（文章来源：第一财经） 据工信部消息，工信部印发《工业互联网和人工智能融合赋能行动方案》。其中提出，加快工业网络开 放智能升级。面向智能装备协同生产、工业模型训练推理、工业智能体通信交互等工业智能新需求，支 持工业企业综合运用5G/5G-A、工业光网、时间敏感网络（TSN）、单对线以太网（SPE）、边缘计 算、云化控制等技术推动工业网络控网算一体化演进和能力升级，探索新型工业网络适应工业智能业务 的创新应用模式。加快5G可编程逻辑控制器（PLC）、AI路由器、工业算网交换机等产品攻关，滚动 发布新型工业网络产品目录，推进重点行业开展新型工业网络改造，形成自组网、自管理、自优化、自 修复等智能化网络能力。 ...

公告显示，该项目实施主体为千方科技控股子公司千曙科技,其核心盈利模式将聚焦于自动驾驶运输服 务、SaaS（软件即服务）模式软件订阅等。千方科技自2016年起牵头成立了北京智能车联产业创新中 心，协助北京市制定了全国第一套自动驾驶上路测试政策、标准并建设了全国首个自动驾驶T5级封闭 测试场，通过助力北京市高级别自动驾驶示范区的建设，参与了车路云一体化进程。在此过程中，公司 构建了涵盖路侧智能感知、边缘计算、云控平台及车端V2X（即车对外界的信息交换）设备的完整技术 产品体系。 北京商报讯（记者 魏蔚）12月28日，北京商报记者获悉，千方科技发布公告称，为把握AI与交通深度 融合的战略机遇，公司拟将原项目"下一代智慧交通系统产品与解决方案研发升级及产业化项目"后续资 金约9.56亿元进行变更并集中投入于更具前景的"物流无人化关键技术研发及产业化项目"，此举旨在全 力发展干线物流自动驾驶业务，为行业提供可规模化落地的无人化物流整体方案。 ...

格隆汇12月5日丨中富通(300560.SZ)在互动平台表示，公司主营业务是通信服务业务、信息化软件服务 业务、数字营销业务、边缘计算等业务；公司目前暂未布局商业航天相关业务。 ...

Core Viewpoint - The company, Xingchen Technology, announced that it will hold a developer conference at the end of this year, showcasing multiple new technologies and chip products related to intelligent robotics, edge computing, and 3D perception [2] Group 1 - The developer conference is scheduled for the end of this year [2] - The conference will feature the latest advancements in intelligent robotics, edge computing, and 3D perception technologies [2] - New chip products will also be presented during the conference [2]

Core Insights - The report from Industrial Securities indicates that in Q3 2025, active public funds are aligning with the AI industry trend, showing a significant shift towards "increasing hardware and decreasing software" [1][10] Group 1: TMT Sector Allocation - The allocation ratio of active public funds to the TMT sector has increased significantly by 11.3 percentage points to 39.9% in Q3 2025, nearing historical highs last seen at the end of 2020 [2] - The TMT sector has grown to encompass over 1,000 companies, with a free float market capitalization exceeding 25%, allowing for a larger capacity for fund allocation [2] - The adjusted indicator of "active public fund allocation ratio/free float market capitalization ratio" for TMT in Q3 2025 is 1.52, which is not extreme compared to historical highs [3][6] Group 2: AI Subsector Analysis - In Q3 2025, active public funds have notably increased their positions in upstream network communication hardware (mainly North American computing chains) and chip storage (mainly domestic computing chains), while reducing positions in midstream algorithm technology and software [10][12] - The allocation in upstream network communication hardware is at 19.4%, with a significant increase in configurations for components like optical modules and PCBs [11] - The midstream software sector has seen a reduction in most areas, with application software and office software showing historically low allocation ratios [12][13] Group 3: Downstream AI Applications - The downstream AI sector has seen an increase in allocations towards consumer electronics such as AI phones and wearable devices, while humanoid robots have been reduced [13] - Most downstream AI applications have experienced a decrease in allocations, with gaming and a few other sectors showing some increases, but overall configurations remain at historically low levels [13]

Core Insights - 79% of surveyed enterprises believe that generative AI will have a disruptive impact on their business within the next 18 months, which is 12 percentage points higher than the Asia-Pacific average [1] - 37% of enterprises have deployed generative AI in production environments, while 61% are in the testing and proof-of-concept stages, indicating a shift from the "PPT stage" to the "practical stage" in AI implementation [1] - The key focus for 2023-2024 is the "large model parameter competition," with enterprises pursuing "hundred billion-level parameters" and "multimodal capabilities" [1] - By 2025, the emphasis will shift to "scenario implementation," where businesses seek to solve real-world problems with AI [1] Infrastructure Strategy - Enterprises in the Asia-Pacific region recognize that centralized cloud architectures cannot meet the growing demands for scale, speed, and compliance, necessitating a rethink of infrastructure strategies to include edge services [1] - The need for a modern digital foundation that integrates "cloud-core-edge computing" is emphasized to deploy intelligent services closer to users and applications [2] Challenges in AI Implementation - 37% of enterprises that have deployed generative AI report that over 60% experience unexpected delays in real-time interactive applications, with conversion rates dropping by 40% due to latency issues [3] - Cost is a significant barrier for many enterprises in adopting AI applications, as the massive data generated by AI inference increases bandwidth costs [3] - 72% of outbound enterprises have been forced to abandon centralized cloud processing due to compliance requirements related to "data outbound," particularly concerning user privacy data [3] Edge Computing Emergence - The traditional reliance on public cloud models is insufficient for all enterprises to embrace AI, leading to the urgent need for a restructured digital foundation that incorporates edge computing [4] - Edge computing is positioned as a core technology for building the next generation of digital infrastructure, enabling distributed deployment to reduce latency and enhance business responsiveness [4] Market Trends and Predictions - The global market for edge cloud is projected to reach 185.1 billion yuan in 2024, with China accounting for approximately 70% of this market [5] - By 2025, edge IT is expected to be the most significant area of IT spending growth for most Chinese enterprises, with 80% of CIOs in the Asia-Pacific region relying on edge services to support AI workloads by 2027 [6] Investment Directions - Future investments in edge IT will focus on four areas: supporting digital operations like AI and IoT, ensuring business continuity when disconnected from core or cloud resources, supporting operations in remote areas, and reducing connectivity costs [7] Integration of Generative AI and Edge Computing - The integration of generative AI and edge computing is bridging the gap between centralized cloud resources and distributed edge environments, ensuring scalability and performance [9] Six Pillars of AI-Ready Infrastructure - The report outlines six core pillars for building AI-ready infrastructure, emphasizing a holistic approach that extends from core to edge [10] Pillar 1: AI Readiness - Infrastructure must be adapted for AI, focusing on hardware optimization and personalized application support to enhance efficiency and user experience [11][12] Pillar 2: GenAI Deployment - Deployment of generative AI is shifting from a focus on large model parameters to lightweight adaptations for edge environments, necessitating hardware investments [13] Pillar 3: Modern Edge IT - Modern edge IT emphasizes extracting value at the data source, prioritizing edge inference and efficient data storage strategies [14] Pillar 4: Edge Optimization Architecture - A unified scheduling solution for distributed resources is essential to avoid "edge island" scenarios, with a three-layer architecture proposed [15] Pillar 5: Cloud to Edge - Existing public cloud investments should be leveraged for edge deployment, focusing on interoperability and data consistency [16] Pillar 6: Autonomous Operations - As edge nodes scale, AI-driven management of infrastructure becomes crucial, enhancing operational efficiency and reducing downtime [17][18]

Core Insights - 79% of surveyed enterprises believe that generative AI will have a disruptive impact on their business within the next 18 months, which is 12 percentage points higher than the Asia-Pacific average [2] - 37% of enterprises have deployed generative AI in production environments, while 61% are in the testing and proof-of-concept stages, indicating a shift from the "PPT stage" to the "practical stage" in AI implementation [2] - The key focus for 2023-2024 is the "large model parameter competition," with enterprises pursuing "hundred billion-level parameters" and "multimodal capabilities" [2] - By 2025, the emphasis will shift to "scenario implementation," where businesses seek to solve real-world problems with AI [2] Infrastructure Strategy - Enterprises in the Asia-Pacific region recognize that centralized cloud architectures cannot meet the growing demands for scale, speed, and compliance, necessitating a rethink of infrastructure strategies to include edge services [2] - The reliance on public cloud for production applications has exposed shortcomings, particularly in the context of generative AI [4] - 37% of enterprises that have deployed generative AI report that over 60% experience unexpected delays in real-time interactive applications, with conversion rates dropping by 40% due to latency issues [4] Edge Computing Emergence - The traditional reliance on public cloud is insufficient for all enterprises to embrace AI, leading to a need for a modernized digital foundation that integrates edge computing [5] - Edge computing is becoming a core technology for building the next generation of digital infrastructure, enabling distributed deployment to reduce latency and improve responsiveness [5][6] - By 2024, the global market for edge cloud is expected to reach 185.1 billion yuan, with China accounting for approximately 70% [6] Investment Directions - Future investments in edge IT will focus on supporting digital operations, ensuring business continuity when disconnected from core or cloud resources, and reducing connectivity costs [7] - The integration of generative AI and edge computing is bridging the gap between centralized cloud resources and distributed edge environments, ensuring scalability and performance [10] Six Pillars of AI-Ready Infrastructure - The report outlines six core pillars for building AI-ready infrastructure, emphasizing a holistic approach that extends from core to edge [11] - Pillar one focuses on making infrastructure adaptable to AI, enhancing efficiency and user experience through hardware optimization and personalized application support [12] - Pillar two highlights the shift from large model competition to edge adaptation, requiring hardware investments in edge-level GPUs and heterogeneous computing chips [14] - Pillar three emphasizes modernizing edge IT to extract value at the data source, reducing data transmission volumes significantly [15] - Pillar four addresses the need for a unified scheduling of distributed resources to avoid "edge island" scenarios [16] - Pillar five advocates for extending existing public cloud investments to edge deployments, emphasizing interoperability and data consistency [17] - Pillar six focuses on autonomous operations driven by AI, enhancing monitoring, resource allocation, and fault recovery capabilities [18]

Core Insights - The rapid development of technology has led to deep integration within the industry chain, with many companies excelling in their respective fields while strategically positioning themselves in two key areas: GPU computing power and collaboration within the Huawei ecosystem [1] Company Summaries - Heptagon is a strong player in the smart controller sector, leveraging AI algorithms to enhance the intelligence of home appliances. The company has invested in Moore Threads, entering the GPU market, which will significantly impact its future production capacity. Additionally, its collaboration with Huawei HiSilicon lays a solid foundation for addressing various industry technical challenges and future joint research [3] - Chuling Information has made significant strides in the big data access sector, showcasing leading technology in the industry. By indirectly investing in AI computing power and operations, the company demonstrates a long-term vision for Moore Threads. As part of the Huawei ecosystem, its wholly-owned subsidiary plays a crucial role in technology certification and industry alliances, facilitating the implementation of intelligent customer service and large model technologies [3] - Yingqu Technology focuses on smart hardware manufacturing and has also invested in Moore Threads. Its collaboration with Huawei is vital, as it supplies numerous core components to Huawei's new energy vehicle factories, showcasing strong manufacturing capabilities in smart home and industrial control sectors [3] - Zhejiang University Network New is a notable player in the smart city and cloud service sectors, acting as the general agent for Moore Threads in Zhejiang Province. The company is responsible for distributing and promoting various businesses, including servers and edge computing. Its relationship with Huawei in the Xinchuang and Harmony ecosystem collaboration is significant for driving industrial upgrades and domestic substitution [3] - Kehua Data excels in data center and clean energy solutions, collaborating with Moore Threads and Gui'an New Area to establish an intelligent computing center. This partnership strengthens its connection with Moore Threads in computing resources and provides essential support for Huawei's collaborative development in related fields [3] Emerging Display and Component Innovations - Hanbo High-tech has demonstrated strong technology in vehicle displays and Mini-LED backlight modules, aligning its product layout with the needs of Moore Threads and Huawei ecosystem terminals, catering to multiple application scenarios [4] - Maijie Technology has shown steady development in the electronic components sector, supplying critical magnetic components to Moore Threads and integrating into the computing server supply chain, while also providing power supplies for Huawei's base stations and other core components, showcasing diverse collaborative capabilities [4] Industry Trends - The performance of these companies in smart technology and industry ecosystem collaboration highlights their unique competitive advantages. They are deeply engaged in popular sectors, driving the autonomous and intelligent development of the industry chain [5]

Group 1 - The event organized by CSAIA in Singapore focused on the unique opportunities and challenges within the AI ecosystem in Singapore, featuring participation from top global tech companies and investment firms [1][2] - Key discussions revolved around Singapore's positioning in the global AI competition, including strategies for overcoming challenges posed by larger tech giants and the importance of localized AI solutions [2][4] Group 2 - The forum included three main discussion panels, startup pitches, and open exchanges, emphasizing investment, product technology, and business growth [2] - Participants highlighted the significance of edge computing and inference chips, addressing challenges such as low latency, privacy, and power consumption in AI applications [6][7] Group 3 - The focus on localized AI solutions was emphasized, with companies encouraged to avoid competing in the "big model arms race" and instead tailor products to meet the specific needs of Southeast Asian markets [8] - The discussions also covered the importance of rethinking business processes from an AI-native perspective to enhance efficiency and effectiveness [9] Group 4 - Early-stage investment strategies were discussed, with a focus on the importance of direction and team dynamics, as well as the need for rapid iteration and data-driven decision-making [12][13] - The event showcased over ten high-potential early-stage projects across various sectors, including AI marketing, healthcare, and robotics, reflecting Singapore's innovative AI landscape [29]

Economic Overview - The GDP growth for the first half of the year is 5.3%, with consumption contributing over half, indicating a stable economic foundation [3] - The central bank is implementing policies such as interest rate cuts and a 500 billion yuan loan to support consumption and elderly care, aiming to "stabilize confidence" [3] Investment Opportunities - **Technology Sector**: The government has launched the "Artificial Intelligence+" initiative, focusing on computing power and chips, with many companies seeing AI revenue approaching 25% [4] - **Consumer Recovery**: Retail sales increased by 5% in the first half of the year, with policies driving demand for 12 million home appliances, benefiting leading companies like Midea and Haier [4] - **Renewable Energy**: The cost of HJT solar cells has dropped to 0.15 yuan per kWh, and there is a potential surge in offshore wind installations, with companies like Mingyang Smart Energy and Daikin Heavy Industries having orders extending into next year [4] Risks to Monitor - High savings rates among consumers indicate that domestic demand may not recover quickly [5] - Rapid technological advancements in semiconductors could lead to volatility, necessitating careful monitoring of R&D investments [5] Investment Strategy - Diversification is recommended, with suggestions to allocate funds into indices like CSI 300 and STAR Market 50, as well as gold ETFs, which have seen a 50% increase in scale [7] - Following government policies in emerging sectors such as low-altitude economy and quantum computing may reveal new investment opportunities [7] - Flexibility in investment positions is crucial, with attention to upcoming events like special treasury bonds and developer conferences [8]