Machine Vision Nondestructive Inspection System Assisted by Industrial IoT Supervision Mechanism

This paper introduces the development status of machine vision nondestructive testing technology and industrial IoT supervision mechanism. The study designs and implements a machine vision nondestructive testing system from two aspects: construction of industrial IoT supervision and detection model, and optimization of machine vision nondestructive testing algorithm. In this paper, the random deployment of dynamic and static nodes is adopted. The coverage rate after random deployment and the moving distance of dynamic nodes are two necessary research parameters. To improve the initial coverage and optimize the mobile path of dynamic nodes, this paper proposes a mobile deployment optimization scheme based on the supervisory mechanism model of industrial IoT, which improves the traversal of the quantum genetic algorithm by improving the genetic variation rules, thus improving the initial deployment of the network. The optimized machine vision nondestructive detection algorithm is used for mobile path optimization from dynamic nodes to target locations. Simulation results show that a random deployment of 100 static nodes and 20 dynamic nodes in a 400 m × 400 m factory area works best with a coverage rate of 6.719% and an average movement distance of 23.47 m, and the movement path avoids the obstacle area. The average accuracy of the modified machine vision nondestructive testing system is 1.59% higher than that before the modification, and the average detection accuracy of the final experiment reaches 95.46%. Not only is the coverage rate better than that of the cellular structure-based dynamic node optimization scheme, but also the monitoring range of the plant tends to be more comprehensive in the actual deployment environment. Through the analysis of test results, the system achieves the monitoring and display of data on the one hand and provides a natural information access and interaction experience for IoT managers on the other hand, which meets the requirements of real time, accuracy, and stability of industrial IoT information data to a certain extent.