Research And Application Of Distributed Energy Efficiency Management Platform Based On Kafka

In recent years,with the rapid development of information technology and Internet technology,the global data volume also shows an explosive growth trend,and the era of big data is coming.This will bring about tremendous changes in all fields of scientific research and will profoundly affect all aspects of our future life.The Kafka-based distributed energy efficiency management platform proposed in this thesis is of innovative significance.The platform's design goal is to distribute the massive stream data input by the system into the cache and to communicate between microservices.The main work of this thesis: a detailed description of the overall architecture of the platform design,it has been divided into three levels of the entire system,namely the control layer,the middle service layer and micro-service layer;take full advantage of Kafka in data transmission advantages,Kafka as Messaging server,to achieve communication between the various microservices.Based on this,a Kafka-based mass flow data distributed energy efficiency management platform based on Kafka is designed and realized to analyze,process and display real-time data collected by metering devices such as meters and water meters,and to make the energy use scientific,intelligent and sophisticated.Finally,the availability of experiments to verify its effectiveness and scalability.The uniqueness of this paper is as follows:(1)Using Kafka distributed message intermediate key to cache massive stream data based on the traditional energy efficiency management platform,the stability of the whole system is improved when the data input of the stream is abruptly changed.(2)Research and improve the Kafka native Consumer load balancing algorithm and data consumption reliability,and improve the efficiency of Kafka server cluster load balancing and message consumption reliability.(3)Design and implement the direct decoupling of microservices and efficient and secure communication methods to meet the needs of the platform scalability and system throughput.This thesis hopes that the platform research and implementation process can provide new methods and ideas for the construction of distributed computing clusters and mass data processing,and hope that through continuous research,and gradually improve the platform system.