研究背景 自 2015年《中国制造2025》提出制造业数字化升级以来，以补短板、育龙头、促新技术与制造业融合创新为主调，国际层面和地方层面纷纷出台相关政策引导我 国制造业向高端制造业转型，并且，十三五规划强调制造业与新兴技术不断融合。在此背景下，中国制造业乃至整个工业领域的数字化转型征程从未停下， 数字化 转型的诸多供给方纷纷致力于在不同行业、不同场景探索提供服务，而且不断打磨自身产品和服务体系，既包含了丰富的软件产品，也涵盖了诸多硬件产品。而 工 业软件是智能制造的战略高地，没有工业软件的支撑，就不能完全实现工业制造的信息化、数字化和智能化。随着传统制造向智能制造不断迈进，工业软件的重要 性也不断凸显，作为自主可控的重要一环的国产工业软件也进入了发展的攻坚期。 近年来，我国工业软件市场持续保持高速发展的态势，根据工信部数据统计， 2021年，中国工业软件产品实现收入2414亿元，同比增加22.3%。虽然工业软件产品增势喜人，但仍与国外产品存在较大差距，存在"技术卡脖子""数据空心化""研 发难度大""品牌效应差"等多种问题，这也是掣肘我国智能制造 发展的主要原因之一。故分析国内工业软件的市场情况、重点产品及 ...

Core Viewpoint - The emergence of large models is transforming the landscape of China's cloud computing industry, with companies like Volcano Engine gaining significant traction in the AI application development platform market [1][2][15]. Group 1: Market Position and Performance - Volcano Engine ranked first in the "Challenger" quadrant of Gartner's Magic Quadrant for AI application development platforms, showcasing its strong capabilities in model services [2][6]. - As of mid-2025, Volcano Engine is projected to hold a 49.2% market share in China's public cloud large model service market, indicating its leading position [7]. - The company aims to achieve a revenue target of over 20 billion RMB this year, reflecting a growth rate exceeding 100% [16]. Group 2: Strategic Focus and Investments - Volcano Engine has aggressively invested in AI over the past three years, positioning itself to capitalize on the rapid growth of the AI sector [7][8]. - The company has shifted its focus towards Model as a Service (MaaS), recognizing the potential for high margins and growth in this area [6][11]. - The integration of AI capabilities into existing cloud services is seen as a critical strategy for competing against established players in the market [16]. Group 3: Competitive Landscape and Challenges - The cloud computing market is characterized by high entry barriers due to established players having strong customer ties and high data migration costs [8][9]. - Volcano Engine faces competition from major cloud providers like Alibaba Cloud, which are also investing heavily in AI and large model services [16]. - The overall market for MaaS is still in its early stages, with a projected size of only 1.29 billion RMB by mid-2025, despite a staggering growth rate of 421.2% [15]. Group 4: Future Outlook and Innovations - The company is exploring new growth avenues, particularly in the development of intelligent agents, which are expected to create significant economic value beyond traditional applications [15]. - Volcano Engine's strategy includes leveraging AI as a lever to penetrate the existing market, with a significant portion of its revenue expected to come from large model services [16]. - The company is also adjusting its sales strategies to prioritize MaaS products, which offer higher returns compared to traditional cloud services [11].

公众号记得加星标⭐️，第一时间看推送不会错过。 今天，微软宣布推出Azure Cobalt 200，这是微软专为云原生工作负载设计的下一代基于 Arm 的 CPU。 Cobalt 200 是微软持续优化云堆栈每一层（从芯片到软件）战略的一个重要里程碑。据微软所说， 该CPU的设计目标是：完全兼容使用现有 Azure Cobalt CPU 的工作负载；相比 Cobalt 100，性能 提升高达 50%；并与最新的 Microsoft 安全、网络和存储技术集成。 微 软 强 调 ， 与 前 代 产 品 一 样 ， Cobalt 200 针 对 常 见 的 客 户 工 作 负 载 进 行 了 优 化 ， 并 为 公 司 的 Microsoft 云产品提供了独特的功能。微软的首批生产级 Cobalt 200 服务器现已在微软的数据中心 上线，更广泛的部署和客户可用性将于 2026 年实现。 基于 Cobalt 100：领先的性价比 据结束，微软的 Azure Cobalt 之旅始于 Cobalt 100，这是他们首款专为云原生工作负载定制的处理 器。Cobalt 100 虚拟机自 2024 年 10 月起正式发布 ( ...

Core Insights - The server operating system market in China is experiencing significant growth, driven by the expansion of the digital economy and the acceleration of digital transformation across enterprises [1][6][8] - The total installed capacity of server operating systems in China is projected to increase from 2.987 million units in 2019 to 5.135 million units by 2024, with a compound annual growth rate (CAGR) of 11.45% [1][8] - The market is dominated by Linux, which holds a 79.6% share, while Windows Server accounts for 19.9% and other systems like Unix only 0.5% [8][10] Industry Overview - Server operating systems are critical for managing and coordinating hardware and software in enterprise IT systems, with major types including Windows Server, Linux, and Unix [1][3] - The industry is supported by a robust ecosystem of companies, including major players like China Software, Kirin Software, and Huawei, focusing on open-source technologies and self-developed capabilities [2][11] Market Growth - The market size of the server operating system industry in China is expected to grow from 4.59 billion yuan in 2019 to 9.4 billion yuan by 2024, reflecting a CAGR of 15.42% [8][9] - The demand for server operating systems is primarily concentrated in sectors such as finance, government, internet, energy, and telecommunications, with finance leading at 21.5% market share [10] Development Trends - The industry is moving towards cloud-native and intelligent architectures, integrating cloud computing and microservices to enhance operational efficiency [14] - There is a growing emphasis on ecosystem development, focusing on full-stack collaboration and open-source innovation to create a sustainable industrial ecosystem [15] - Customization for vertical industries is becoming a key trend, with server operating systems being tailored to meet specific needs in finance, government, and industrial sectors [16]

为深入推进投资者教育纳入国民教育体系，助力金融科技复合型人才培育，2025年11月7日与11日，招 商证券（600999）国家级投教基地联合招证国际走进南方科技大学，成功开展《大数据技术在金融行业 的应用》《云原生技术在金融行业的应用》两门前沿课程讲座。招证国际信息技术部数据专家王超主讲 授课，为南科大学子搭建起理论与金融实务深度衔接的学习桥梁，助力学子精准把握行业发展趋势。 课程中，王超以技术演进为主线，系统拆解大数据与云原生核心技术体系：在大数据课程中，从 Hadoop生态圈、Spark、Flink等关键技术特性切入，结合证券行业数字化转型实战案例，生动阐释技术 在精准客户画像、智能风控、精准营销等核心场景的落地应用，解读金融数据生态整合多元数据源的发 展趋势；在云原生课程中，梳理技术从萌芽到成熟的脉络，解析其为金融机构构建高效架构、支撑海量 交易处理的核心逻辑，通过券商与银行实际案例，展现技术在支付清算、风险管理中的落地路径。同 时，他前瞻性展望云原生与人工智能、大数据融合的行业图景，并结合行业需求为学生职业规划提供实 用指引，让学子切实感受技术迭代为金融业带来的深刻变革。 此次课程是招商证券产学研融合 ...

内容概要：数据库管理系统（DBMS）是一种操纵和管理数据库的大型软件，用于存储、管理和维护数 据库。它对数据库进行统一的管理和控制，以保证数据库的安全性和完整性。通俗来说，数据库管理系 统是用来管理数据库的系统，使得数据库的管理和使用更加科学、方便和安全。数据库作为承载数据存 算的核心底座，是数字经济发展的重要支撑，更是企业数智化转型的关键环节。过去几年，全球数据库 新技术、新业态、新模式不断涌现；我国数据库的应用创新工作在重点行业和领域快速推进，行业规模 不断壮大。CCSA TC601数据显示，2024年全球数据库市场规模约为1154亿美元，其中中国数据库市场 规模为83.7亿美元（约596.2亿元人民币），占全球7.3%。1964 年，查尔斯·巴赫曼开发了全球第一个数 据库管理系统-网状数据库管理系统（IDS），解决了层次结构无法针对复杂数据关系进行建模 的问 题。随后数据库管理系统不断发展，到2024年全球数据库管理系统（DBMS）市场规模已达627.3亿美 元，持续领跑平台软件市场。聚焦中国市场，随着数据库的不断发展，对数据库管理和维护的需求不断 攀升，为数据库管理系统市场带来了广阔的发展空间，行业增 ...

瑞财经 刘治颖 近日，桥介数物（深圳）科技有限公司（以下简称"桥介数物"）宣布完成PreA+轮融 资，本轮融资由深创投独家投资。 本轮融资资金将主要用于下一代云原生机器人动作开发平台的迭代升级和商业化落地，以及加速推进公 司出海战略布局。 值得关注的是，这已是桥介数物今年内完成的第三轮融资。三个月之前，桥介数物刚刚完成PreA轮融 资。 桥介数物成立于2023年，是一家足式机器人控制系统提供商。帮助多家人形机器人公司完成从0到1的强 化学习运动控制demo开发；在2024年8月的世界机器人大会(WRC)上，20多家人形机器人厂商中有11家 采购了桥介的运动控制解决方案。 截至2025年第三季度，桥介数物行为控制方案已成功部署于50余种不同构型的机器人型号，覆盖人形、 四足及轮足等多元应用场景。 天眼查显示，桥介数物实际控制人为尚阳星，总持股比例为55.14%，表决权为63.82%。目前，尚阳星 担任公司董事长。 桥介数物创始人尚阳星，出生于1999年，本科毕业于华中科技大学（2017-2021年），随后保研至南方 科技大学，师从逐际动力创始人张巍教授，并于2023年创立桥介数物。 ...

Core Insights - The company reported a total revenue of RMB 44.02 billion for the first three quarters of 2023, representing a year-on-year growth of 36.8%, significantly surpassing last year's growth rate [1] - The gross profit reached RMB 16.21 billion, with a year-on-year increase of 20.1%, and a stable gross margin of 36.8% [1] - The company achieved its first quarterly profit in Q3 2023, driven by increasing demand for practical AI applications across various industries [2] Financial Performance - Revenue from the core product, the AI platform, surged to RMB 36.92 billion, marking a 70.1% year-on-year growth, contributing to the acceleration of overall company performance [2] - R&D expenses amounted to RMB 14.89 billion, an increase of 8.4% year-on-year, with an R&D expense ratio of 33.8%, down 8.9 percentage points, indicating improved efficiency in R&D investment [2] Strategic Investments - The company invested in domestic computing power by acquiring over 9% of a leading domestic GPU manufacturer and strategically investing in another [3] - The launch of ModelHub XC and the AI engine EngineX aims to enhance the synergy between domestic computing power and large models, facilitating the conversion of hardware capabilities into visible business value [3] Technological Advancements - The company introduced the Virtual VRAM expansion card, allowing single-card memory to be expanded to 256GB, supporting large-scale AI tasks without hardware replacement [3] - The company has developed GPU dynamic scheduling capabilities based on Kubernetes v1.34, significantly lowering the barriers to cloud-native computing management [4] Market Position and Future Outlook - The company has maintained its position as the market leader in China's machine learning platform sector for seven consecutive years, with plans to further develop its AI platform as a core engine for productivity across various industries [4] - The company is expanding into new fields such as AI combined with stablecoins and sports, with recent initiatives in AI-driven sports solutions aimed at enhancing training and promoting digital upgrades in fitness [4] Consumer Electronics Expansion - The Phancy consumer electronics business is gaining momentum, focusing on open technology collaborations to transition consumer electronics into consumer goods [5] - The company has launched AI smart glasses with a 13-megapixel camera and is collaborating with brands like AOC to innovate in smart wearable devices [5]

Core Insights - Eclipse Foundation has launched the Agent Definition Language (ADL) within its open-source platform Eclipse LMOS, allowing users to define AI behaviors without coding [2] - LMOS aims to reconstruct the development and operation chain of enterprise-level AI agents in a unified and open manner, challenging proprietary platforms and Python-centric enterprise AI tech stacks [2][4] - The project follows a "land first, open source later" approach, initially developed from Deutsche Telekom's production-level practices in traditional cloud-native architecture [2][6] Group 1: Project Overview - ADL is a structured, model-agnostic description method that simplifies the definition of AI behaviors [2] - LMOS is designed to run natively on Kubernetes/Istio, targeting the JVM ecosystem and facilitating the integration of AI capabilities into existing infrastructures [2][4] - The project was led by Arun Joseph, who aimed to deploy AI capabilities across 10 European countries for Deutsche Telekom [6] Group 2: Technical Implementation - The platform utilizes Kubernetes as its foundation, deploying agents as microservices and enhancing them with custom resources for declarative management and observability [7] - Eclipse LMOS integrates seamlessly with existing DevOps processes and tools, allowing for minimal migration costs when introducing AI agents into production systems [7][8] - The initial deployment of agents has resulted in significant operational efficiencies, including a 38% reduction in human handovers and processing approximately 4.5 million conversations monthly [9][10] Group 3: Development Efficiency - The development cycle for creating new agents has been significantly reduced, with initial deployments taking one month, later decreasing to as little as one to two days [10] - A small team consisting of one data scientist and one engineer can rapidly iterate from idea to production deployment, showcasing cost advantages [10][12] - The dual strategy of LMOS includes both the open-source platform and the ADL, which allows business and engineering teams to collaboratively define agent behaviors [12][17] Group 4: Market Positioning - Eclipse LMOS positions itself between the agile, open-source Python ecosystem and the robust, mature JVM world, aiming to bring AI agents into familiar enterprise infrastructures [22] - The platform is designed to enable organizations to build scalable, intelligent, and transparent agent systems without the need to overhaul existing technologies [22] - Eclipse Foundation's executive director emphasizes the need for open-source solutions to replace proprietary products in the agentic AI space [22]

Core Insights - The "2025 Cloud Industry and Standard Application Conference" was successfully held in Beijing, focusing on cloud computing industry development, standard system construction, technology trends, and ecological collaboration [1][2] - The China Electronic Technology Standardization Institute aims to enhance the standardization of cloud computing, supporting high-quality development and global competitiveness [1] - The Ministry of Industry and Information Technology emphasizes the critical role of cloud computing in fostering new productive forces and driving industrial transformation [1][2] Group 1 - The cloud computing sector in China has established a comprehensive standard system, with a total of 45 national standards published, and is actively participating in international standardization efforts [2] - The "Cloud Computing Comprehensive Standardization System Construction Guide (2025 Edition)" aims to formulate over 30 national or industry standards by 2027, serving more than 1,000 enterprises [2] - The conference released the "2025 Enterprise AI Cloud Industry Ecosystem Map," outlining the collaborative innovation landscape of the industry [3] Group 2 - Experts from leading organizations such as Huawei Cloud and the University of Science and Technology of China shared insights on the integration of cloud computing and artificial intelligence, computational power applications, and cloud-native technologies [3] - The conference recognized outstanding contributions in cloud computing standardization by awarding excellent working group leaders, member units, and individuals [3]