Group 1 - The core viewpoint of the article highlights the recent policies implemented by the government to standardize local investment attraction practices, aiming to reduce excessive competition and resource waste in the context of building a unified national market [1][4] - Local governments are experiencing short-term challenges due to the new policies, which have shifted from special treatments for major projects to more generalized support, impacting their ability to attract investments [1][2] - The normalization of investment attraction is accelerating, with innovative models emerging, such as market-oriented investment companies replacing traditional government-led initiatives, which enhances efficiency but also poses sustainability challenges [2][3] Group 2 - Regional collaborative investment models are gaining traction, focusing on resource sharing and industrial cooperation, as seen in initiatives like the Shanghai-Hangzhou Collaborative Innovation Center [3] - The shift from competing for individual projects to chain-based investment strategies is becoming prevalent, with industries clustering around key enterprises to create significant economic ecosystems [3] - Improving the business environment is a top priority for local governments, emphasizing the importance of service quality and operational support over mere financial incentives to attract businesses [4]

记者从深圳数位大数据科技有限公司(以下简称"数位大数据")获悉，其核心数据产品成功通过数据资产 化认证，并凭借该资产获得银行授信贷款。与此同时，公司正式入驻陕西丝路数据交易平台及贵州省数 据流通交易服务中心。此次突破是继成功上线浙江、厦门大数据交易中心后，数位大数据在数据要素流 通领域的又一重要里程碑。 作为国内少数具备"数据采集—处理—分析—应用"全链路能力的AI大数据企业，数位大数据坚持十年线 下全域数据积累，构建了不可复制的"动态商业数据库"。通过自研地理信息采集系统与AI算法，数位大 数据精准刻画了全国300余个城市超1.2亿条POI数据，覆盖门店级位置、客流动线、存活周期等1000+维 度的稀缺字段，填补了传统公开数据的空白。 广东数联数据要素有限公司正是认准数位大数据持有数据价值，运用其数据资产金融变现能力，成功为 数位大数据实现数据资源化、资产化、资本化全链路服务，联手交通银行深圳学府支行为全国民营企业 数据资产融资变现落地再添新案例。除数据资产入表及银行融资成熟能力外，广东数联在数据资产专项 债、数据资产证券化、地方政府公共数据运营及可信数据空间等方面也均有落地能力。 此次入驻两大交易平台，数 ...

盛夏的乌兰察布草原，传统奶香与现代数据流在这里碰撞出高质量发展的火花。作为扎根内蒙古的本土 银行，内蒙古银行用金融活水精准滴灌地方民营经济发展脉络，以创新金融服务为笔，绘就了一幅"产 业+金融"协同发展的新画卷。 内蒙古银行乌兰察布分行相关负责人介绍，这场银企"双向奔赴"源于长期的战略互信。2014年，内蒙古 银行与兰格格乳业正式建立战略合作关系 —— 彼时，银行看中了企业在细分领域深耕多年的专业积 淀，而企业正迫切需要金融力量支撑其智能化转型。双方的合作不仅见证了传统乳业向高端化、智能化 的跃升，更彰显了地方金融机构深耕本土、服务实体的责任担当。 如今，俯瞰兰格格乳业的智能化工厂，智能化生产车间与14条智能生产线交相辉映。在金融活水的持续 滋养下，一场"科技+金融"驱动的产业变革正在草原深处蓬勃生长。 能源基地 数字化转型 在乌兰察布市郊，中金数据算力中心项目的施工现场机器轰鸣，工人们正紧锣密鼓地进行设备安装和调 试。这座占地数百亩的"草原云谷"核心项目，承载着乌兰察布从传统能源基地向数字经济高地转型的雄 心壮志。然而，就在项目推进至关键阶段时，项目方因设备采购与基建工程产生的巨额资金需求，让建 设工作遭遇 ...

Core Viewpoint - The article discusses the IOMM model introduced by the China Academy of Information and Communications Technology (CAICT) as a framework for assessing and guiding enterprises in their digital transformation journey, emphasizing the necessity of digital transformation in the current economy [1][8]. Group 1: Digital Transformation Trends - Digital transformation has shifted from an optional strategy to a mandatory requirement for enterprises due to the rapid integration of cloud computing, big data, and artificial intelligence [1][2]. - Policies have been implemented to encourage enterprises to adopt digital technologies, enhancing their operational processes and production methods [2][3]. Group 2: Nature and Path of Transformation - The essence of digital transformation lies in the comprehensive integration of digital technologies with business elements to optimize resource allocation and improve productivity [3][4]. - Different types of enterprises have distinct transformation paths; IT-driven companies often start with IT infrastructure, while non-IT-driven companies focus on core business processes [3][21]. Group 3: IOMM Model Overview - The IOMM model evaluates enterprises based on two dimensions: digital infrastructure service operation capability and overall digital operational capability [4][5]. - The model consists of five maturity stages for digital infrastructure, ranging from basic assurance to innovation leadership, providing a roadmap for enterprises to assess their current status and identify improvement areas [4][28]. Group 4: Practical Case Studies - Leading enterprises that participated in the IOMM assessment have demonstrated significant advancements in their digital transformation efforts, such as China Construction Bank and Alibaba Cloud, showcasing the effectiveness of the model [6][7]. Group 5: Conclusion and Future Directions - The adoption of the IOMM model marks a transition from chaotic growth to refined operations in digital transformation, emphasizing the importance of aligning digital technologies with value creation [8][26]. - Digital transformation is viewed as an ongoing process rather than a final goal, enabling enterprises to adapt and thrive in the digital economy [8][18].

Core Viewpoint - The "Digital Empowerment of New Consumption Forum" highlighted the integration of advanced technologies like AI and big data into various industries, emphasizing the shift from price sensitivity to value sensitivity in consumer behavior [3][5][11]. Group 1: Industry Insights - The forum discussed the transformation of marketing concepts in the context of digital empowerment, with a focus on creating transparent value models rather than merely selling products [3]. - The apparel industry is experiencing changes due to AI, which may replace certain technical roles but also necessitates new educational directions to enhance talent value in future jobs [5]. - The biopharmaceutical sector is leveraging AI to shorten the R&D cycle for new materials, exemplified by a company that has utilized AI to build a skin microbiome database [9]. Group 2: Company Strategies - Yalu Holdings is transitioning to a digital ecological service platform, restructuring its business model to integrate resources and enhance internal management functions [3]. - Daily Interactive has established a closed-loop business model driven by data, with 90% of its revenue coming from deep data mining and monetization [7]. - The cultural and creative industries are presented with new opportunities through digital communication, as companies can better understand user needs and develop attractive products [11].

Core Viewpoint - Liaoning Province has introduced the "Implementation Plan for Digital Government Construction (2025-2027)" to enhance government governance and service efficiency through digital technologies such as big data and artificial intelligence [1][2] Group 1: Implementation Plan Overview - The plan emphasizes a "whole province in one chessboard" approach, focusing on top-level design and overall promotion of digital government initiatives [2] - Key components include the establishment of a data governance system, data standard system, and the integration of data resources to enhance data sharing and utilization [2][3] Group 2: Infrastructure Development - Liaoning has established a "two places, three centers" provincial data center support system, with 699 information systems migrated to the cloud [3] - The electronic government extranet covers over 27,000 service points across various administrative levels, enhancing accessibility and service delivery [3] Group 3: Future Directions - The province plans to continue promoting cloud migration for non-sensitive government information systems and optimize the management of government cloud resources [4] - Future upgrades will focus on integrating specialized networks with the electronic government extranet, expanding bandwidth, and enhancing data transmission efficiency [4]

Group 1 - The Chinese government has approved the "Artificial Intelligence +" action plan, emphasizing the integration of AI into various sectors and the importance of government-led demonstration projects [1] - The policy shift indicates a transition from technology support to positioning AI as a core engine for economic transformation, with local initiatives like Beijing's "AI-native city" and Shenzhen's "smart driving pioneer" reflecting this trend [1] - Overseas, AI capital expenditures are being revised upwards, with Meta increasing its 2026 capital expenditure guidance to nearly $100 billion and Microsoft planning to invest over $30 billion in the next quarter, primarily targeting AI infrastructure [1] Group 2 - The rapid development of AI is intensifying competition for traffic entry points, with significant hardware investments driving further efficiency in AI applications [2] - Cloud service providers are increasing their investments in computing power, driven by the competition for massive traffic and advertising revenue, with a focus on AI models and intelligent agents to enhance customer engagement [2] - The Big Data ETF closely tracks the CSI Cloud Computing and Big Data Theme Index, which includes 50 companies involved in cloud computing and big data services, reflecting the overall performance of these sectors [2] Group 3 - As of July 31, 2025, the top ten weighted stocks in the CSI Cloud Computing and Big Data Theme Index account for 53.85% of the index, including companies like Zhongji Xuchuang and Keda Xunfei [3]

Group 1 - The seventh International Academic Conference on Artificial Intelligence, Computer Science, and Information Processing (AICSIP 2025) was held in Hangzhou, Zhejiang on July 25 [1] - A paper titled "Explaining The Improvement Of Multi-Exit Structure Distillation Using Stage Training And Attention Integration" was presented by a data intelligence technology company, attracting significant attention from both academia and the tech industry [1] - The research addresses the performance decline and inefficiency issues that arise when original AI models are distilled into smaller, less hardware-intensive models for enterprise applications, proposing a technique that enhances knowledge transfer efficiency during the distillation process [1] Group 2 - The technology has been successfully applied to a cloud-based hydropower model platform, resulting in a 52% reduction in computational consumption and a 40% increase in inference speed for the established runoff time series prediction model compared to pre-distillation [1] - This advancement significantly lowers the hardware resource requirements for AI applications in the hydropower industry, providing strong support for enhancing the intelligence level of hydropower applications [1] - The conference was sponsored by the IEEE China Council and co-hosted by Hangzhou Normal University and Hubei Zhongke Geological and Environmental Technology Service Center, with all accepted papers to be included in the IEEE Xplore core database and submitted for EI Compendex and Scopus indexing, highlighting the academic value and technological forefront of the research [2]

Group 1 - The National Internet Information Office of China has raised concerns regarding security risks associated with NVIDIA's H20 computing chip, prompting the company to provide explanations and proof materials [1] - Following the news, there has been increased market interest in domestic cloud computing and data security sectors, with significant stock price movements in related companies [1] - As of July 31, the Big Data ETF (159739.SZ) rose by 2.39%, and its associated cloud computing index increased by 2.27%, with notable gains in key component stocks such as Zhongji Xuchuang (+4.63%), Yonyou Network (+10.03%), and Kingsoft Office (+4.10%) [1] Group 2 - Northeast Securities highlighted Huawei's comprehensive layout from hardware to industry algorithms, with its Ascend 910C chip achieving FP16 computing power of 752 TFLOPS, supporting AI model training and inference [2] - The report also noted Huawei's collaborations with companies like Sinochem and Xingfa Group in smart manufacturing, demonstrating the practical benefits of AI models in optimizing chemical processes and reducing energy consumption [2] - Additionally, Meta and Google have increased their annual capital expenditure to $85 billion to strengthen AI infrastructure, indicating sustained high demand for global AI computing power and confirming growth momentum in the cloud computing industry [2]

Core Points - The CSI Cloud Computing and Big Data Theme Index rose by 2.26% to 5125.82 points, with a trading volume of 76.683 billion yuan [1] - The index has increased by 10.31% over the past month, 19.25% over the past three months, and 17.46% year-to-date [1] - The index consists of 50 listed companies involved in cloud computing services, big data services, and related hardware [1] Index Composition - The top ten weighted companies in the index are: iFlytek (8.6%), Zhongji Xuchuang (8.44%), Xinyi Sheng (8.35%), Kingsoft Office (5.33%), Zhongke Shuguang (4.95%), Hengsheng Electronics (4.34%), Inspur Information (4.31%), Unisplendour (3.81%), Runhe Software (3.29%), and Runze Technology (2.54%) [1] - The industry composition of the index shows that Information Technology accounts for 65.80%, Communication Services for 33.03%, and Industry for 1.17% [1] Index Adjustment - The index samples are adjusted semi-annually, with adjustments implemented on the next trading day after the second Friday of June and December [2] - Weight factors are generally fixed until the next scheduled adjustment, with temporary adjustments made under special circumstances [2] - Public funds tracking cloud computing include various ETFs and theme funds from different financial institutions [2]