2024 版 数字能源 2030 构建万物互联的智能世界 数字能源 2030 01 从巴黎协定到阿联酋 COP28 会议， 减碳成为全球确定性趋势 COP28 会议启动"结束化石燃料"的时代，超过 150 个国家提出 减碳承诺 十八世纪以后，煤炭、石油、电力的广泛使用， 先后推动了第一、第二次工业革命，使人类社 会从农耕文明迈向工业文明，能源为推动社会 进步、消除贫困、改善民生提供了源源不断的 动力，成为世界经济发展的最重要基石之一。 同时，人类对地球气候系统的影响显而易见， 近年来人为排放的温室气体达到历史最高水平。 根据联合国政府间气候变化专门委员会（IPCC） 的统计，人类活动引起的二氧化碳变化量每年 约为 237 亿吨（尤其是燃烧化石燃料，每年大 约排放 200 亿吨）。其结果就是现在大气中的 二氧化碳含量比过去 65 万年（平均水平）高了 27%。特别是工业革命时代开始大量燃烧煤炭， 二氧化碳水平开始急速上升，有可能引发气候 系统前所未有的变化，导致严重的生态和经济 失调。这已促使人们讨论如何减少化石燃料的 燃烧来降低温室气体的产生。 科学界和各国政府对气候变化问题正在形成更 加明确的共识，2015 ...

Key Points Industry/Company Involved - **Huawei Industry Chain**: The focus of the discussion revolves around Huawei's recent developments and the impact on its supply chain and ecosystem. Core Views and Arguments - **Significant Changes in Huawei's Supply Chain**: The return of HiSilicon's smaller division, HiSilicon Mini, to the market has driven related stock movements. This division produces storage process chips for the industry and external sales, while the larger division, HiSilicon, focuses on internal use. [doc id='1'] - **Huawei Cloud and Hardware Emphasis**: Huawei's full connection conference highlighted Huawei Cloud and its hardware, including AI computing Ascend and general computing Kunpeng. [doc id='1'] - **New Product Launches**: Huawei has launched several new products, including the Mate X5 smartphone and wearable watches. [doc id='1'] - **HarmonyOS Progress**: HarmonyOS has made significant progress, including public beta testing for all consumers, with initial compatible devices including the Mate60 and X5 series smartphones and the latest tablet computers. [doc id='1'] - **Upcoming Product Launches**: Huawei will launch several important new products in the coming months, including the Mate70 smartphone in November, which will pre-install HarmonyOS S and be its main selling point. [doc id='1'] - **Technology Stocks as a Key Investment Theme**: Technology stocks should be a key theme in the current market differentiation stage, focusing on companies with strong thematic attributes and clear industry trends. [doc id='1'] - **HarmonyOS PC Development Prospects**: HarmonyOS PC has a promising future, with two product lines: To B and To C. The To C product line is expected to be launched earlier than the To B product line. [doc id='1'] - **Investment Opportunities in HarmonyOS**: Investment opportunities related to HarmonyOS are concentrated in several areas and companies, including companies related to Huawei's intelligent vehicles, HarmonyOS, HarmonyOS PC, Huawei's computing power, and Huawei Cloud ecosystem. [doc id='1'] Other Important Content - **Investor Perspective on Technology Stock Investment Opportunities**: Investors should focus on companies involved in HarmonyOS and its ecosystem, HarmonyOS PC-related companies, companies involved in Huawei's computing power and cloud computing ecosystem, and companies performing well in the financial technology field. [doc id='5', '6', '7', '8', '10'] - **HiSilicon Semiconductor Development**: HiSilicon has undergone significant changes this year. The larger division, HiSilicon, produces Kirin chips for smartphones and PCs, while the smaller division, HiSilicon Mini, focuses on semiconductor mature processes and provides services to the industry. [doc id='9'] - **Focus Areas for Future Investors**: Investors should focus on companies related to HarmonyOS and its ecosystem, HarmonyOS PC-related companies, companies involved in Huawei's computing power and cloud computing ecosystem, and companies performing well in the financial technology field. [doc id='10']

那么我们看最近市场涨幅比较大嘛那么今天呢我也给大家汇报一下华为产业链相关的变化那为什么要关注华为产业链我始终在提呢其实八月中旬至今啊就牛股最多的板块一定是大家很清晰啊就华为相关的一些公司 那么今年以来其实跌幅最大的行业那也是我们计算机行业华为加计算机那就是华为相关的计算机操作系统现在关注度最高的就是鸿蒙那我先给大家说一下华为产业相关的变化然后再给大家聚焦鸿蒙相关的变化 那么整个是这样的就是我们之前八月中旬的时候其实华为就开始陆陆续续的大家关注到小海思要逐步回归了小海思它主要是做成熟制成的芯片它相比于大海思它更多是小海思服务于产业的它的芯片是对外卖的大海思更多是华为自用为主 对 那么八月中旬小海思带动了一波华为产业链相关的行情到九月份华为的全届大会召开这个主要是合作伙伴的一个大会并且今年的全届大会很明显他其实是围绕着华为云在办这个跟以前都非常不一样今年就是围绕华为云华为云的硬件华为云他采用的硬件我们的生腾和昆蓬生腾是AI的计算昆蓬是通用计算 那么软件主要是开源鸿蒙跟开源欧拉包括所请来的合作伙伴都是以华为云的合作伙伴为主那开了整个全联机大会这个全联机大会也使得整个市场其实对于华为的关注度也比较高一直延续到了我们9 ...

Core Insights - The report emphasizes the importance of a new generation underground network for coal mines, designed based on "SDN (Software Defined Network) + Slicing" architecture, which aims to enhance communication reliability, bandwidth, and operational efficiency in the context of increasing automation and intelligence in coal mining operations [5][27][36]. Group 1: Requirements for Communication Networks - The development of intelligent coal mining necessitates a robust communication network that can support high reliability, low latency, and simple maintenance, as well as high bandwidth to accommodate various applications such as remote control and safety monitoring [8][12]. - Intelligent coal mining applications include high-definition video monitoring, safety monitoring, wireless communication, and remote control, all of which require a network capable of handling large data volumes and ensuring real-time communication [9][10][11]. Group 2: Current Status and Challenges of Underground Networks - The existing underground networks consist of various systems, including wired communication networks, safety monitoring networks, and industrial Ethernet, which often leads to a complex and fragmented network environment [18][19][20]. - Challenges faced by current underground networks include insufficient bandwidth, network storms affecting communication reliability, and difficulties in network maintenance due to the complexity of multiple independent systems [24][25]. Group 3: New Generation Underground Network Solutions - The new generation underground network is defined as a comprehensive carrier network that integrates various intelligent applications, utilizing advanced technologies such as IPv6 and SDN to achieve unified data transmission and operational efficiency [26][27]. - The architecture of the new generation network is structured into three layers: the surface core, underground backbone, and underground access, ensuring effective data flow and management [27][28][29]. Group 4: Key Features and Technologies - The new generation underground network features include multi-service integration, ultra-wide coverage, intelligent operation and maintenance, dedicated network slices, and high reliability, which collectively enhance the operational capabilities of coal mining [31][32][33]. - Key technologies supporting the new network include network slicing, SRv6, clock synchronization, and Wi-Fi 6, which address the limitations of traditional networks and facilitate the deployment of intelligent applications [33][34].

Investment Rating - The report does not explicitly state an investment rating for the industry or company Core Insights - The emergence of large models has triggered an exponential growth in global computing power demand, with AI computing requirements increasing by 300,000 times from 2012 to 2023, and expected to grow another 500 times in the next decade [11][12] - Huawei Cloud's Ascend AI Cloud Service is positioned as the best cloud-based full-stack computing service for the era of large models, providing comprehensive support for model training, inference, and application development [17][18] - The AI industry is experiencing a paradigm shift, moving from specialized models to general-purpose models capable of handling diverse applications through extensive pre-training on massive datasets [8][12] Summary by Sections Section: Global Computing Demand - The demand for computing power has surged due to the rise of large models, with video generation models like Sora requiring significantly more computing resources compared to traditional models [11][12] - The report highlights that the scale of datasets will increase from a few terabytes to 100 terabytes, and the token length for models will expand from thousands to hundreds of thousands [11] Section: Full-Stack Computing Services - Huawei Cloud's Ascend AI Cloud Service integrates cluster computing, computing engines, and AI development frameworks to provide stable and reliable full-stack computing support for large model training and inference [12][18] - The service offers various computing usage, management, and deployment modes to cater to diverse business needs [19][21] Section: Business Innovation Focus - The report emphasizes that enterprises require full-stack computing services to focus on business innovation, leveraging accumulated training and operational experience to avoid redundant problem-solving [14][19] - The Ascend AI Cloud Service supports a wide range of AI frameworks and models, facilitating rapid application development and deployment [68][70] Section: Performance and Recovery - The service boasts rapid fault recovery capabilities, with industry practices showing that cluster faults can be detected in one minute, diagnosed in five minutes, and recovered in ten minutes [36][40] - The report indicates that the average interruption for large model training in the industry occurs every 2.8 days, with recovery times traditionally taking much longer [36][41] Section: Ecosystem and Community - The AI Gallery serves as a one-stop community service platform, fostering an open community for AI development and collaboration [69] - The D-Plan ecosystem partnership program aims to build a collaborative AI ecosystem, providing partners with comprehensive support for training, technology, marketing, and sales [70]

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The modernization of financial core systems is essential for banks to enhance operational efficiency and meet the evolving demands of digital finance, regulatory compliance, and customer service [5][10][15] - Financial institutions are increasingly focusing on integrating financial technology to drive innovation and improve service delivery, aligning with national strategic goals [5][10] - The shift from centralized to distributed core systems is becoming a consensus among financial institutions, driven by the need for resilience, reliability, and agility [28][30] Summary by Sections Chapter 1: Challenges and Trends in Modern Financial Core Systems - The banking sector is transitioning from an inward-focused improvement model to an outward-focused ecosystem integration model, emphasizing the release of data value to enhance financial services [10][11] - Key challenges include differentiated customer service, product innovation, regulatory compliance, stable operations, and cost structure optimization [5][10] - The concept of "new quality productivity" highlights the central role of financial technology in driving business innovation and service model transformation [5][10] Chapter 2: Goals and Key Designs for Modern Financial Core Systems - The target architecture for modern financial core systems integrates infrastructure modernization, technology architecture, data architecture, security, compliance, and innovation [15][16] - The business architecture includes defining core business goals, rules, processes, and stakeholder needs to enhance operational efficiency [16][18] - The technical architecture aims for resilience, trustworthiness, agility, and intelligence, utilizing cloud computing, distributed systems, and AI technologies [25][28] Chapter 3: Methodologies and Pathways for Core System Construction - The report outlines a six-dimensional new quality system for building modern financial core systems, including resilient architecture, full-stack trust, open integration, engineering practices, smooth migration, and agile intelligence [6][10] - A "four stages and ten steps" implementation methodology is proposed to guide the construction of modern financial core systems [6][10] Chapter 4: Solutions for Distributed New Core Systems - The report discusses collaborative solutions developed by Huawei and leading domestic software vendors to support the transition to distributed core systems [6][10] - Emphasis is placed on the need for a robust digital infrastructure to facilitate the integration of core business with modern distributed capabilities [6][10] Chapter 5: Practical Cases of Modern Financial Core System Construction - The report includes case studies from various financial institutions that illustrate the practical challenges and solutions encountered during the core system transformation process [10][30]

Industry Investment Rating - The report highlights the transformative potential of AI, particularly in the context of AI-driven data centers (AI DC), which are becoming the core infrastructure for enterprises aiming to achieve intelligent transformation [5][6][9] Core Viewpoints - AI is an irreversible trend, not a passing trend, and it will reshape every industry and organization [4][22][23] - The future of data centers is defined by AI, with AI DC being a comprehensive reconstruction of traditional data centers, shifting from cost centers to innovation centers [5][6][46] - AI DC will play a critical role in supporting AI model training, inference, and application, becoming the cornerstone of enterprise intelligent transformation [5][6][9] Chapter Summaries Chapter 1: AI World Vision and Macro Drivers - AI is a major direction that cannot be stopped, with generative AI and large models driving a new industrial revolution [16][18] - The global AI market is rapidly expanding, with significant investments in generative AI, despite a slight decline in overall AI investment [16] - AI is expected to trigger a once-in-a-century transformation, reshaping industries and driving economic growth [18][20] Chapter 2: All in AI - Generative Business Systems - Enterprises face both uncertainties and certainties in adopting AI, with over 70% of leaders expecting AI to significantly impact their business within five years [29] - The key to successful AI adoption lies in building an enterprise-level AI architecture that can handle the instability of large models and the "impossible triangle" of generalization, specialization, and economy [31][33] - Enterprises should focus on application scenarios, data, models, and computing power to achieve value creation and business transformation [34][36] Chapter 3: Development and Changes in Data Centers - Data centers are evolving into AI DC, which are designed to support AI model training, inference, and application, with a focus on high-performance computing and energy efficiency [49][51] - AI DC differ from traditional data centers in terms of business load, computing power type, and cooling methods, with a shift towards xPU-centric architectures and liquid cooling [51][52] - AI DC are categorized into three types: ultra-large, large, and small, each serving different needs and facing unique challenges [56][57][58] Chapter 4: Typical AI DC Planning and Construction - Ultra-large AI DC are primarily used for foundational model pre-training, facing challenges such as power supply, reliability, and effective computing power [59][60] - Large AI DC are used by industry leaders for secondary training and central inference, with a focus on optimizing inference performance and resource utilization [60] - Small AI DC are designed for lightweight inference and AI applications, requiring flexible deployment, easy maintenance, and security [61][62] Chapter 5: AI DC Construction and Development Initiatives - The report proposes four key initiatives for AI DC development: moderate超前 construction, intensive and green development, open collaboration, and building three foundational bases to accelerate AI adoption [15] - AI DC will be redefined by AI, providing diverse computing power and enabling the innovation of AI-native applications [47][65]

Investment Rating - The report does not explicitly state an investment rating for the ICT services and software industry Core Insights - Digitalization, intelligence, and low-carbon development are identified as certain trends, with artificial intelligence (AI) being the biggest opportunity in the next decade, projected to grow AI computing power by 500 times by 2030 compared to 2020 [2][10] - The integration of AI into various sectors such as digital government, intelligent finance, and smart manufacturing is expected to accelerate the digital transformation of industries [2] - The report emphasizes the need for the ICT industry to prepare for challenges and demands in six key areas, including the integration of computing and networking, the development of new professional service capabilities based on large models, and ensuring a reliable service level agreement (SLA) experience [2][10] Summary by Sections Section 1: Accelerating Green Network Development - The report highlights the necessity for a green transformation of ICT infrastructure to support sustainable business development, addressing issues like outdated equipment and high energy consumption [11][12] - A three-tier management framework for green governance, planning, and execution is proposed to guide the green transformation strategy [12][15] Section 2: Data Center Green Low-Carbon Leadership - The demand for AI model training computing power is expected to grow exponentially, leading to increased energy consumption in data centers [19][21] - New energy efficiency metrics are being introduced, shifting from PUE to more comprehensive sustainability indicators [19][21] Section 3: Intelligent Data Management - Data is recognized as a critical asset for AI model development, necessitating robust data storage and management systems to handle the increasing complexity and volume of data [27][28] - The report discusses the importance of ensuring data security and efficient data flow to support AI applications [27][30] Section 4: Transitioning to Business-Centric Operations - The report outlines a shift from network-centric to business-centric operational models, driven by the need for automation and intelligent operations [34][40] - New operational models are expected to enhance customer experience and operational efficiency, with a focus on agile business recovery capabilities [42][46] Section 5: 5G-A and Business Scenario Integration - The integration of 5G-A with business scenarios is anticipated to create new value and enhance customer experience through tailored service offerings [46][47] - The report notes that operators are moving towards experience-driven business models, emphasizing the need for network optimization to meet diverse customer demands [47]

Investment Rating - The report does not explicitly state an investment rating for the industry or company Core Insights - The HiSec Endpoint intelligent terminal security system addresses the increasing network security risks faced by enterprises due to digital transformation and the evolving nature of cyber threats [9][14] - The system integrates big data security analysis capabilities to form a multi-layered adaptive defense loop, enhancing threat perception, detection, and response [14][29] - The HiSec Endpoint Agent is designed for lightweight deployment and efficient data collection, ensuring minimal resource consumption while providing comprehensive threat awareness [18][19] Summary by Sections Introduction - The report discusses the challenges in network security construction, emphasizing the vulnerabilities of terminal security systems in the face of advanced threats [9][11][12] - It introduces the HiSec Endpoint intelligent terminal security system as a solution to these challenges, highlighting its innovative architecture and capabilities [14] Advantages and Value - The HiSec Endpoint system offers comprehensive threat perception, precise threat detection, and superior threat response capabilities [17] - The system's lightweight agent collects data efficiently, reducing noise and ensuring data integrity for effective threat analysis [18][19] - It features advanced detection engines, including a third-generation antivirus engine and a threat tracing graph engine, significantly improving the detection rate of unknown threats [26][27] Working Principles - The system employs full-stack data collection to monitor terminal activities in real-time, facilitating effective threat detection and response [30][31] - It utilizes a dynamic behavior analysis approach to identify malicious activities, enhancing the overall security posture against various attack vectors [32][33] - The HiSec Endpoint system incorporates advanced mechanisms for ransomware detection and response, ensuring comprehensive protection against such threats [45][56] Threat Detection and Response - The system's proactive measures include automated threat analysis and response, enabling quick containment of security incidents [29][45] - It features a lightweight backup and recovery mechanism to restore encrypted files to their pre-attack state, ensuring data integrity [56] - The HiSec Endpoint system also addresses cryptocurrency mining threats through specialized detection engines that monitor suspicious behaviors [57][62]

2024 版 通信网络 2030 构建万物互联的智能世界 目 录 06 型超势 04 未来网络场景 | --- | --- | |-----------------------------------------------------------------------------------------------------------------------------------------------------|-------| | | | | 2.1 下一代人机交互网络：以人为中心的超现实体验 | | | 2.1.1 XR：虚实的完美结合，自然的交互体验…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………… 08 | | | 2.1.2 裸眼 3D：逼真的影像再现，全新的视觉体验 ……………………………………………………………………………………………………………………………………… 09 | | | 2.1.3 数字触觉：多维的体感交互，可触摸的互联网 ……………… ...