Data Collection and AI Analysis Processing Technology Based on Digital Twins

This article aims to explore the application and effect of AI (Artificial intelligence) analysis and processing algorithm based on digital twins in complex systems, so as to improve the intelligence level and decision support ability of the system. In order to achieve this goal, this article constructs a data acquisition framework that adapts to the digital twin environment. The framework can efficiently and accurately collect multi-dimensional data from the physical environment. Futhermore, this article deeply studies the AI algorithm for digital twins, and designs and implements AI analysis and processing algorithms including DL (Deep learning) and RL (Reinforcement learning) in combination with specific application scenarios. In the experimental part, a typical digital twin scene is selected for algorithm deployment and effect evaluation. Through experiments, this article found that these algorithms performed well in equipment fault prediction, production process optimization and other scenarios, which significantly improved production efficiency, reduced operating costs, and enhanced the reliability and security of the system. The AI analysis and processing algorithm based on digital twins has significant advantages and application value. They can accurately predict and identify abnormal situations in the system, and provide strong data support for optimal decision-making.