Research And Development Of Smart Electricity Supervision System Based On Rough Set Theory

Under the current background of the accelerated economic development and continuous urbanization,lots of electrical devices are deployed in smart city as the infrastructure for data collection,which facilitates the collection of massive data in the urban environment.The interwined electric wires caused by the deployment of lots of powerconsuming smart devices,which have brought huge security risks to the city governance and residents’ lives.Therefore,how to effectively prevent the hidden dangers of electric wires and avoid the occurrence of electrical fires has become one of the research hotspots.Benefitting from the rapid development of new-generation information technologies,such as the Internet of Things,artificial intelligence,and cloud computing.We can collect the real-time data from the intelligent sensing devices,and utilize the new-generation information technologies to model and monitor the data,which can improve the performance of the resource monitoring and electical fire warning.It has become a new breakthrough in the electrical fire protection problem.This thesis aims to establish a smart electricity supervision platform,which can realize the monitoring and early warning of electrical hidden dangers,as well as the real-time supervision of fire resources.Firstly,this thesis conducts a comprehensive research on the use demand of the smart electricity supervision platform.According to the business division and functional requirements,the management platform is designed into five subsystems,which can realize the data visualization,electrical hidden danger monitoring,alarm,fire resource supervision,fire protection inspections,etc.Secondly,the physical structure,logical layering and technical architecture of the supervision platform are designed by combining technologies such as the Internet of Things and big data processing.Thirdly,for obtaining a fault decision table,the rough set theory is used to reduce the attributes of the collected hidden danger factor data,and then diagnose the current electrical faults according to the characteristics of the data.Further,aiming at the high concurrency and high availability operation requirements of smart electricity projects,a Codis distributed cache is used to provide services,and the native Codis architecture is optimized to meet the high availability requirements of power consumption in the smart city.Finally,the rough set-based diagnosis mechanism is integrated into the analysis module of the system,and through the comparative experiments between different architecture systems found that the optimized Codis architecture supervision platform is superior to the native caching architecture in scalability,stability and responding speed.