effective platform for people’s activities and information management.The community network construction can realize the sharing of resident information and data,and facilitate the communication and decision-making between people in various departments,which all benefit from the support and advantages of the Internet of Things(Io T).Since the beginning of the 21 st century,the Io T has shown a strong driving power in promoting technological innovation,economic development,and the quality of life.Meanwhile,benefiting from the development of 5G,IPv6,AI and other new technologies,the Healthy Io T,as an important branch of the Io T,plays an increasingly important role in health services and telemedicine with its incomparable advantages.However,the existing researches in this field have many deficiencies such as data redundancy,poor generality,complicated operation,low accuracy of disease prediction,high cost,etc.,and difficult to apply to the construction of community health infrastructure.This paper aims to apply data fusion methods to community health management in the Io T environment,and comprehensively discusses some key issues in community health management,such as data filtering,data fusion,and disease prediction.The main content is divided into three parts: The first part mainly analyzes the sensor data filtering method.The second part mainly discusses the disease prediction scheme based on data fusion.The third part adds a series of auxiliary functions such as data storage,user management to construct a community healthcare system on the basis of the integration of the first two parts.The main research work include belows:(1)To deal with the problem of collecting data from sensor in unreliable environments,a data filtering method of Io T sensors is discussed.Firstly,use sensors to collect environmental data,and add random noise interference to the data,then the data aggregation node composed of the ARM2440 platform performs Kalman filtering on the data to get the optimal expectations of the data,and finally upload the filtered data to internet server for analysis and storage.The experiment fully prove that the proposed method can effectively filtering the measurement errors caused by random noise interference,and fundamentally improving the accuracy and processing efficiency of the system.(2)Focusing on the problem of data redundancy in a big data environment,the data de-redundancy method is discussed.On one hand,the principal component analysis(PCA)method is used to reduce the data dimension from a horizontal perspective;on the other hand,the cluster analysis method is used to reduce the redundant data from a vertical perspective,and then the features in the sample data can be fully mined to improve the processing efficiency and quality of subsequent work.(3)Aiming at the problem of low accuracy of traditional disease prediction methods,a support vector machine prediction method optimized by genetic algorithms is proposed.The comparison results show that this method can effectively predict potential diseases.(4)Aiming at the problem of low compatibility between different devices in the traditional architecture,a community health information interaction interface based on jsonp is established,and determined a clear data exchange format to ensured the interconnection between different types of devices,and improved the portability and autonomy of information transmission.(5)Construct a data fusion healthcare system,and achieve some functions such as data collection and storage,data fusion analysis,disease prediction,message push,medical advice,and establish of health files.