2025年8月26日，国务院发布《国务院关于深入实施"人工智能+"行动的意见》。政策明确提出：推进工 业全要素智能化发展。推动工业全要素智能联动，加快人工智能在设计、中试、生产、服务、运营全环 节落地应用。工业和信息化部副部长张云明明确提出"对于实现新型工业化这一关键任务，发展人工智 能是必答题而非选择题。" 工业AI是中国AI发展的特色战略方向 智通财经APP获悉，东吴证券发布研报称，中国工业大模型和工业智能体加快演进，工业大模型百花齐 放，原先各个行业的工业信息化和数字化公司大多都发布了基于本公司产品和本行业的工业大模型。工 业智能体渗透比例逐步提升。国家政策大力支持AI赋能新型工业化，该行预期后续会有工业AI相关支 持政策落地，工业AI行业有望迎来加速发展。 东吴证券主要观点如下： 工业AI已经进入广泛普及阶段 工业AI是AI技术在工业领域的应用，它通过机器学习、深度学习、计算机视觉等先进的计算智能方 法，实现对工业生产过程的优化和智能化，最终帮助企业提高生产效率、降低成本、提升产品质量，实 现数字化转型。工业大模型发展正从初步兴起走向广泛探索阶段，越来越多的工业企业开始布局使用工 业AI技术。 工业A ...

Core Insights - The global industrial intelligence market is expected to exceed 3.5 trillion yuan this year, with China holding over 40% of the market share [1] - The Ministry of Industry and Information Technology emphasizes the development of industrial intelligence systems to enhance AI applications in the industry, focusing on typical scenarios for research and application [1] Industry Overview - Industrial intelligence systems integrate AI, big data, and cloud computing, enabling intelligent decision-making and collaboration, significantly transforming manufacturing processes [2] - These systems reduce reliance on individual experience in R&D, shorten development cycles, and optimize production scheduling and equipment maintenance [2] Market Trends - Various regions in China are implementing policies and initiatives to accelerate the application of industrial intelligence systems, moving from laboratory settings to practical industry applications [3] - The Yangtze River Delta electronic information industry cluster has improved cross-enterprise process design efficiency and supply chain responsiveness through collaborative platforms [3] - By 2028, AI spending by Chinese industrial enterprises is projected to reach 90 billion yuan [3] Challenges and Solutions - Effective application of industrial intelligence systems requires overcoming data bottlenecks, such as data silos and complexity, necessitating the integration of various data sources [4] - Companies need to establish robust data management systems to support advanced intelligent applications [4] Technical Development - The complexity of industrial scenarios demands the development of adaptable and flexible AI platforms that can address technical challenges like computing power adaptation and model compression [4] - There is a need for deep integration of technological advancements with industry-specific demands to maximize the value of industrial intelligence systems [4] Talent Development - The successful implementation of industrial intelligence systems relies on a skilled workforce that understands both industrial mechanisms and AI technologies [4] - Emphasis on talent cultivation and education in AI across all levels is crucial for enhancing the overall AI competency within the industry [4]

Core Insights - Chongqing's digital industry is projected to achieve a business revenue of 991.02 billion yuan in 2024, marking a year-on-year growth of 6.1%, which is 0.6 percentage points higher than the national average [1] - The integration of the real economy and digital economy is emphasized as a strategic move to seize opportunities from the new technological revolution and industrial transformation [1] Group 1: Digital Transformation and Infrastructure - Chongqing is advancing its industrial digital transformation, with the national top-level node for industrial internet identification covering 10 provinces in the west, processing over 42.6 billion identification codes and connecting 40,000 enterprises [2] - The city has established 1,231 digital workshops, 211 smart factories, and 10 future factories, leading the western region in the construction of 22 national-level excellent smart factories [2] Group 2: Industry Competitiveness and Growth - The smart connected new energy vehicle industry in Chongqing has seen a production increase of 26.8% year-on-year, with 586,000 vehicles produced from January to July this year [3] - The new generation of electronic information manufacturing continues to upgrade, with Chongqing maintaining the world's highest production of laptops for 11 consecutive years, and AI computer production reaching 4.057 million units with a penetration rate of 12.8% [3] - The software and information service industry in Chongqing achieved a main business revenue of 269.01 billion yuan from January to July, reflecting a year-on-year growth of 19.4%, ranking eighth nationally [3]

Core Insights - The article discusses the transformation of the industrial internet, emphasizing the shift towards "Industrial Internet 2.0" with a focus on smart factories and intelligent platforms as core strategies for enhancing efficiency and decision-making in manufacturing [1][2]. Group 1: Industrial Internet Transformation - The industrial internet is evolving, with a core path defined as smart factories combined with intelligent platforms, aiming to address challenges such as rising costs and intensified competition in the manufacturing sector [1][2]. - The concept of "Industrial Intelligence" is introduced as a new paradigm for scaling Chinese manufacturing, highlighting the importance of intelligent decision-making and autonomous optimization alongside connectivity and data [2][3]. Group 2: Technological Implementation - The company has developed a low-code platform called "Lingxi Industrial Operating System," which streamlines the development process, resulting in a 59% increase in development efficiency and a 70% reduction in operational costs [2][3]. - Six key scenarios have been identified for practical application, including industrial governance and supply chain management, which have been validated through extensive real-world testing [2][3]. Group 3: Global Expansion Strategy - The company is preparing for international expansion, aiming to export its industrial internet and intelligent capabilities, particularly targeting the light industry sectors that require flexible production lines and rapid product iteration [4]. - The shift from exporting products to exporting capabilities signifies a strategic transformation, focusing on providing integrated smart solutions rather than just hardware [4]. Group 4: Talent Development - A new talent cultivation philosophy called "PhD in the Workshop" is being implemented to bridge the gap between technology and manufacturing processes, fostering a new generation of professionals who understand both domains [5]. - Collaborations with universities to establish industrial internet academies aim to produce versatile talents capable of engaging in real projects and gaining practical experience [5].

Group 1 - The core viewpoint is that China's industrial internet sector is rapidly developing, with over 340 influential platforms and more than 100 million connected devices, covering 41 major industrial categories and 185 subcategories [1] - The industrial internet is seen as a key pathway for integrating the real economy with the digital economy, driving technological and industrial innovation [1] - The core industry added value of the industrial internet exceeds 1.5 trillion yuan, significantly promoting the high-end, intelligent, and green development of manufacturing [1] Group 2 - The integration of artificial intelligence (AI) with the industrial internet is accelerating innovation and unlocking the value potential of data elements [2] - AI and industrial internet convergence is redefining industrial production paradigms, with applications improving product quality and operational efficiency [2] - The deployment of industrial AI models is transitioning from experimental stages to practical applications, enhancing manufacturing efficiency across various processes [2] Group 3 - Companies like Yili Group are advancing digital transformation by leveraging AI and cloud computing to enhance supply chain efficiency and data analysis capabilities [3] - The implementation of AI-driven business intelligence tools has significantly reduced data query times and improved decision-making processes within supply chains [3] - The investment in data engineering for smart transformation projects is recognized as a critical factor, with a consensus on the increasing returns from data elements [3] Group 4 - Experts recommend a dual empowerment approach for industrial internet and AI, focusing on building high-quality data sets and developing industrial AI models [4] - The strategy emphasizes the importance of open intelligent upgrades of industrial networks to enhance platform service value and diversify application models [4]

Group 1: Digital Transformation in Manufacturing - Yiyi Dairy has improved its operational management through smart upgrades and digital transformation, achieving a 27% increase in automation rate, a 4% improvement in product quality, and a 25% reduction in labor [1] - The Anhui province is accelerating digital transformation in manufacturing, with the upcoming 2025 World Manufacturing Conference serving as a platform to showcase achievements in smart factory construction and industrial internet applications [1] - The industrial internet is identified as a crucial area for digital transformation, with the Liangyang Industrial Internet platform demonstrating significant performance in the competitive landscape [2][4] Group 2: Industrial Internet Platforms - The Liangyang Industrial Internet platform supports companies like Aotegia Technology in enhancing production efficiency and product quality through precise data support [4] - Anhui has cultivated 92 provincial-level industrial internet platforms, connecting over 11 million devices and serving more than 500,000 enterprises, ranking sixth nationally in industrial internet platform development [6] Group 3: 5G Factory Initiatives - Anhui's 5G factory initiatives have become a significant marker of digital transformation, with 80 enterprises recognized in the national 5G factory directory, ranking fifth in the country [11] - The integration of 5G and industrial internet has led to substantial improvements in operational efficiency and cost reductions for companies like Zhong'an United Coal Chemical and Tianneng Battery [11] Group 4: Talent Development and Support Systems - Anhui plans to cultivate over 300,000 digital economy professionals by 2027, with a focus on training in ten key industries [13] - The province has established a digital transformation expert committee and organized numerous training and consulting activities to support enterprises [13] Group 5: Future Outlook - The Anhui provincial government aims to further enhance the policy framework and service systems for manufacturing digital transformation, with a focus on AI empowerment and high-quality economic development [14][15] - The upcoming World Manufacturing Conference is expected to highlight Anhui's digital transformation achievements and promote the province's manufacturing sector towards high-end, intelligent, and green development [15]

Core Insights - The article highlights the rapid development and application of industrial internet in China, with over 30,000 basic smart factories and more than 1,200 advanced smart factories established, indicating a significant transformation of data into productive capabilities [2] - The Chinese government has issued opinions to deepen the integration of artificial intelligence (AI) and industrial internet, emphasizing its role as a strategic infrastructure for new industrialization [2] Group 1: Characteristics of AI and Industrial Internet Integration - The integration of AI and industrial internet exhibits three core characteristics: 1. **Scenario-based**: AI is deeply embedded in critical industrial processes such as equipment health management and process optimization, enhancing efficiency through real-time decision-making and predictive maintenance [3] 2. **Ecosystem-based**: A collaborative ecosystem has emerged, characterized by large enterprises leading and small to medium enterprises collaborating, exemplified by partnerships like Siemens with smaller service providers [3] 3. **Embodied intelligence**: Traditional equipment is upgraded to "embodied intelligent agents" through enhanced sensing, computing, and decision-making capabilities, enabling autonomous optimization and interaction [3] Group 2: Mutual Empowerment of AI and Industrial Internet - The industrial internet serves as a core carrier for "AI+", creating a bidirectional empowerment relationship: 1. The industrial internet empowers AI by providing scalable application scenarios, facilitating the transition from technology development to industrial application, and driving innovation and value release in the industrial sector [4] 2. AI reshapes the industrial internet by addressing the "data gap" in the integration of industrial data and reality through deep integration of data, algorithms, and computing power, evolving the industrial internet from a tool-based platform to an "outcome-as-a-service" intelligent ecosystem [4] Group 3: Future Prospects and Market Growth - The demand for industrial internet applications in China is surging, with coverage across 41 major industrial categories and a projected core industry scale exceeding 1.5 trillion yuan by 2024 [5] - The industrial internet is accelerating the transformation of China's manufacturing sector towards new and intelligent paradigms, with future innovations in models, technologies, and services expected to deepen global integration and provide solutions for global industrial chain upgrades [5]

Core Insights - The article emphasizes the rapid expansion of artificial intelligence (AI) across global industries, particularly in manufacturing, which is undergoing a transformation from automation to autonomy [2] - AI's evolution is marked by significant milestones, including the transition from philosophical inquiries about machine intelligence to practical applications that permeate daily life [3] - The manufacturing sector is identified as a strategic high ground for AI technology implementation, with a focus on enhancing production methods and business models through deep integration of AI [7] AI Evolution - AI has progressed through various stages, starting from philosophical discussions to practical applications, with notable breakthroughs such as deep learning in image recognition and AlphaGo's victory over a world champion [3][4] - The current phase of AI development involves three stages: initial training with vast data, advanced training through reinforcement learning, and high-level training in real-world scenarios [4] Manufacturing Industry Transformation - The manufacturing industry has evolved from manual production to intelligent manufacturing, with significant shifts occurring post-industrial revolutions, leading to increased automation and precision [5] - The article outlines four major historical shifts in global manufacturing, highlighting the need for industry transformation and the role of AI in driving this change [6] Development Recommendations - The integration of AI in manufacturing is crucial for achieving high-quality development, necessitating technological innovation and overcoming existing technical bottlenecks [7] - Key technologies for AI agents include large language models, machine learning, and various supporting technologies such as computer vision and cloud computing [8] Infrastructure and Data Strategy - A collaborative layout of computing power and data is essential, focusing on optimizing the synergy between models, systems, and hardware to enhance AI applications in manufacturing [9] - The article advocates for the construction of a robust data foundation to support AI model training, emphasizing the transition from traditional data delivery to data-driven business actions [9] Ecosystem Development - A collaborative effort among government, industry, academia, and research is necessary to foster an AI-enabled manufacturing ecosystem, facilitating the rapid conversion of research into practical applications [10] - The establishment of AI future manufacturing demonstration zones aims to integrate national strategic needs with regional advantages, enhancing competitiveness in the global market [10] Implementation of AI in Manufacturing - The focus on creating benchmark cases in key areas such as smart factories and supply chains is highlighted, with examples of using AI for real-time monitoring and optimization of production processes [11] - Future trends indicate that AI will increasingly penetrate core manufacturing processes, leading to a shift from passive responses to proactive optimization in production models [12]

Core Insights - The industrial intelligent agent is a fusion of large models, industrial mechanisms, and machine learning, generating significant economic value and driving innovation in industrial applications [1][2] - The global industrial intelligence market is expected to exceed 3.5 trillion yuan this year, with China accounting for over 40% of the market share, indicating a rapid acceleration towards the era of industrial intelligent agents [1] Group 1: Definition and Functionality - Industrial intelligent agents are designed specifically for industrial production, possessing autonomous perception, cognition, decision-making, and learning capabilities, distinguishing them from general intelligent agents [2] - These agents can understand high-level and natural language commands, transforming human-machine interaction by allowing direct command execution without manual software operation [2][4] - The integration of multi-modal perception, large model task planning, and refined motion control enhances the autonomous operational capabilities of robots in complex industrial environments [3] Group 2: Applications and Benefits - Industrial intelligent agents are shifting R&D from experience-based trial and error to an intelligent-driven paradigm, significantly reducing R&D cycles and enhancing design combinations [3] - In manufacturing, these agents facilitate the transition from automation to autonomy, optimizing production scheduling, equipment maintenance, and cross-system collaboration [3][5] - The implementation of intelligent agents has led to a 60% reduction in process preparation time and a 20% increase in order fulfillment rates, showcasing their efficiency-enhancing capabilities [6] Group 3: Challenges and Future Directions - The deployment of industrial intelligent agents faces challenges such as technology maturity, data isolation, and the complexity of industrial environments, which affect adaptability and reliability [7] - Safety concerns are paramount, as intelligent agents operate through autonomous code generation, exposing them to potential security threats like API vulnerabilities and code supply chain issues [7] - Strengthening infrastructure, establishing standard systems, and creating experimental ecosystems are essential for the effective deployment and integration of industrial intelligent agents [8]

Core Insights - The industrial intelligent agent is a new product resulting from the integration of large models, industrial mechanisms, and machine learning, attracting significant attention for its economic value [1] - The global industrial intelligence market is expected to exceed 3.5 trillion yuan this year, with China's market share surpassing 40%, indicating the rapid arrival of the industrial intelligent agent era [1] Group 1: Definition and Characteristics - The industrial intelligent agent is designed specifically for industrial production, possessing autonomous perception, cognition, decision-making, and learning capabilities, distinguishing it from general intelligent agents [2] - It enables a shift from preset programming and mechanical responses to autonomous decision-making and dynamic adaptability, enhancing human-machine interaction [2] - The industrial intelligent agent can decompose tasks, call tools, and collaborate with multiple agents, marking a significant difference from traditional automation systems [2] Group 2: Applications and Benefits - Industrial intelligent agents are transforming R&D from an experience-based trial-and-error model to an intelligent-driven paradigm, significantly shortening R&D cycles and enhancing design combinations [3] - In manufacturing, they upgrade processes from automation to autonomy, optimizing production scheduling, equipment maintenance, and cross-system collaboration [3] - The integration of multi-modal perception and task planning technologies enhances robots' autonomous operational capabilities in complex industrial environments [3] Group 3: Operational Efficiency - Industrial intelligent agents play a crucial role in supply chain optimization and internal management, enhancing operational efficiency through intelligent reasoning and prediction [5] - They can autonomously handle order processing, inventory alerts, and sales analysis, thereby increasing supply chain resilience [5] - The implementation of intelligent agents in production processes has led to a 60% reduction in preparation time and a 20% increase in order fulfillment rates [6] Group 4: Challenges and Future Directions - The deployment of industrial intelligent agents faces challenges such as technology maturity, data isolation, and safety concerns, which need to be addressed for effective implementation [7] - Infrastructure development is critical for supporting the construction of autonomous and compatible industrial AI platforms, overcoming technical bottlenecks [8] - There is a need for a standardized system and evaluation mechanism to guide enterprises in expanding applications and fostering industry-level collaborative innovation [8]