强大的赋能能力背后，是海尔40余年的制造经验和卡奥斯工业互联网平台的数字化实践。通过AI+工业 互联网融合发展，平台将沉淀的海量数据、超200个专家算法和110+智能体开发工具，化作有实际产业 价值的工业大模型和场景智能体，打造出"一行业一模型"的精准赋能模式，将人工智能技术深入嵌入研 发设计、生产制造、运营管理等核心环节，推动了工业AI的规模化应用。 接连获得省政府认可，既是对卡奥斯COSMOPlat技术实力与创新能力的高度肯定，也彰显了卡奥斯 COSMOPlat以"AI+工业互联网"融合，推动山东产业升级和制造转型的实践价值，将为制造大省高质量 发展注入新动能。 深耕制造需求，推动AI规模化落地 作为制造大省，山东扛牢"走在前、挑大梁"使命担当，深入实施"人工智能+"行动，推进数字产业化"十 大工程"，产业数字化"八大行动"，人工智能核心产业营收超过1200亿元，占全国10%左右。 作为国家级双跨平台，以大规模个性化定制为核心，卡奥斯COSMOPlat打造出国内首个基于工业互联 网平台的垂域大模型——天智工业大模型，并在此基础上结合不同行业的工艺特性、生产痛点，形成具 备感知、决策与执行能力的垂直领域模型 ...

Core Insights - The integration of "Artificial Intelligence + Industrial Internet" is positioned as a core engine for advancing new industrialization, with a focus on transforming policy blueprints into effective industrial upgrades [1][2] - The Ministry of Industry and Information Technology (MIIT) has outlined clear development goals for the industrial internet sector, including the implementation of new industrial network transformations for over 50,000 enterprises by 2028 and the cultivation of more than 450 influential platforms [1][2] - The industrial internet's core industry scale is projected to exceed 1.6 trillion yuan by 2025, driving an increase in industrial added value of approximately 2.5 trillion yuan [1] Policy Developments - The MIIT has released the "Action Plan for High-Quality Development of Industrial Internet Platforms (2026-2028)", which includes specific targets such as exceeding 1.2 billion connected industrial devices and achieving a platform penetration rate of over 55% by 2028 [1][2] - The "Fusion Empowerment Action Plan" emphasizes upgrading foundational infrastructure and ensuring data model interoperability to enhance the integration of AI and industrial internet [2][3] Industry Challenges - The industrial sector has faced challenges due to unclear transformation paths, leading to fragmented progress and a lack of replicable models for different industries and enterprise sizes [3][4] - The "Action Plan" aims to provide a systematic transformation guide, addressing issues such as data silos and weak foundational support [3][4] Technological Innovations - The "Action Plan" prioritizes upgrading the intelligence level of industrial internet platforms and enhancing industrial computing power, recognizing the importance of a stable foundation for successful transformation [4] - The integration of AI technologies with industrial applications is crucial, with companies like Kaos COSMOPlat leading the way through innovative architectures and tailored solutions for various industries [8][9] Data Governance - Effective data governance is essential for unlocking the value of industrial data, with the "Action Plan" promoting the establishment of a trusted data circulation space and mechanisms for data asset registration and revenue distribution [6][12] - Kaos COSMOPlat has developed a comprehensive data governance and model application system, facilitating the flow and value realization of industrial data [12][13] Ecosystem Development - The "Action Plan" calls for the implementation of a "chain-network collaboration" project to cultivate high-level intelligent solution providers, with Kaos adopting an ecosystem model that promotes collaboration between large and small enterprises [15][20] - Kaos has established a global network of lighthouse factories and actively participates in international standard-setting, contributing to the development of a cohesive industrial ecosystem [15][20] Conclusion - The practices of Kaos COSMOPlat serve as a comprehensive framework for leading the transformation of the industrial sector, aligning with the policy directives outlined in the "Action Plan" and accelerating the integration of AI and industrial internet [16][19]

Core Insights - The year 2023 marks a period of rapid development and popularization of general large models, while 2024 and beyond will see the application of various specialized large and small models in vertical fields, becoming a major trend in the integration of artificial intelligence across industries [1] - Industrial sectors, characterized by complex production processes and clear mechanisms, are identified as key areas for the commercialization of vertical large models [1] Group 1: Industrial Applications - Industrial large models are being applied in energy conservation, manufacturing, and management, with expectations for accelerated commercialization as data accumulation enhances model capabilities [1] - The introduction of large models can significantly improve production accuracy, with average accuracy rates increasing from 70% to 90% in complex manufacturing processes [2] - Large models facilitate the integration of various energy mediums and types of water used in production, allowing for comprehensive decision-making in energy conservation efforts [2] Group 2: Challenges and Solutions - Challenges include the limited understanding of production processes by personnel and the lack of integration between independent systems, which hampers effective energy efficiency control [3] - The introduction of large models enables comprehensive energy and carbon management, creating a unified service model that enhances operational efficiency [4] - Data issues remain a significant barrier, with many facilities lacking real-time data collection capabilities, which is essential for deploying large models effectively [6] Group 3: Implementation Strategies - The fastest implementation projects are often retrofitting older facilities, particularly in the energy sector, which yields immediate economic benefits and encourages further digitalization efforts [6] - Service providers are also engaging in new facility construction, establishing digital twin systems to facilitate comprehensive large model integration across the entire production chain [7] - The combination of immediate results and flexible implementation strategies is accelerating the commercialization of industrial large models, providing better adaptability and customized solutions for various application scenarios [7]

Group 1 - The World Artificial Intelligence Conference 2025 highlighted the latest advancements in AI-enabled new industrialization, focusing on four major trends in manufacturing: data as a key production factor, human-machine collaboration becoming mainstream, intelligent manufacturing transitioning from "point" to "area," and enhanced capabilities for value-added services [1] - The integration of AI and industrial internet is showcased by Haier Group's subsidiary, showcasing innovations in industrial models, digital twins, and intelligent entities, with practical cases aiding companies like Yanchang Petroleum and New Jin Group in their transformation [1] - The launch of the "AI + Manufacturing" initiative aims to enhance the industrial internet sector, with significant growth expected in applications such as predictive maintenance and intelligent supply chain management [2] Group 2 - The industrial internet industry is projected to grow, particularly in sectors like petrochemicals and home appliances, driven by the application of large models and intelligent entities, leading to significant improvements in production efficiency and quality [2] - AI is expected to transform management models by breaking down information silos in enterprise systems, enabling deeper integration and profound changes in operations [2] - China Unicom is enhancing the integration of digital and physical realms, supporting the construction of smart factories across various industries, and accelerating the digital twin development of industrial equipment [2] Group 3 - China Telecom, in collaboration with the China Industrial Internet Research Institute, launched an "Industrial + AI" integrated service platform, demonstrating a large industrial weaving machine with an embedded intelligent entity that significantly improves delivery rates and production efficiency [3] - Financial institutions, including major banks, announced a credit facility of 400 billion yuan to support the development of "AI + Manufacturing," emphasizing the critical role of finance in this sector [3]