Research And Design Of Big Data Acquisition And Storage System For Injection Molding Equipment Based On Kubernetes

The rapid development of information technology,represented by big data,cloud computing and artificial intelligence,has given a profound and powerful impetus to the fourth transformation of world industry,marking the advent of an intelligent era of using information technology to promote industrial change.In the global industrial development,the plastic industry holds the extremely important status.The innovation and transformation of the plastics industry is taking place all over the world.China has become a major consumer and producer of plastic products.The market of plastic products industry has broad prospects for development.With the sustained and steady development of the national economy,the injection molding equipment industry has achieved leap-forward development,and the scale of the industry has been expanding.However,compared with industrial developed countries,there is still a certain gap,such as the level of industry intelligence and informatization is low,and most enterprises are labor-intensive enterprises,the low production efficiency and added value of products,those questions have seriously restricted the development of China's plastic products industry.In view of the problems faced by the injection molding equipment industry,this paper researches and designs the big data acquisition and storage system of injection molding equipment,and builds the expandable,reliable and easy to transplant injection molding equipment by using big data and cloud computing technology.Big data acquisition and storage system provides key data for offline product quality and process parameter relationship big data analysis,data relationship model building,and online quality monitoring this is of great significance for the injection molding equipment industry to optimize production and improve product quality,promote data sharing and integration in the injection molding industry,build injection molding clouds,and promote the intelligentization and informationization of the injection molding industry.The main research and design content of this paper includes the following aspects:1.Based on the industrial process control standard protocol OPC and MES production management system,building the injection molding machine production data acquisition system,and realizing the on-site collection and data uploading of injection molding machine production.2.Based on the NiFi data integration tool,building a NiFi data stream cluster,and deploying a variety of data stream processors to achieve multiple data sources for injection molding production data receiving and visual processing of data stream.3.Analyzing the big data reception and processing requirements of injection molding production,building a Kafka cluster using high-throughput Kafka distributed publish and subscribe message system,so that can cache injection molding production data as a data source for offline analysis and real-time processing of big data,and decouple big data reception and processing.4.Analyzing the cloud computing requirements of big data acquisition and storage systems for injection molding equipment,designing and building Kubernetes cluster in order to manage and deploy NiFi clusters,Kafka clusters,Kafka consumer clusters and HBase distributed databases based on container orchestration technology Kubernetes and container virtualization technology Docker,thus improving the scalability,reliability,and portability of big data systems and preparing for building data clouds of injection molding industry.