Intelligent Control and Electronic Information Processing Optimization Algorithm for Industrial Automation

With the rapid growth of science and technology, China's industrial development capacity is steadily increasing, especially in the field of energy development and sustainable utilization, where significant achievements have been made. However, in complex industrial environments, these advancements still face many challenges. Such environments often have high latency, nonlinear characteristics, and strong coupling, which are intertwined with each other, making achieving stable and accurate continuous control a daunting task. To address these challenges, this article innovatively proposes an intelligent control system based on deep learning (DL). This system combines the powerful data processing capabilities of DL with the optimized search characteristics of genetic algorithms (GA), aiming to precisely navigate complex industrial environments. DL is responsible for mining deep features of data, while GA simulates natural selection and genetic mechanisms to explore optimal solutions in a vast solution space. The synergistic effect of the two significantly improves the control efficiency of the system. The experimental results show that the intelligent control system designed in this paper exhibits excellent stability and accuracy in complex industrial environments.