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可信数据空间发展联盟发布成员名单 神州控股、神旗数码等公司入选
智通财经网· 2025-05-28 03:21
Group 1 - The Trusted Data Space Development Alliance was established on April 29, 2025, to promote the construction of a national integrated data market and accelerate the practical promotion of trusted data space [1] - Digital China Holdings Limited and its subsidiary, Shenqi Digital Co., Ltd., were approved as members of the alliance, recognizing their achievements in data security, trusted circulation, and digital transformation [1][2] - The alliance aims to focus on industry pain points and practical needs, promoting the development of key results and cultivating significant benchmarks in trusted data space [1] Group 2 - Digital China Holdings has a unique first-mover advantage in the data element field, with its Yanyun DaaS product covering the entire data lifecycle [2] - The company has launched Yanyun Infinity in collaboration with Peking University, creating a value loop from "Data for AI" to "AI for Process" [2] - The company's trusted data space solutions have been applied in various sectors, including government, supply chain, and finance, and are actively promoting the establishment of trusted data space standards [2] Group 3 - The company initiated a logistics data open interconnection initiative with Beijing Jiaotong University and China Data Union Logistics to accelerate the release of data element value in the logistics sector [3] - Trusted data space is seen as a key lever for the marketization and valuation of data elements, providing a safer and more efficient infrastructure for market allocation [3] - Membership in the Trusted Data Space Development Alliance offers opportunities for expanding the data element market, enhancing ecological cooperation, and promoting industry standard formulation [3]
标杆引领 神旗数码参编大数据国标发布并加入人工智能标委会工作组
Zhi Tong Cai Jing· 2025-05-14 02:20
Core Viewpoint - The recent release of two national standards in the field of ecological environment big data marks a significant step in establishing systematic technical specifications for ecological environment big data in China, with the company playing a key role in their development [1][3]. Group 1: National Standards and Company Involvement - The national standards titled "Classification and Codes of Ecological Environment Big Data" (GB/T45408-2025) and "Framework of Ecological Environment Big Data System" (GB/T45397-2025) will be implemented starting October 1, 2025 [1]. - The company participated in the formulation of these standards, leveraging its advanced technological capabilities and successful practices in the ecological environment sector [1][3]. - The company has also become a member of the first standardization technical committee for artificial intelligence established by the Ministry of Industry and Information Technology, covering multiple working groups [1]. Group 2: Company’s Technological Capabilities and Solutions - The company, as a brand enterprise of Shenzhou Holdings in the data intelligence field, has led or participated in the development of 184 national and industry standards, with 92 approved and published [2]. - The company has developed the Yanyun Infinity intelligent software platform, which integrates data fusion and intelligent applications, providing end-to-end services across the data lifecycle [2]. - The company’s core technology enables seamless access to various types and modalities of data, facilitating rapid construction of digital twins through a no-code business modeling platform [2]. Group 3: Focus on Ecological Environment and Industry Impact - The ecological environment remains a key focus for the company, which aims to support the construction of a resource-saving and environmentally friendly society through intelligent solutions [3]. - Successful projects include the smart environmental protection system for the Beijing Expo and Winter Olympics, as well as smart water management projects in Jilin Province [3]. - The company is actively applying its data intelligence technology in supply chain and retail sectors, aiming to enhance efficiency and reduce costs through deep data value extraction [3].