Design And Implementation Of Anomaly Monitoring System Architecture For Secondary Heating Network Based On Private Cloud

While the urban central heating system brings convenience to people,there are also some abnormal phenomena such as excessive or too low heating temperature,loss of data uploaded by heat meters,and users stealing water and stealing heat.Based on the large amount of heating data that heating companies have accumulated,using big data related technologies to solve the above problems has become the development direction of the heating industry.However,traditional databases have not been able to meet the development of current requirements,and their performance has not been able to meet the requirements for access and statistics of massive heating data.In response to the above problems,the heating data anomaly monitoring system is based on Docker and Kubernetes to build a private cloud platform,which is used as the underlying platform for big data.Based on this,the server for monitoring node data upload timeout and the service for detecting data anomaly are developed.The system improves resource utilization and cluster expansion capabilities,simplifies service deployment and operation and maintenance,and maximizes the use value of heating data.According to the layered architecture characteristics of the private cloud's heating big data anomaly monitoring system,this paper introduces the design of each layer of the system in a bottom-up order,and explains the principle of the components in detail.According to the idea of layered architecture,the system is divided into resource management layer,big data base layer and service implementation layer to realize the isolation of business and technology,so that the system is coupled in a looser way,which is easier to maintain.This paper explains the design and implementation of large data platform based on private cloud for the above architecture design.The resource management layer focuses on the selection of Hadoop images and the process of making Zookeeper and HBase images using Docker container technology.In the big data base layer part,the structure design and implementation process of each service based on Kubernetes are introduced.In this paper,the design and implementation of anomaly monitoring service for heating secondary network are explained in detail,the specific implementation process of heating data upload delay and heating data anomaly detection service is introduced,and the specific production and deployment design of the mirror is introduced in detail.On the basis of the design and implementation of the system,this paper discusses the optimization of the system from three aspects:Docker image size and cache optimization,and HBase database row key optimization.This paper introduces the specific deployment process of the system and conducts a comprehensive and objective test of the system level.The system runs stably in the test environment and completes the functions.It satisfies the system's storage requirements for massive data,the need for heating anomaly detection,and the need for rapid system deployment,meeting the needs of the original design.Finally,this paper summarizes the problems existing in the system,and looks forward to the future expansion of system functions and the development trend of technology.