炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 来源：专利创新魔方 统信软件，国产桌面 / 服务器 OS 核心厂商，适配生态丰富。 麒麟软件（中国软件旗下，600536），银河麒麟 OS，党政、金融等关键行业核心供应商。 以上图片来自新华社截图 新华社消息，9日上午，习近平总书记来到位于北京亦庄的国家信创园，了解信息技术应用创新和北京 加快建设国际科技创新中心情况，察看代表性科技创新成果展示，并同科研人员和科技企业负责人代表 亲切交流。 从新闻报道公开的视频中，参加本次会见的人士可能有： 雷军（小米集团创始人）、唐杰（智谱 AI 创始人）、廖湘科院士（麒麟软件核心负责人）、胡伟武 （龙芯中科董事长）、刘闻欢（统信软件总经理）、冯裕才（达梦数据库创始人）、王继平（人大金仓 总经理）…… 国家信创园已集聚 700 + 企业，按产业链环节分类，覆盖 CPU、操作系统、数据库、整机、网络安 全、应用软件等核心赛道。 主要核心企业有： 龙芯中科（688047），自主指令集 LoongArch CPU、配套芯片组国产 CPU 龙头，党政与终端市场份额 领先。 飞腾信息（中国长城旗下，000066 ...

证券日报网讯2月8日，云意电气（300304）在互动平台回答投资者提问时表示，公司高度重视数字化、 智能化发展，已搭建并完善ERP、MES、WMS等核心数字化管理系统，实现了从原材料采购、生产制 造到成品交付全流程的端到端数据追溯与精细化运营管理，具备工业4.0相关实施能力与应用实践，数 字化运营水平持续提升。 ...

找钢集团 20260204 摘要 找钢集团是中国领先的第三方钢铁交易平台，年交易量约 5,000 万吨， GMV 约 1,500 亿元，通过轻资产模式连接钢铁行业各环节，提供交易、 物流、金融和 SaaS 等增值服务，佣金模式不受钢价周期影响。 公司积极拓展 AI 跨品类和海外市场，年增长率均达 100%。AI 跨品类 服务已覆盖芯片、工业电器及有色金属，海外市场已在中东、东南亚和 非洲设立分公司，成为一带一路地区最大的产业互联网平台。 找钢集团主要收入来源包括交易佣金（每笔订单约 6 元）、物流费用 （每吨 3-5 元）、SaaS 订阅年费（约 3,000 元）以及金融数据服务费 （贷款金额的千分之五到百分之二），佣金每年增长约 10%。 国际化战略是找钢集团的重要增长引擎，尤其是在中东、东南亚和非洲 地区，2024 年增速达 170%，2025 年保持 100%增速，未来将拓展加 工及金融物流等增值服务，复制商业模式。 AI 技术在找钢集团的应用日益深入，通过大模型技术将订单匹配准确度 提升至 95%，AI 相关收入占公司总收入的 10%，毛利占 5%。公司计 划打造 B2B 全流程通用 Agent，并积 ...

工业软件行业丨研究报告 工业软件具有发展的紧迫性和必要性，且当前处于政策红利带的有利时间窗口期。 当前，我国工业和经济达到分水岭，经济体发展需要创新驱动，而工业软件作为工业知 识 的 载体，既是新型工 业化的核心生产资料和关键生产力，又是工业大脑和数字基 石，其 自主可控意义深远。不同于 国外工业软件是 先工业后软件的自然生长 ，我国的工业 软件先是用市场换 效 率，后是工业 和软件同步的压缩 式 发展，现在是追赶核 心技术可控，保障供应链安全。因此，当前工业软件既有发展的必要性，又有发展的紧迫性。 工业软件是一个慢行业，发展需要耐心和长期主义思想，同时在变化与重构中，也为企业带来机遇与挑战。 中国的工业软件市场是千亿的大盘子， 2024 年市场接近 3000 亿，市场增长稳健， 但 核心技 术空心、产业结构失衡等 问题凸显。当前，研发设计类工业软件是卡脖子最为 严重，其本质 是与数学 与基础 学科相关的根技术缺乏海量真实工业场景试错进行工程优化，表现为实体就是核心组件 / 引擎层受限。值得注意的是，根技术的突破没有捷 径可走，只能死磕 。 工业软件产业处于动态发展中，未来产业、市场、产品的发展方向值得探讨与 ...

卢芷心 S0340524100001 电话：0769-22119297 邮箱： luzhixin@dgzq.com.cn S0340521020001 电话：0769-22110619 邮箱： luoweibin@dgzq.com.cn S0340520060001 电话：0769-22119430 邮箱： chenweiguang@dgzq.com.cn | 陈伟光 | | | --- | --- | | SAC 执业证书编号： S0340520060001 | | | 电话：0769-22119430 邮箱： | | | chenweiguang@dgzq.com.cn | | | 主要数据 2026 | 年 1 月 28 日 | | 收盘价（元） | 56.42 | | 总市值（亿元） | 153.21 | | 总股本（亿股） | 证 2.72 | | 流通股本（亿股） | 券 2.70 | | ROE(TTM) | 6.89% 研 | | 12 月最高价（元） | 66.06 究 | | 12 月最低价（元） | 26.17 报 | | 股价走势 | 告 | | 资料来源：iFind，东莞证券研究所 ...

Riding a powerful mix of cloud infrastructure momentum and AI enthusiasm, ORCL stock went on a blistering rally that had investors fully locked in. After delivering strong earnings last year, the stock rose – most notably in September, when shares jumped nearly 36% in a single session following a blockbuster Q1 report. That surge pushed Oracle to a high of $345.72 on Sept. 10, cementing its status as a top AI infrastructure beneficiary.With expanding capabilities in cloud infrastructure, hardware, and consu ...

Investment Rating - The report assigns an investment rating of "Buy" for the AI industry, indicating strong growth potential and favorable market conditions [3]. Core Insights - The AI sector is projected to experience significant growth, with an expected increase in market size from $283 billion to $1 trillion by 2028, reflecting a compound annual growth rate (CAGR) of approximately 28% [3][4]. - The report highlights the transformative impact of AI technologies across various industries, including healthcare, finance, and manufacturing, emphasizing their role in enhancing efficiency and driving innovation [3][7]. - Key drivers of growth include advancements in machine learning, natural language processing, and automation technologies, which are expected to create new business opportunities and improve operational efficiencies [3][6]. Summary by Sections Market Overview - The AI market is currently valued at $283 billion, with projections indicating a potential growth to $1 trillion by 2028, representing a CAGR of 28% [3][4]. - The report notes that the adoption of AI technologies is accelerating across multiple sectors, driven by the need for digital transformation and improved decision-making capabilities [3][7]. Growth Drivers - Major growth drivers identified include advancements in machine learning, natural language processing, and automation technologies, which are expected to enhance productivity and create new revenue streams [3][6]. - The increasing demand for AI solutions in sectors such as healthcare, finance, and manufacturing is highlighted as a key factor contributing to market expansion [3][7]. Competitive Landscape - The report outlines a competitive landscape characterized by rapid innovation and the emergence of new players, alongside established tech giants [3][4]. - Companies that effectively leverage AI technologies to enhance their product offerings and operational efficiencies are expected to gain a competitive edge in the market [3][6].

Core Insights - The core issue in the manufacturing and trading industry is the ineffective implementation of digitalization, with many companies investing in systems without achieving significant improvements in efficiency [1][3]. Group 1: Digitalization Challenges - 62% of manufacturing and trading companies have attempted digitalization, but only 28% report significant results, indicating a widespread struggle with effective implementation [1]. - Common pain points include management silos, data fragmentation, and lack of standardized processes, leading to inefficiencies and errors in order fulfillment and inventory management [3][4][6]. - Information transfer relies heavily on informal communication methods, such as WeChat and emails, resulting in delays and inaccuracies in order processing [4][5]. Group 2: Common Mistakes in Digitalization - Companies often make the mistake of purchasing comprehensive ERP systems without ensuring inter-module connectivity, leading to underutilized systems [8]. - Focusing on single-point solutions without integrating them into the broader business process can result in operational disconnects, such as inventory discrepancies [8]. - Custom development projects that attempt to cover all needs can lead to prolonged implementation times and increased complexity, ultimately hindering user adoption [8]. Group 3: Recommended Steps for Digitalization - The first step in digitalization should be to streamline and connect the core business processes of order management, production, and delivery, as these directly impact revenue and customer retention [10][16]. - Identifying the most critical pain points in the business process is essential for effective digital transformation [10][17]. - Utilizing an integrated platform that connects all relevant data across departments can significantly enhance operational efficiency and decision-making capabilities [10][11].

Group 1 - AI is evolving from "digital assistants" to "autonomous execution clusters," with a significant transition expected by 2026 towards multi-agent systems that will redefine operational leverage for enterprises [1][2][7] - The integration of electrification, materials science, and AI is creating an "electro-industrial stack" that will serve as the foundational logic for the physical world, leading to a renaissance in American manufacturing [1][2][21][22] - SaaS is shifting from passive data recording to proactive reasoning, enabling personalized services that cater to individual needs rather than a one-size-fits-all approach [1][2][13][14] Group 2 - The future of enterprise software will be driven by multi-agent systems that can understand instructions and manage complex workflows collaboratively, resulting in significant increases in per-employee revenue [7] - AI is expected to automate 90% of repetitive tasks, shifting investment focus from user engagement metrics to the quality of automated task completion [8] - The emergence of platforms that can efficiently manage unstructured data will be crucial for enterprise knowledge management, representing a potential multi-billion dollar market [8] Group 3 - The rise of AI-driven cybersecurity will automate repetitive tasks, allowing security teams to focus on deeper vulnerabilities and crime tracking [10] - Creative tools will integrate across modalities, drastically reducing content production costs and enabling users to generate complex outputs from simple inputs [10] - The concept of an AI-native university is anticipated, which will optimize its curriculum and research collaboration in real-time, indicating a transformation in education and workforce development [10] Group 4 - The "electrification revival" of American factories is being driven by software and AI, enabling efficient production processes akin to assembly lines [21] - The integration of software with physical automation will redefine industrial capabilities, allowing for rapid production of complex products [21][22] - Countries and companies that master the electrification supply chain will hold strategic advantages in future industrial and military technologies [24] Group 5 - The future of healthcare will focus on preventive services, leveraging AI to manage health proactively, which presents a lucrative subscription-based business model [25] - Cryptocurrencies are expected to evolve, with privacy becoming a key competitive factor and stablecoins emerging as the foundational layer for global payments [27][29] - Decentralized networks will transform communication methods, enhancing user privacy and control over information [30]

Core Viewpoint - Traditional SaaS applications like CRM and ERP systems will not be replaced but will evolve to serve as the infrastructure for AI Agents, which will enhance the importance of data definition and interpretation within enterprises [2][10][15] Group 1: The Role of AI Agents - AI Agents will not eliminate traditional software systems; instead, they will necessitate a clearer separation between how tasks are performed and the sources of facts [2][10] - The effectiveness of AI Agents is contingent upon their ability to access and understand the correct data from various systems, highlighting the need for accurate and structured input data [2][9] - The emergence of AI Agents creates significant entrepreneurial opportunities for companies that can help businesses manage and structure their unstructured data [3][10] Group 2: Data Management Challenges - A significant portion of enterprise knowledge (80%) exists in unstructured data, which is becoming increasingly difficult to manage [2] - The complexity of data definitions within organizations leads to discrepancies in key metrics like Annual Recurring Revenue (ARR), complicating the role of AI Agents in providing accurate information [7][11] - The traditional approach of consolidating data into warehouses has only partially succeeded, as operational teams still rely on individual systems for real-time transactions [8][10] Group 3: Evolution of Systems - CRM and ERP systems will transition from user-centric interfaces to machine-oriented APIs, allowing AI Agents to interact with these systems programmatically [12][15] - The core value of enterprise systems lies in their ability to encapsulate chaotic data, which will remain essential despite changes in interface and interaction methods [13][15] - The demand for a clear, authoritative source of truth will only increase as AI Agents become more prevalent in business processes [14][15] Group 4: Future of Data Infrastructure - The combination of data warehouses, semantic layers, and governance tools will form the foundation for AI Agent workflows, evolving beyond traditional reporting systems [10][12] - The valuation of AI platforms will increasingly depend on their ability to define and manage facts, rather than just their user interfaces [14][15] - Companies that can create exceptional AI Agent experiences based on reliable data sources will have a competitive advantage in the evolving landscape [15]