工业人工智能白皮书2025年版：边缘AI驱动，助力新质生产力

Investment Rating - The report does not explicitly provide an investment rating for the industrial AI sector Core Insights - The rapid development of AI technology is driving a new industrial revolution, transitioning industries from labor-intensive to technology-intensive and knowledge-intensive models, enhancing productivity through AI, big data, and cloud computing [3][4] - Industrial AI and large models are penetrating various industrial processes, enabling optimal decision-making and enhancing competitiveness across product design, process management, predictive maintenance, supply chain optimization, and more [3][4] - The industrial AI market is expected to grow at a compound annual growth rate (CAGR) of 46% from 2022 to 2032, with AI application rates in Chinese manufacturing currently around 11% [9][10] Summary by Sections 1.1 Industrial AI - Market Size and Growth Potential - Industrial AI is projected to grow rapidly, with a CAGR of 46% from 2022 to 2032, driven by the need for digital transformation in manufacturing [9] - AI application rates in China are relatively low at approximately 11%, but are expected to rise at a CAGR of 10% by 2027 [9] 1.2 Application Scope of Industrial AI - AI technology is applied across various industrial sectors, including product design, production, management, and service, optimizing resource allocation and enhancing efficiency [13][14] - Key applications include demand analysis, R&D process optimization, automated code generation, and product structure optimization [16] 1.3 Industrial Large Models - Large models are characterized by their ability to handle vast amounts of data and perform complex tasks, including natural language processing and computer vision [24] - Industrial large models are designed for application across various industrial sectors, offering strong generalization capabilities and lower development costs compared to specialized models [26] 1.4 Industry Applications - Automotive Industry: AI enhances production efficiency and product quality through applications in quality inspection, logistics, and assembly line automation [35] - Consumer Electronics: AI is crucial for maintaining high quality in fast-paced production environments, addressing complex defect types in products like smartphones [37] - New Energy Lithium Battery Industry: AI improves quality control and accelerates material selection and design processes, significantly impacting production efficiency [39] - Semiconductor Industry: AI optimizes circuit design and defect detection, crucial for maintaining high production standards in a complex manufacturing environment [44] 1.5 Challenges in Implementing Industrial AI and Large Models - Data quality and quantity, computational power, model accuracy, real-time response, and cost are significant challenges facing the deployment of industrial AI and large models [48][51][54][56][57]