Intelligent production line is the abbreviation of intelligent production line. Intelligent production line refers to a form of production organization that uses intelligent manufacturing technology to realize the production process of products. The actual manufacturing process includes different levels and links, and each step cooperates to create a high-efficiency production line. The intelligent production line includes 3 levels covering automation equipment, digital workshops, and intelligent factories and runs through 6 major links of intelligent manufacturing (intelligent management, intelligent monitoring, intelligent processing, intelligent assembly, intelligent inspection, and intelligent logistics). The emergence of the Internet of Things system has changed the way of information dissemination. The system combines radio frequency automatic identification and global positioning system technologies to achieve functions such as information exchange and processing, enabling information processing to be intelligent and improving resource utilization. Big data processing includes multiple data processing procedures, but data quality is the most important link in the entire process, and each data processing link will have an impact on the quality of big data. The big data processing process mainly includes data collection, data preprocessing, data storage, data processing and analysis, data display, data visualization, data application, and other links. This article aims to study the new progress of artificial intelligence algorithms for big data processing of IOT systems on intelligent production lines. It is hoped that through the development of intelligent production lines and big data processing technologies, ways to optimize artificial intelligence algorithms can be found. This study proposes a metadata replication method based on a separate replication strategy, which separates the replication process of the data operation log, each is independent, and shortens the data replication time. Combining the existing intelligent production line network platform in the laboratory and carrying out the research of the intelligent production line network state prediction system based on the neural network to design a network prediction system can prejudge the operation status of the intelligent production line network. The experimental results in this article show that when the Namenode mode is used to read data and when the number of clients reaches 8, the data processing basically remains unchanged. When the NCluster system reads data and when the number of clients is 6, the data is processed 1256. When the number of clients is 20, the data is processed 2100, the NCluster system will remain stable when the number of clients reaches 12, and compared with the Namenode system, it has obvious advantages.
A mature coordinated and optimized control system can bring timeliness and benefits to the production line. This paper firstly exemplifies the big data intelligent production line coordination control system model and algorithm. They, respectively, include the intelligent production line coordination control system model, the big data intelligent coordination control model, and the hardware key electrical control system model of the air conditioning assembly big data intelligent production line, taking the tea production line as an example. The parameters of the coordinated control model are identified by the algorithm and system design, which considers and adjusts the functional requirements of the system to maintain the stable operation of the software system and store and transmit efficiently. To achieve efficient fit between functional requirements and modules, it created a complete assembly automation production line that meets actual needs. Then, the experiment of big data intelligent coordination and optimization control system based on cement clinker production line is carried out, and the intelligent coordination control system of cement clinker is designed. Compared with the data, it can be seen that in the coordinated control mode, the fluctuation of the temperature and pressure of the main steam is smaller, which can make the entire power generation system operate safely and stably. Based on the fuzzy control of the waste heat boiler and steam turbine load coordination control requirements, the reasonable coordination control of the boiler system and the generator set is carried out through fuzzy control, and the optimization and adjustment are carried out. Finally, the comparison and analysis of fuzzy control and traditional control experiments are carried out, and a coordinated optimization system using fuzzy control is obtained. It requires less feeding material, consumes less energy in the production process, generates more power, and can increase the average income by 4.87%, which has a good practical application prospect.
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