在全国迎峰度夏能源保供的关键时段，由南京鼓楼企业、朗坤智慧科技股份有限公司承建的"基于云边协同的火电安全生产管控数字化平台"，正保障国电 电力（600795）发展股份有限公司稳定高效运转。 国电电力基于云边协同的火电安全生产管控数字化平台"大屏 云边协同筑防线智能预警护安全 "1号机1B引风机失速预警，请及时确认处理！"国电电力上海庙电厂集控室内，系统发出一条清晰语音播报。值班长立即查看平台推送的故障诊断报告， 按照系统所生成的处置建议迅速操作，成功避免了一起非计划降负荷事件。 朗坤智慧项目负责人介绍说，该AI平台开创性地构建了"集团统筹、云边协同"的智慧管控全新模式。云端"大脑"凭借强大的算力，对全局进行智能优化并 作出科学决策；边缘"末梢"确保实现毫秒级的实时响应。二者协同运作，如同精密高效的"导弹防御系统"，实现风险预感知与预干预，防患于未然。 值得一提的是，该平台整体技术已被鉴定为"国际领先"水平，在实际应用中，不仅能有效延长设备使用寿命、提升设备运行效能，还能显著降低能源消 耗，助推"双碳"战略目标的实现。 国电电力生产技术部主任温长宏表示，平台打破了以往电厂管理"条块分割"的组织壁垒，率先实现了行 ...

Core Viewpoint - The article discusses the transformation of Guodian Power's production management through the implementation of a cloud-edge collaborative digital platform, enhancing safety and operational efficiency in thermal power generation [1][2][3]. Group 1: Cloud-Edge Collaboration - Guodian Power has developed a digital platform that integrates real-time data from 12 domestic and international power plants, allowing for centralized monitoring and management [2]. - The new management model retains the stability of traditional thermal power systems while introducing flexibility to adapt to the new energy system [2]. - This collaborative approach combines cloud computing for global optimization with edge computing for real-time responses, meeting the dual demands for safety and speed in the energy sector [2][3]. Group 2: Digital Transformation - In August 2023, Guodian Power prioritized digital transformation as its top project, forming a specialized team to create a comprehensive safety management system for thermal power [3]. - The platform features a multi-layered structure with one platform, two levels of control, five responsibility tiers, eight applications, and six warning centers, achieving full-chain digital penetration from headquarters to power plants [3][4]. - The system has integrated over 50,000 intelligent warning models and accumulated 550,000 warning cases, enhancing its predictive capabilities [3][4]. Group 3: Empowerment and Efficiency - The platform not only provides timely alerts but also offers precise handling suggestions by referencing similar equipment failure cases across the company [4]. - This unified management model promotes knowledge sharing and best practices among the 12 regional power plants, transitioning decision-making from experience-driven to data-driven [4][5]. - The platform aims to achieve a "five increases and one decrease" goal, enhancing equipment reliability, operational levels, safety management, technical control, and production cost management while reducing labor intensity [4]. Group 4: AI Integration - The integration of large and small models is a key technological breakthrough, allowing for intelligent decision-making and precise execution [5][6]. - The platform has issued over 550,000 warning messages since its launch, with an accuracy rate exceeding 90%, leading to a reduction in coal consumption and non-stop incidents [7][8]. - The collaboration between large and small models enhances the system's intelligence, enabling proactive maintenance and operational efficiency [6][7]. Group 5: Talent Development and Ecosystem - Guodian Power emphasizes the importance of building a skilled workforce that understands both business and technology to sustain its digital transformation [8][9]. - Regular model competitions and training sessions are held to enhance AI literacy among employees, fostering a culture of innovation [8][9]. - The company aims to evolve its platform towards a fully integrated cloud-edge-end architecture, facilitating rapid decision-making and reducing manual labor [9][10]. Group 6: Industry Impact - Guodian Power's cloud-edge collaborative model is positioned as a replicable solution for digital transformation in the process industry, potentially influencing broader sectors [9][10]. - The company is leading the way in integrating AI with energy production, setting a precedent for new industrial development paths [10].

Group 1 - The ninth Micro Semiconductor Conference was held in Shanghai, focusing on the impact of artificial intelligence on the semiconductor industry and its transformation potential [1] - The conference featured over thirty top analysts and industry experts discussing geopolitical factors, tariff barriers, supply chain restructuring, and technological breakthroughs [1] - A special session titled "Towards 2030 - AI-Driven Everything" highlighted how AI is reshaping key technology links and emerging applications in the semiconductor sector [1] Group 2 - The cloud segment of the large model ecosystem has developed well and is showing a trend towards edge computing [1] - Neil Shah from Counterpoint Research stated that generative AI is reconstructing the semiconductor value chain through "cloud-edge collaboration," with edge devices expected to drive large-scale deployment in AI smartphones, PCs, and automotive sectors [1] - Mike Demler emphasized that advancements in algorithms, processors, and software have made devices capable of running machine learning and AI applications widespread across the computing field [1] Group 3 - Mike Demler noted that the concept of "edge" is becoming obsolete, as AI computing knows no boundaries, with AI models migrating from cloud data centers to low-power MPUs and MCUs [2] - Karl Weaver from Newport Technologies highlighted that the Asia-Pacific region is becoming a core battleground for AI chip supply chain restructuring, facing challenges such as supply chain resilience and technological bottlenecks [2] - Peter Lendermann from D-SIMLAB Technologies emphasized the role of AI and digital twins in optimizing production processes within smart factories [2] Group 4 - Jens Hsu from Semi Vision pointed out that AI is driving significant transformations across various industries, including autonomous driving and smart cities, redefining automation in manufacturing, agriculture, healthcare, and consumer sectors [2] - The integration of AI is expected to advance high-performance computing, I/O bandwidth, advanced packaging, substrate, and sensor development in the semiconductor field [2] Group 5 - Pankaj Kedia from 2468Ventures stated that AI is changing industries by helping entrepreneurs incubate rapidly growing startups and driving innovation within established companies [3] - Ian Cutress from More Than Moore analyzed that the fusion of AI computing demands with optical chip technology represents a paradigm shift, forming a new "photonic-electronic heterogeneous" architecture [3] - The integration of optical chips with AI is seen as a key variable in breaking through computing bottlenecks and reshaping the industry landscape [3]

Core Insights - The emergence of AI agents this year has created commercial opportunities for large model vendors and chip companies, with a notable shift towards edge AI development [2][3] - AI models are becoming smarter and more compact, leading to increased demand for data centers and cloud computing, emphasizing the importance of capturing the expanding edge-cloud collaborative AI chip market [2][3] Edge AI Expansion - Three key elements are essential for building AI systems: creating a ubiquitous platform from cloud to edge, optimizing power consumption and performance per watt, and the importance of software alongside hardware [3] - The energy consumption of data centers has surged from megawatt (MW) to gigawatt (GW) levels, with over 50% of this consumption attributed to racks and semiconductor devices [3] AI Capabilities and Market Trends - The focus is shifting from model training to inference, which is crucial for realizing AI's commercial value, enabling smarter decision-making in devices like robots and smartphones [4][5] - The computational requirements for training large models are significantly higher than for inference, necessitating a substantial amount of inference operations to achieve commercial returns [5] Chip Design Challenges - The evolution of AI and the slowdown of Moore's Law are increasing the technical challenges and costs associated with chip design, making time-to-market critical [6] - Arm's strategy includes offering standardized products and platform solutions, such as the upcoming Armv9 flagship CPU, which aims to enhance performance and efficiency [6][7] Data Center Market Dynamics - Arm is actively competing in the data center market, traditionally dominated by x86 architecture, with predictions that nearly 50% of computing power for major cloud service providers will be based on Arm architecture by 2025 [8][9] - The transition from general computing to AI computing in data centers is underway, with significant efficiency improvements reported by cloud service providers using Arm-based processors [9]