Intelligent remote monitoring and manufacturing system of production line based on industrial Internet of Things

Abstract The continuous development of Internet communication technology has given birth to the development of Internet of Things technology and the integration of intelligent manufacturing to accelerate the upgrading of traditional industrial production lines. In view of the slow speed, low fusion accuracy and other problems appeared on the current Internet of Things data fusion method, an Internet of Things heterogeneous data fusion method based on intelligent optimization algorithm is proposed to improve the Internet of Things heterogeneous data fusion effect as the goal. First, multiple nodes are used to collect the monitoring object state data. The data noise collected by each node is filtered. The data scale is initially reduced and the quality of the Heterogeneous data of the Internet of Things is improved. Then, the clustering algorithm is introduced to process the cluster head data, eliminating the redundancy between the data in the cluster. Meanwhile, the redundancy occurs between the data cluster. In the aggregation node, the cluster head data is weighted and integrated by intelligent optimization algorithm. The experiment is compared with other fusion methods under the same environment. Experimental results show that this method can effectively converge the heterogeneous data of the Internet of Things, obtain the results of the higher precision of the heterogeneous data fusion of the Internet of Things, and improve the efficiency of the fusion of Internet of Things data with fewer and faster errors of the Heterogeneous data of the Internet of Things. Finally, MATLAB simulates the proposed network transmission optimization algorithm, and verifies the reliability and stability of wireless network remote monitoring.