Research And Design On Distributed IoT Measurement And Control Scheme For Large Connection

With the development of science and technology and the acceleration of industrialization,the Internet of Things measurement and control system applicable to various scenarios has been widely used in the production and life of the people.Many domestic and foreign scientific researchers have carried out research and practice on measurement and control technology and have achieved a lot of results.However,in the case of large connections,most existing measurement and control systems have problems such as poor stability and low efficiency of measurement and control,making it difficult to complete on-site measurement and control tasks.To this end,this thesis conducts in-depth research and design of the large-connected distributed Internet of Things measurement and control program.The main research work is as follows.1.In the large connection application scenario,the distributed measurement and control system has low regional management efficiency,insufficient load capacity,and complex structure.The basic measurement and control structure is studied.The performance change is discussed by increasing the number of measurement and control nodes mounted on the system.The structural model of the distributed measurement and control system under the large connection application scenario is designed.In order to meet the requirements of distributed measurement and control functions in large connection application scenarios,the system load capacity and acquisition efficiency are improved by enhancing the interface circuit drive and design optimized bus acquisition method.By designing a reasonable anti-interference method,multi-device mounting is improved.The stability of the system;a task queue communication mechanism is designed to ensure the reliability of communication;the data processing efficiency based on the effective byte matrix is designed to improve the data processing efficiency.2.For the large-scale application scenario,the distributed measurement and control system has many types of measurement and control equipment,complicated measurement and control tasks,and low measurement and control efficiency caused by different measurement and control time scales.The task scheduling method of the distributed measurement and control system is studied and analyzed.The advantages and disadvantages of a classic task scheduling algorithm,combined with the characteristics of distributed measurement and control and time-sharing reuse in a larme connection scenario,a multi-time scale based measurement and control optimization scheme is designed.The scheme utilizes the characteristics of time scale diversification of measurement and control tasks under the condition of large connection.Using the idea of time division multiplexing,the time scale of each measurement and control task is first estimated,and then the reasonable task is based on the size of the time scale and the reuse of the measurement and control port.Priority allocation,and considering the processing mechanism under the circumstances of hardware failure non-response and time estimation error,realize optimal scheduling of measurement and control tasks.In order to verify the effectiveness of the scheme,a test platform is built.The test results show that compared with the traditional method,the measurement and control optimization scheme based on multi-time scale can effectively improve the measurement and control efficiency.3.For the problem of low system total measurement and control caused by system failure caused by long-term operation or environmental change in large connection application scenario,it is necessary to evaluate and diagnose the health status of the system,and design a measurement and control data based on the fusion matrix.The effectiveness detection method firstly establishes the fusion matrix by calculating the correlation between the measurement and control data in space and time,and then calculates the support strength of each detection point according to the threshold condition,which is used as the judgment basis of data validity to screen out the suspect.Faulty hardware device.In order to further improve the diagnostic accuracy,an SVM-based diagnostic model was designed using Embedded linux platform.The measurement and control data of the equipment in various environments was used as the training sample,and the health status of the hardware was evaluated through the analysis of the measured data.The test results show that the data validity detection scheme has high credibility,and the accuracy of the SVM-based hardware stability evaluation scheme is as high as 90%or more.In summary,this thesis has carried out a number of research work on the problems existing in the large-connected distributed Internet of Things measurement and control system,and the research results obtained have certain practical value.