Core Viewpoint - The event focused on the construction of data infrastructure and its role in supporting the digital economy and technological innovation, emphasizing the need for a new infrastructure system that prioritizes data value release, network support, and computing power as a foundation [1][14]. Group 1: Event Overview - The "Data Infrastructure Theme Exchange Activity" was held in Guiyang, gathering representatives from government, industry, academia, and research to discuss the construction path of data infrastructure and the national integrated computing power network [1]. - Key officials, including Liu Liehong, the head of the National Data Bureau, highlighted the goal of creating a new infrastructure system aimed at high-quality standards, large-scale facility deployment, and market-oriented ecological operations [1]. Group 2: Regional Economic Performance - In the first half of 2025, Guizhou's GDP grew by 5.3%, with 49 key data centers under construction or in operation, achieving over 90% in intelligent computing and domestic production rates [2]. Group 3: Technological Innovations - Chen Jianfeng from the Chinese Academy of Engineering discussed innovations in green data centers, projecting that China's total computing power electricity consumption will exceed 300 billion kWh by 2024, with a significant reduction in energy consumption through new cooling technologies [3]. - The "cloud-edge computing" concept proposed by Wang Huaimin aims to connect dispersed computing resources through a collaborative mechanism, enhancing operational efficiency across over 900 computing centers [4]. Group 4: Industry Insights - Yu Xiaohui from the China Academy of Information and Communications Technology emphasized the need to address connectivity and circulation issues in data infrastructure, with the National Data Bureau's "three unifications" laying the groundwork for data connection [6]. - Xu Qiang from the National Information Center proposed a "three fives" strategy for computing power network construction, focusing on inclusive computing, green development, and addressing supply and service efficiency challenges [7]. Group 5: Monitoring and Evaluation - Hu Jianbo from the National Data Development Research Institute introduced a monitoring system with "four dimensions, three levels, and 60 indicators" to ensure effective management and utilization of data infrastructure [9]. Group 6: Local Practices and Achievements - Jiang Yang, Deputy Director of Guizhou Data Bureau, shared five initiatives that led to the establishment of 49 data centers and significant improvements in computing power and latency [11]. - Wang Wanjun from Jiangsu Data Bureau reported that the province's computing power reached 61.54 Eflops, with the digital economy's core industry contributing 11.8% to GDP [12]. Group 7: Key Outcomes - The event announced three significant outcomes: the establishment of a national integrated computing power network monitoring and scheduling platform, the second batch of pilot projects for national data infrastructure, and successful case studies demonstrating cross-domain integration [13][14].

Core Viewpoint - The National Data Bureau of China has launched the "National Integrated Computing Power Network Monitoring and Scheduling Experimental Verification Platform" at the 2025 China International Big Data Industry Expo, aimed at integrating dispersed computing power across the country to provide more accessible computing services [1] Group 1: Platform Overview - The platform has achieved initial monitoring and scheduling of eight major hubs and ten clusters under the "East Data West Computing" initiative, covering over 900 computing facilities nationwide [1] - The platform facilitates the integration of domestic computing power, idle computing power, and green computing power, significantly reducing rendering tasks for projects like the movie "Nezha 2" from 600 years to a few months [1] Group 2: Cost Implications - According to representative Lu Hongjun, computing power costs are crucial for small and medium-sized enterprises transitioning to "data + artificial intelligence" [1] - The platform is expected to reduce training costs by 25% and inference costs by 10% [1]