华为智慧油气 解决方案 | • 勘探开发算力中心 02 | | --- | | • 油气勘探数据存储 05 | | • 智慧作业区 09 | | --- | | • 油气田一张网 21 | | • 华为星河 AI 高品质油气总部园区网络 25 | | • 数智化管网 30 | | --- | | • 管道一张网 36 | | • 智慧管网光通信 40 | 智慧勘探开发 / 智慧油气田 / 智慧管网 智慧勘探开发 01 华为智慧油气解决方案 勘探开发算力中心 背景与挑战 目前业界主流的勘探开发地学专业软件的计算硬件资源适配具有生态门槛，间接影响计算底座多元化发展与资 源共享。存算网资源未统一规划，资源云化进度慢、成本高。 无法跨集群完成大工区深度域地震数据处理、两宽一高海量数据和多工区大连片地震数据等作业任务，影响业 务推进效率。 多部门采购同类软件，单独部署，无法共享，软件利用率低，成本高。 解决方案简介 勘探开发算力中心解决方案基于华为云实现对算力资源的灵活调度，以及专业软件和数据的统一管理，支撑各 部门之间数据成果协同与共享，提升业务效率。 处理 解释 储层预测 地质建模 数值模拟 安全保障体系 运维保障 ...

本文引用的参考文献搜集于互联网，非原创，如有侵权请联系小编删除！ 在工业4.0和新基建的浪潮下，工业互联网和智能工厂的建设已经成为企业数智化转型的关键路径。本文将带您深入了解基于5G+边缘计算的智慧工厂解决 方案，通过"云边协同"架构和全场景覆盖能力，帮助企业解决传统工业模式中的痛点，实现降本增效、提升运营效率、保障数据安全，同时为政府和园区提 供数字化赋能支持。通过案例与数据的结合，我们将展示这一解决方案如何成为工业智能化转型的必经之路。 请勿将该文章用于任何商业用途，仅供学习参考，违者后果自负！更多参考公众号：无忧智库 在当今竞争激烈的工业环境中，您的企业是否也面临以下痛点？ 这些问题，是否正在阻碍您企业的进一步发展？别担心，基于5G+边缘计算的智慧工厂解决方案将为您打开一扇全新的大门！ 在5G、人工智能、物联网等技术的推动下，工业互联网已经成为企业实现数字化、网络化、智能化转型的核心载体。然而，传统的集中式云计算模式由于 高时延、高带宽成本和数据安全问题，难以完全满足工业场景中的多样化需求。因此，边缘计算应运而生。 边缘计算通过在靠近终端应用的位置建立站点，将云计算的能力延伸到边缘侧，有效解决了低时延、 ...

Core Viewpoint - The strategic partnership between Teslian and Unisoc Cloud aims to enhance AI computing power resources and create a more flexible and cost-effective AI computing solution, marking a significant milestone in the development of the domestic general AI computing industry [2][4]. Group 1: Strategic Cooperation - Teslian and Unisoc Cloud have signed a strategic cooperation agreement to leverage each other's strengths for resource sharing and capability synergy [2]. - The collaboration will integrate Teslian's hybrid intelligent computing cloud technology with Unisoc Cloud's cloud service capabilities, enabling users to quickly access cloud computing resources with a single operation [2][3]. Group 2: AI Chip Development - The partnership extends to the upstream of the industry chain, with Unisoc Group's comprehensive chip design capabilities laying a solid foundation for collaboration in customized AI chip development [2][3]. - The combination of Teslian's experience in spatial intelligence and Unisoc's full-stack cloud capabilities is expected to lead to the joint design and optimization of customized AI inference chips for specific scenarios [3]. Group 3: Cloud-Edge Collaboration - The demand for AI computing is shifting from "single-node clusters" to "distributed collaboration," necessitating a cloud-edge collaborative architecture that effectively addresses low resource utilization challenges [3]. - The collaboration aims to establish an open, efficient, and trustworthy domestic AI computing ecosystem, promoting the development direction of the industry [3][4]. Group 4: Impact on Digital Economy - The strategic cooperation is seen as a significant step towards accelerating the construction and implementation of the domestic general AI computing industry, contributing to the development of the digital economy in China [4].

Core Viewpoint - The implementation of a cloud-edge collaborative digital platform for thermal power safety production by Nanjing Gulou Enterprise and Langkun Smart Technology is enhancing the operational efficiency and stability of Guodian Power Development Co., Ltd during the critical summer energy supply period [1][2]. Group 1: Technology and Innovation - The AI platform has successfully prevented an unplanned load drop event by providing timely fault diagnosis and operational suggestions [2]. - The platform employs a "big model + small model" collaborative approach, enhancing operational safety and economic optimization by combining deep analysis with rapid diagnostics [4]. - The platform has achieved a significant reduction in coal consumption by 0.45 grams per kilowatt-hour and a 38% decrease in non-stop occurrences since its launch [4]. Group 2: Organizational Impact - The platform breaks down traditional management barriers in power plants, achieving a digital control goal of "five increases and one decrease" in reliability, operational levels, safety management, technical control, and production cost management while reducing labor intensity [4]. - Langkun Smart Technology is fostering a culture of innovation and AI capability enhancement through monthly competitions and specialized training for all employees [5]. Group 3: Future Development - The platform is evolving towards a "cloud-edge-end integrated" architecture to achieve deeper production automation, allowing for real-time command delivery to equipment [5]. - The success of Langkun Smart Technology exemplifies the ongoing optimization of the innovation ecosystem in the Gulou District, aiming to support technology companies in overcoming key technological challenges [5][6].

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]