Optimization Research of Industrial Automation Intelligent Detection System Based on Nonlinear Internet of Things

Industrial automated inspection systems suffer from inefficiency, insufficient optimization, and poor adaptability to dynamically changing environments when dealing with complex nonlinear IoT data, making it difficult to meet the demand for intelligent and high-precision inspection. The optimization study of intelligent detection system based on nonlinear IoT architecture aims to improve the performance of industrial automation system through nonlinear optimization algorithm, edge computing and data fusion. This paper discusses the design of nonlinear IoT architecture and its co-optimization in intelligent networks, focusing on the optimization method of data transmission and processing based on edge computing, and the adaptive resource scheduling and nonlinear optimization techniques in distributed intelligent networks. The multimodal data fusion and real-time processing techniques in the intelligent detection system are investigated, and low-latency data transmission and efficient storage schemes are proposed, aiming at solving the challenges of big data processing in industrial IoT. The decision-making mechanism of the intelligent detection system is constructed through a nonlinear optimization algorithm, which is combined with a Bayesian network to achieve fault prediction and decision support, further improving the stability and fault detection rate of the system. The fault detection capability of the system is improved to a certain extent. Through the combination of nonlinear optimization algorithm and Bayesian network, the system achieves 91.0% and 88.93% fault prediction accuracies, showing high stability and fault detection capability. However, in the complex task of multimodal data fusion and real-time analysis, the fault diagnosis rate only reaches 57.2% and 28.1%, which exposes the deficiencies in the optimization of the data processing process and the integration of deep learning and Bayesian network models, and there is an urgent need to further improve the system's comprehensive performance and adaptive capability.