原标题：资源行业智能化转型，AI助力核心生产系统重构 从内蒙古草原的露天煤矿到东海之滨的炼化基地，从西部高原的金属矿山到华东地区的钢铁高炉， 从中东中亚的石油井场到拉美地区的铁矿开采，人工智能的浪潮正在重塑资源行业的每一个角落。一年 来的客户交流和实地调研，我深刻体会到，这个支撑着各国国民经济命脉的领域，经历着一场静水潜流 的变革。 回首2024年，我认识到劳动资料、劳动对象、劳动者的"三个跨越"是驱动这场变革的核心动能。迈 入2025年，人工智能浪潮席卷千行万业，资源行业已站在了历史性的拐点，国家对安全保障的要求日益 严苛、全球减碳的倒计时愈发紧迫，高耗能行业的生存空间继续被压缩。 与金融、互联网等"先建后用"的数字化路径截然不同，资源行业正走出一条"以用促建"的新路。前 者的数字化基础设施历经长期迭代，信息系统本身即为核心业务，AI应用可快速嫁接其上，形成了'建 设驱动使用'的良性循环。而后者长期依赖机械自动化，常面临极端环境、网络覆盖薄弱、数据采集困 难等挑战，传统信息化建设模式成本高、周期长且投资回报存在不确定性。 如何破开困局、行稳致远？我认为根本路径在于，积极拥抱先进生产力，推动AI从辅助生产的配 ...

Group 1 - The core theme of the event is the digital transformation of regional banks, focusing on the upgrade of core systems and collaboration with technology partners like Huawei [1][3] - Regional banks are transitioning from centralized architectures to distributed cloud architectures, driven by the urgent need for digital transformation and the opportunity for strategic innovation through technology [3][4] - The modernization of core systems is crucial for enhancing customer service efficiency, risk management capabilities, and sustainable development potential in the banking sector [3] Group 2 - The event highlighted the importance of AI technology in innovating financial core business operations, moving from cloud migration to business innovation on the cloud [4] - Huawei's cloud solutions aim to provide intelligent resilience and modern application upgrades, addressing challenges in the banking sector such as low development efficiency and poor user experience [4][5] - Various regional banks shared their cloud migration practices, demonstrating the critical value of core system migration for business agility and the development of replicable transformation methodologies [5]

Core Insights - Nanjing Steel Co., Ltd. (Nanjing Steel) is leveraging advanced digital and intelligent technologies to transform its operations and enhance its competitiveness in the steel industry [1][2][4]. Group 1: Digital Transformation Initiatives - Nanjing Steel has initiated a comprehensive data governance program, achieving over 90% core data asset integration into a centralized data lake, and became one of the first listed companies in China to report digital assets exceeding 10 million yuan in 2024 [2]. - The company launched a three-year action plan called "Artificial Intelligence Hundred Scenes Thousand Models," collaborating with Huawei to build a vertical model base for the steel industry, facilitating real-time data flow and algorithm iteration [2][4]. Group 2: AI Applications in Production - Nanjing Steel has implemented an intelligent peak-valley optimization scheduling system, which improved energy utilization efficiency by 4.24 times, generating an additional 6.55 million kWh of electricity in 2024 [4]. - The company has enhanced the mechanical performance prediction of carbon-manganese low-temperature steel, increasing product performance qualification rates by over 1.5% through advanced data analysis and real-time monitoring [5]. - An AI-driven coal blending system has reduced the blending time from 1-2 days to 1-2 minutes, lowering the cost of coke by 5-10 yuan per ton [7]. Group 3: Quality Control and Process Optimization - Nanjing Steel has developed a new AI prediction engine for rolling force, enabling rapid modeling and high-precision predictions across various steel grades and conditions [7]. - The implementation of AI visual models for quality inspection has significantly improved defect detection capabilities, addressing challenges related to sample scarcity and variability [7][9]. Group 4: Future Directions and Collaborations - The collaboration with Huawei Cloud Stack is not only a technological partnership but also aims to establish a new paradigm for industrial intelligence, reflecting the broader transformation of the Chinese steel industry [9][10].

Core Insights - The summit focused on the theme "Huawei Cloud Stack, Understanding Government and Enterprise in the Intelligent Era," highlighting the importance of AI technology in driving digital transformation for government and enterprise clients [1] - Huawei Cloud aims to enhance its AI cloud service products and solutions, collaborating with clients to implement AI technology across various business scenarios [1][3] - The company recognizes the diverse needs of government and enterprise users, categorizing them into four distinct roles to better tailor its offerings [4] Group 1: AI and Digital Transformation - Government and enterprise clients are increasingly adopting AI technologies for applications such as smart customer service, process optimization, safety supervision, and digital marketing [1] - Huawei Cloud's mixed cloud solutions are designed to support the digital transformation needs of these clients, providing over 120 cloud services and more than 50 scenario-based solutions [3] - The company emphasizes the importance of leveraging vast amounts of industrial, financial, and public data in China to foster AI industry growth [3] Group 2: User-Centric Approach - Huawei Cloud Stack has developed a comprehensive approach to support different user roles, including data center engineers, data engineers, AI algorithm model application engineers, and application development engineers [4] - The company is set to launch a mixed cloud solution compatible with CloudMatrix 384 super nodes, enabling clients to have their own cloud super nodes locally [4] Group 3: Case Studies and Applications - Xiangtan Steel Group, in collaboration with Huawei, launched the world's first steel industry large model, establishing a unified AI training center to enhance operational efficiency [5] - Chengdu Urban Investment Digital Group is building a trusted data space to empower the city's digital transformation, focusing on data integration and new business models [6] Group 4: Future Directions and Innovations - Huawei Cloud Stack is enhancing its capabilities in large model mixed cloud solutions, addressing challenges in computing power management and AI toolchain development [7] - The release of the white paper on "Government and Enterprise AI Platform Architecture and Application Practices" aims to provide a reference framework for clients to plan their AI platform construction effectively [7]

Core Viewpoint - Huawei Cloud Stack aims to provide a hybrid cloud solution that better understands the needs of government and enterprise users in the era of intelligence, focusing on AI integration and data utilization [1][3]. Group 1: Huawei Cloud Stack Features - Huawei Cloud Stack will become the first hybrid cloud to adapt to CloudMatrix 384 super nodes, enabling enterprise customers to have their own cloud super nodes locally, enhancing AI computing power for intelligent transitions [3]. - Currently, Huawei Cloud Stack offers over 120 cloud services and more than 50 scenario-based solutions, maintaining the leading market share in the hybrid cloud sector across government, finance, and manufacturing for several consecutive years [3][4]. Group 2: User Segmentation and Solutions - Huawei Cloud Stack recognizes that government and enterprise users are not a monolithic group but can be categorized into four distinct roles: data center engineers, data engineers, AI algorithm model application engineers, and application development engineers [3][4]. - The platform supports users throughout the entire cloud lifecycle, from building to managing cloud resources, enabling efficient resource allocation, data governance, model training, and application development [4]. Group 3: Case Studies - In finance, Huawei Cloud Stack helped a state-owned bank establish a unified computing power platform, allowing data center engineers to deploy 106 DeepSeek R1 instances in just two days, improving efficiency by 70% compared to traditional bare-metal deployments [4][5]. - In manufacturing, Huawei Cloud collaborated with XCMG to create a robust big data platform, enhancing data analysis efficiency and enabling value extraction from operational data of construction machinery [4][5]. - In the steel industry, Xianggang utilized Huawei Cloud Stack to develop a one-stop AI development platform, achieving quality improvement and cost reduction through the deployment of a steel model across over 30 scenarios [5]. - In the energy sector, CNOOC implemented CodeArts to develop a digital platform, reducing development time by 30% and streamlining the deployment of intelligent oilfield management systems from one week to one day [5].