Research on an Intelligent Appearance Recognition System for Electrical Cubicles Based on Digital Modeling and AI Technology

With the rapid development of the power industry, electrical cubicles, as an important component of power systems, require increasingly stringent quality control during production. Traditional appearance inspections of cubicles rely heavily on manual operations. Given that these inspections involve extensive comparisons of characters, colors, and relative positions, manual inspection is not only inefficient but also prone to subjective errors. This paper proposes an intelligent appearance recognition system for electrical cubicles based on digital modeling and artificial intelligence (AI) technologies. By employing physical modeling and deep learning, the system aims to automate the appearance detection and inspection of cubicles, partially replacing manual labor and improving efficiency and accuracy. Through research on digital modeling techniques, prior knowledge theory, digital twin technology, and intelligent text recognition, this study offers new approaches for intelligent manufacturing and quality control of electrical cubicles.