Research And Implementation Of Data Acquisition And Real-time Processing Technology In Process Intelligent Manufacturing Workshop

With the rapid development of the Internet of Things and information technology,the degree of intelligence in the manufacturing industry is also gradually increasing,and it is moving towards the direction of "intelligent manufacturing".The Internet of Things,real-time data collection and analysis processing,and intelligent decision-making are the core technologies of intelligent manufacturing.Through real-time collection and analysis of data generated during production equipment,environment,products,and material processing,production decision-makers can understand the production process of products in real time.And according to the equipment abnormality,logistics abnormality,quality problems and other conditions to carry out corresponding production scheduling,so as to improve the production efficiency of the workshop and improve product quality.The collection and real-time processing of production data has become a research hotspot in the current intelligent manufacturing field.This thesis aims at production monitoring and production scheduling decision support for process intelligent manufacturing workshops.The research of production data collection and real-time processing technology is carried out.The OPC data collection client is designed and implemented.A multi-source data classification and real-time processing model based on sliding window is established.Based on the production data collection and real-time processing and the automatic identification of product batches requirements of the process manufacturing company in Sichuan Province,the data acquisition and real-time processing system for process manufacturing workshop.The specific research is as follows:(1)According to the characteristics of the data to be collected in the process production workshop,large amount of data,many types and different collection frequencies,the message middleware was introduced to design and implement the OPC client that is compatible with asynchronous and subscription data reading methods and supports multiple data dump schemes.(2)For the problem of data exception caused by the interference of the actual production environment of the workshop and network factors,the data classification and real-time processing model based on sliding window is proposed.According to the characteristics of the data,the data is classified into three types: environment/equipment status data,equipment switch data and equipment logic data.And according to the data type,the data exception processing algorithm was established.(3)For the automatic identification of product batches requirements of the process manufacturing company in Sichuan Province,according to the product manufacturing process,the automatic identification of product batches model and algorithm based on collected data is established.And realized automatic association between product batches and raw material batches.On this basis,the development and application testing of the prototype system are completed.Finally,the prototype system was put into use in the actual production environment,verifying the applicability of the OPC data collection client,the compatibility of the data classification and real-time processing model based on sliding window for different tasks and different data types,and the reliability of the automatic identification of product batches model and algorithm.The results show that the application of the prototype system improves the production efficiency of the enterprise,and the error rate of product batch judgment is less than 1%,which has practical application value.