Research on Advanced Digital Model of Regional Manufacturing Export Based on Computer Aided Analysis

In this paper, a new computational intelligence method is proposed to optimize the organizational decision process in industrial automation. The approach studied in this paper utilizes advanced machine learning algorithms to analyze real-time sensor data and predict system failures before they occur, enabling proactive maintenance and reducing downtime. In this paper, a multi-agent system is designed to work with the traditional manufacturing control system to improve the decision-making ability. The architecture of the system is specifically tailored to deal with the complexity and dynamics of modern industrial environments, where fast response times and accurate forecasting are critical to maintaining operational efficiency and product quality. Major contributions of this paper’s research include the development of a hybrid machine learning model that combines deep learning with evolutionary optimization techniques to improve prediction accuracy, and the integration of this model into a multi-agent system that can adapt to changing environmental conditions and evolve over time to maintain optimal decision performance.