Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The rapid development of large model technology is driving the intelligent transformation across various industries, necessitating high-quality infrastructure to support model training, deployment, and application [6][12] - Current large model infrastructure faces challenges such as low availability and poor stability, which need to be addressed through multi-layer optimization across computing, networking, storage, software, and operations [6][27] - The report identifies five core capability areas for large model infrastructure: computing, storage, networking, development toolchain, and operations management [6][12] Summary by Sections Overview of Large Model Infrastructure - Large model infrastructure refers to the hardware and software resources that support the training, deployment, and application of large-scale AI models [13] - The infrastructure must possess high availability, high performance, scalability, and evaluability to meet the demands of large model applications [15][22] Current Status of Large Model Infrastructure - Technological advancements in AI storage and networking are improving infrastructure availability and communication efficiency [23][24] - Major tech companies like Amazon, Microsoft, and Google dominate the large model infrastructure ecosystem, integrating computing, platforms, models, and software [24] - Governments are increasing funding to promote the development of AI data center infrastructure [25][26] Challenges in Large Model Infrastructure - Low availability of large model infrastructure clusters and inefficient resource allocation are significant challenges [27][31] - Data processing inefficiencies and storage bottlenecks hinder the performance of large models [33][34] - Network communication issues arise as the scale of parallel computing increases, impacting training efficiency [37][39] Key Technologies for Large Model Infrastructure - Efficient computing resource management and scheduling technologies are essential for optimizing resource utilization [49][50] - High-performance storage technologies, such as KV-cache, enhance the efficiency of model inference [51][53] - Advanced networking technologies improve service stability and address communication bottlenecks in large model training [56][58]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Advanced computing has become a core driver for economic and social transformation, significantly impacting the development of the digital economy [7][14] - The demand for computing power is rapidly increasing, particularly driven by the deep development and application of artificial intelligence large models [7][15] - China's computing power development level has steadily improved, with a total computing power scale reaching 230 EFlops in 2023, ranking second globally [7][41] Summary by Sections Advanced Computing Development - Advanced computing encompasses three main areas: computing power, algorithms, and data, and includes various computing methods such as cloud, edge, and terminal computing [7] - In 2023, China's computing power scale reached 230 EFlops, with general data centers and intelligent computing centers rapidly deployed [7][41] - The overall computing power in China reached 435 EFlops, accounting for 31% of the global total, with a growth rate of 44% [7][41] Technological Innovations - Significant breakthroughs in computing technologies have emerged, including advancements in algorithms, computing chips, and software [8][49] - The number of computer-related patent applications has exceeded 30,000 for four consecutive years, indicating a robust innovation environment [8][49] - The integration of advanced computing technologies is driving the development of a complete industrial ecosystem in China [9][47] Industry Empowerment and Environment - The internet remains the largest sector for computing power demand in China, accounting for 38.6% of general computing power and 52% of intelligent computing power [9][55] - The development environment for computing power is continuously improving, with significant advancements in network infrastructure and data resource sharing [9][50] - The average annual growth rate of computing power in China over the past eight years has been 46%, outpacing the global average [9][60] Global Computing Power Trends - The global computing power scale reached 1,397 EFlops in 2023, with a growth rate of 54% [23][24] - Intelligent computing power accounted for 63% of the total global computing power, reflecting a significant increase from the previous year [24] - The global market for AI servers reached $47 billion in 2023, with a year-on-year growth of 157% [29] Competitive Landscape - The competition in computing power is intensifying, with major countries accelerating their strategic layouts in advanced computing technologies [32][34] - The United States and China remain the leaders in global computing power, with the U.S. holding a 41% share and China 31% [37] - Countries are increasingly recognizing the strategic importance of computing power in economic development and national security [38][34]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report emphasizes the growing importance of connected vehicle data as a new production factor that drives innovation across the automotive, information communication, and transportation industries, facilitating the construction of a new digital economy value chain [8][21][22] - It highlights the need for comprehensive data policies and infrastructure to unlock the value of connected vehicle data, which is crucial for the intelligent transformation of the automotive industry [8][21][29] Summary by Sections 1. Overview of Connected Vehicle Data - Connected vehicle data is categorized into four main sources: vehicle-end, road-end, cloud-end, and network-end, each contributing to the overall value creation in the industry [12][16][18] - The report outlines the characteristics of connected vehicle data, emphasizing its richness and multi-dimensional value, which can be leveraged across various sectors [17][19][20] 2. Vehicle-End Data Empowering Automotive R&D - The integration of data collection devices in smart connected vehicles is increasing, with 79% of new models equipped with external cameras, enhancing data flow for R&D and manufacturing processes [36] - Connected vehicle data is being utilized to optimize vehicle performance, enhance user experience, and improve production quality through real-time monitoring and predictive analytics [39][41][43] 3. Road-End Data Enhancing Traffic Management - Roadside infrastructure data is being used to improve traffic safety and efficiency, with a focus on real-time data collection and processing capabilities [22][24][26] - The report discusses the potential of road-end data to support vehicle upgrades and enhance user experience through better traffic management solutions [15][26] 4. Cloud-End Data Supporting Mobility and Logistics - Cloud platforms are central to data aggregation, providing services that enhance smart mobility and logistics efficiency [28][30] - The report identifies the need for improved data quality and value release capabilities in cloud-end data to support advanced driving functions [28][33] 5. Network-End Data Improving Connectivity and User Services - Communication network data significantly enhances connectivity services for connected vehicles, with a focus on improving user service capabilities [35][36] - The report highlights the importance of deepening the exploration of network data value to support various applications in the connected vehicle ecosystem [35][37] 6. Recommendations for Future Development - The report suggests strengthening digital infrastructure, promoting data application expansion, and enhancing cross-domain data interaction to maximize the value of connected vehicle data [10][29][30] - It emphasizes the need for collaborative development environments to foster a diverse industrial ecosystem around connected vehicle data [10][29][30]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Open source has become a global trend for enterprises, leading to the establishment of Open Source Program Offices (OSPOs) to manage and coordinate open source activities effectively [6] - The report outlines the development history of OSPOs globally and summarizes the current mainstream construction paths for OSPOs [6][10] - The report highlights the challenges and opportunities for OSPO development in China, emphasizing the need for improved management and standardization in open source practices [6][10] Summary by Sections 1. Overview of OSPO Development - OSPOs have evolved over two decades, with the first established by Sun Microsystems in 1999, marking the beginning of dedicated open source management within enterprises [10] - Major companies like Google, Facebook, and Microsoft have since established their OSPOs, enhancing their open source project management and strategic integration [10][12][13] - The establishment of OSPOs is positively correlated with company size, with larger enterprises more likely to have dedicated open source offices [17][18] 2. OSPO Construction Paths - The report identifies five main construction paths for OSPOs: top-down planning, demand-driven, technology-driven, virtual, and temporary pop-up models [24][25] - Each path is tailored to different organizational needs and scales, with top-down planning being suitable for large enterprises with complex structures [27][29] 3. Case Analysis of OSPO Construction in China - The construction of OSPOs in China is rapidly expanding across various industries, primarily in the information technology sector, which accounts for over 76% of OSPOs [48][49] - Most OSPOs in China are lightweight and often consist of virtual teams, focusing on internal governance and external community engagement [50][54] 4. Challenges and Future Outlook for OSPO Construction - The report discusses the challenges faced by OSPOs, including the need for a consensus on their core value and the establishment of quantifiable evaluation metrics [64] - It emphasizes the importance of cultivating open source talent and integrating open source governance into overall corporate strategy [64]

Group 1 - The report emphasizes the importance of county-level industrial economy as a foundation for national strength, highlighting the government's focus on promoting industrial development in counties [5][6][8] - The overall economic capacity of counties has steadily increased, with the GDP of counties rising from 27.8 trillion yuan in 2012 to 46.7 trillion yuan in 2022, showcasing the significant role of industrial economy [14][16] - The report identifies four major characteristics of county-level economic development, including the solid support for growth, the rebound in both eastern and western regions, the strengthening of leadership in Jiangsu and Zhejiang, and the rapid increase in innovation-driven momentum [7][9][10] Group 2 - The evaluation of industrial competitiveness in counties is based on six dimensions: comprehensive quality and efficiency, innovation, coordination, green development, openness, and sharing, aiming to enhance high-quality industrial development [22][24] - The report presents the 2024 list of China's top 100 industrial counties, which collectively contribute 10.2% of the national GDP and 14.7% of industrial added value, indicating their critical role in the national economy [32][34] - The analysis reveals that the number of industrially competitive counties in the eastern region has increased, while the western region has also seen slight growth, maintaining a pattern of more counties in the east than in the west [35][36][38] Group 3 - The report highlights the digital transformation of manufacturing in counties, identifying it as a key area for developing new productive forces and promoting new industrialization [8][10][19] - It discusses the strategic significance of digital transformation in manufacturing, emphasizing the need for effective investment to expand domestic demand [29][30] - The report provides insights into the current state of digital transformation in county-level manufacturing, including the emergence of various models tailored to local conditions [24][29][30]

Investment Rating - The report does not explicitly provide an investment rating for the manufacturing industry or specific companies within it [6]. Core Insights - High-quality development in the manufacturing sector is emphasized as a priority for China's economic modernization, with listed manufacturing companies reflecting the overall operational status of the industry [5][14]. - The report identifies a significant increase in the number of listed manufacturing companies, particularly in hard technology sectors, indicating a robust growth trajectory [15][18]. - Despite stable operational performance, profit margins are under pressure, with a notable decline in total profits for listed manufacturing companies [22][23]. - The report highlights the importance of innovation and R&D investment, with a substantial increase in R&D spending among leading companies [28][48]. - Regional distribution shows that eastern provinces dominate the list of top-performing manufacturing companies, with Guangdong, Shanghai, Beijing, and Jiangsu leading [6][34]. Summary by Sections Section 1: Listed Companies as a Microeconomic Foundation for High-Quality Development - The number of A-share manufacturing companies reached 3,578 by the end of 2023, accounting for 67.6% of all listed companies, with a notable increase in hard technology firms [15][18]. - Total revenue for A-share manufacturing companies was 28.1 trillion yuan, reflecting a 3.3% year-on-year growth, outperforming the overall A-share market [20][21]. - Profit margins faced challenges, with total profits declining by 10.9% to 1.8 trillion yuan, indicating a trend of increasing revenue without corresponding profit growth [22][23]. Section 2: Top 100 Manufacturing Companies Leading High-Quality Development - The top 100 manufacturing companies are evaluated based on innovation, competitiveness, influence, and contribution, with a focus on fostering world-class enterprises [6][30]. - The eastern region of China shows a clear advantage, with 70 of the top 100 companies located there, particularly in Guangdong and Shanghai [34][37]. - The electronic information sector has the highest representation among the top companies, followed by electrical machinery and specialized equipment [44]. Section 3: Challenges in High-Quality Development - There is a noted slowdown in the growth of companies reaching international standards, particularly in high-tech sectors, indicating a gap in competitiveness [59]. - Profitability and operational efficiency remain areas of concern, with many companies struggling to maintain profit margins comparable to global leaders [62][63]. - R&D investment, while increasing, still lags behind international averages, highlighting a need for greater innovation and technological advancement [64]. Section 4: Recommendations for Promoting High-Quality Development - The report suggests enhancing technological innovation capabilities, improving product quality, and strengthening international competitiveness as key areas for development [7][8].

云原生应用 保护平台 建设指南 （2024年12月） 主编单位 青藤云安全 中国信通院云大所 浙江大学 通过本报告的深入分析和实践指导，企业能够构建起一个强大的云 原生安全体系，有效应对云环境下的复杂安全挑战，确保业务的连续性 和数据的安全性。希望本报告能为企业的云原生安全建设提供宝贵的参 考和指导。 编写说明 2024 年是网络强国战略提出 10 周年，也是完成"十四五"规划 目标任务的关键年。在此背景下，光明网网络安全频道携手中国信息 通信研究院云计算与大数据研究所、《信息安全研究》，在浙江大学网 络空间安全学院、《中国金融电脑》、青藤云安全等单位支持下，联合 推出《安全洞察 · 大咖说》云原生安全专题访谈活动，邀请来自政府、 金融、通信、交通、能源、制造、互联网等行业相关负责人，分享数 字化转型持续深化路径、新技术应用安全风险防范策略等内容；同时， 聚 焦 前 沿 趋 势 及 行 业 实 践 调 研，撰 写 发 布《云 原 生 应 用 保 护 平 台 （CNAPP）建设指南（2024）》，旨在积极发挥行业示范引领作用，降 低重复试错成本，为各行业用户提升安全防护策略提供借鉴参考。 参编单位： 青藤云安全 ...

1 中国信息通信研究院产业与规划研究所 中国信息通信研究院广州智慧城市研究院 2024年12月 百舸争流 千帆竞发 城市全域数字化转型行业洞察报告 (2024 年) 版权声明 本报告版权属于中国信息通信研究院，并受法律保护。 转载、摘编或利用其它方式使用本报告文字或者观点的， 应注明"来源：中国信息通信研究院"。违反上述声明者，本 院将追究其相关法律责任。 前 言 当前，数字技术已成为全球研发投入最集中、创新最活跃、应 用最广泛、辐射带动作用最大的技术领域，数据正在成为最具战略 价值潜力的新型生产要素，二者融合蝶变持续催化数字新质生产力， 引发全球科技产业、经济形态、社会结构深刻变革。城市作为人类 历史文明进程中的核心组成部分，承载推动科技创新突破、经济结 构转型与社会形态演进的重大战略使命。站在新的历史起点，把信 息技术创新的强大动力势能与城镇化发展的广阔空间有机结合起来， 有助于加快壮大数字经济为代表的新质生产力，有助于全面构建更 普惠宜居的数字社会，有助于系统推进数字中国高水平建设。 城市全域数字化转型是落实习总书记"五位一体"总体布局思想 的数字化综合工程，是促进数字中国、数字经济、数字社会统筹发 展 ...

预见未来 中国元医院建设发展 调研报告 2024 ng 上海市数字医学创新中心 CAICT 中国信通院 Co Shanghai Digital Medicine Innovation Center 瑞盒醫院 版权声明 本调研报告的版权属于上海交通大学医学院附属瑞金 医院、上海市数字医学创新中心、中国信息通信研究院，并 受法律保护。转载、摘编或利用其它方式使用本调研报告文 字或者观点的，应注明"来源:上海交通大学医学院附属瑞 金医院、上海市数字医学创新中心、中国信息通信研究院"。 违反上述声明者，将追究其相关法律责任。 2 前 言 随着下一代网络和算力基础设施的不断发展，以及大数 据、人工智能、虚拟现实等信息技术的日臻成熟，虚拟世界 与现实世界在身份、社交、工作、生活等领域的融合日益密 切，允许人们在其中去创造、扩充和交流；把跨时空、跨地 域、跨载体、跨媒介融为一体，从而构成一个基于现实但又 超越现实的虚实融合社会新形态。 "医院"是患者接受治疗、照护、生活的重要场所。在 虚实融合相关技术赋能下，医院形态突破线下实体物理空间 不断向线上虚拟空间扩展，从二维平面向三维空间延伸，这 种趋势也推动着临床医疗、运营管理 ...

人形机器人产业发展研究报告 (2024 年) 中国信息通信研究院泰尔系统实验室 2024年12月 版权声明 本报告版权属于中国信息通信研究院，并受法律保护。 转载、摘编或利用其它方式使用本报告文字或者观点的，应 注明"来源：中国信息通信研究院"。违反上述声明者，本院 将追究其相关法律责任。 前 言 人形机器人作为未来产业的重要赛道，是科技自立自强的标志型 成果，是人工智能、机械工程、电子工程等领域融合创新的典范，也 是实现新质生产力的最佳手段之一。人形机器人凭借其类人的感知交 互能力、肢体结构和运动方式，能够快速融入为人类设计的各种环境， 未来有望在简单重复劳动和危险场景中替代人类，在复杂技能场景中 辅助人类，在商业和家庭场景中服务人类。可以预见，未来人形机器 人的广泛应用将深刻改变社会形态和人们的生产生活方式，已成为全 球科技领域的发展热点。业界普遍认为，人形机器人未来有望成为继 个人电脑、智能手机、新能源汽车后的新终端，形成新的万亿级市场。 本报告从人形机器人内涵出发，深入分析人形机器人核心技术及 重点产品的发展现状和演进路径、产业布局的重点方向、应用需求和 市场预期等。同时，聚焦生产制造、社会服务、特种 ...