（来源：经济参考报） 促进数据应用创新。《实施意见》明确，要深化行业协同与跨行业数据融合，深入挖掘典型场景，促进 公共数据资源多场景应用、多主体复用；夯实交通运输数据流通利用设施底座，强化高质量算力供给， 建设一批行业高质量数据集；加强公共数据供需精准对接，探索建立交通运输数据要素型企业培育机 制，繁荣产业发展生态圈。 强化数据安全保障。《实施意见》提出要同步推动制度建设与能力建设，强化数据安全保障体系建设， 加强数据分类分级管理、加大公共数据各环节安全技术支撑，积极有效防范和化解各种数据安全风险。 《实施意见》强调，未来将通过强化资金支持力度、完善行业数据管理制度、推进数据管理与应用创新 等政策层面的支持，进一步加快交通运输公共数据资源高效开发利用，持续提升行业治理能力和服务水 平。 建立高质量数据资源体系方面，《实施意见》提出构建覆盖分层级、多运输方式的交通运输公共数据资 源目录体系，加强公共数据资源采集与归集，强化公共数据源头治理，推动行业主数据、资源库部省两 级共建共用，全面提升公共数据质量。 强化公共数据供给方面，《实施意见》提出要从共享、开放、授权运营三方面发力，通过持续深化政务 数据共享机制与应 ...

当京杭运河上的船队穿梭于"北煤南运"黄金通道，济宁港正以一场数字化革命改写内河航运的历史。全 省内河首家供应链平台"融汇数易"的落地运行，不仅为这座新晋亿吨大港注入技术动能，更助推其向千 亿级大宗货物交易中心加速迈进。 齐鲁晚报·齐鲁壹点 孔茜 通讯员 臧盛博 支撑这一生态的平台采用"1+3+5N"的架构，深度整合大宗商品交易、多式联运、港口系统及供应链金 融系统，构建全链路数字化体系，依托控制中心实现业务数据、单据数据与风险管控一体化闭环。 "数据安全方面，我们建立了'传输-存储-使用'全链路安全保障体系：数据传输采用加密技术，存储层面 有多重备份和灾备机制，使用过程中设置了严格的权限分级管理，只有授权人员才能查看相关数 据。"融汇物产物流事业部调度中心平台主任臧健介绍，与此同时，平台也通过了国家信息安全等级保 护等多项合规认证，确保客户的交易数据、资金信息、物流轨迹等核心信息绝对安全。 后续的迭代优化，将主要聚焦深化AI赋能、拓展场景适配、强化生态协同三个方向。深化AI赋能上， 将引入智能调度算法，根据港口作业效率、路况等因素，自动优化物流路线和作业计划。拓展场景适配 上，针对不同品类大宗商品的运输特点， ...

Core Insights - The integration of artificial intelligence (AI) in education is transforming talent cultivation, scientific research, and campus governance, becoming a key support for the digital transformation of higher education institutions [1] - AI consists of three core elements: data, algorithms, and computing power, with data being a fundamental resource that significantly influences the effectiveness of AI models in educational applications [1] Group 1: Human-Machine Collaboration - The structure of educational data governance is shifting from a binary relationship of "teacher-student" to a triadic collaboration of "teacher-student-machine," enhancing the role of AI in data recognition, processing, and application [2] - Traditional educational data governance primarily relies on result-oriented data from various business systems, lacking sufficient collection of process-oriented data that reflects teaching activities [2] - Higher education institutions should leverage AI's capabilities in data mining and intelligent feedback to enhance the collection of process-oriented data, thereby enriching educational data resources [2] Group 2: Precision Improvement in Data Quality - Traditional data governance relies heavily on manual management, which can lead to inefficiencies and inaccuracies, making it difficult to identify and rectify data quality issues [3] - Institutions can utilize general large models to create intelligent data governance agents that autonomously perceive, decide, and execute data governance tasks, ensuring data accuracy and completeness [3] - Implementing a proactive data quality monitoring mechanism can shift data governance from reactive remediation to proactive prevention, thereby continuously improving data quality [3] Group 3: Enhancing Data Value through Intelligent Applications - The primary goals of educational data governance are to improve data quality, ensure data security, and extract data value, transitioning from merely solving problems to actively mining value [4] - Institutions should integrate technologies like natural language processing and data mining into the data governance process to facilitate intelligent data collection, cleaning, and classification [4] - By analyzing behavioral data and individual characteristics, institutions can create precise profiles for teachers and students, providing personalized support and unlocking deeper data value [4] Group 4: Establishing a Regulatory Framework for Data Security - The rise of AI in educational data governance presents challenges such as data ethics, privacy risks, and potential data manipulation, necessitating a comprehensive regulatory framework [5][6] - Institutions must establish guidelines for the collection, processing, and usage of sensitive data, ensuring compliance with legal and ethical standards [6] - Implementing encryption and access control measures during data usage can help prevent the spread of erroneous or false information, thereby safeguarding educational data security [6] Group 5: Strategic Response to AI Integration - The deep integration of AI in education not only empowers data governance but also imposes new requirements on institutions to optimize processes and reconstruct governance elements [7] - Institutions are encouraged to seize opportunities and scientifically address challenges by applying intelligent technologies to maximize the inherent value of educational data [7]

Group 1 - The core viewpoint of the article is that Guangdong has introduced policies to promote the high-quality development of the online gaming industry, focusing on technological advancements and collaboration with educational institutions [1] Group 2 - The policies encourage enterprises to conduct research and development around "bottleneck" technologies in the gaming sector, particularly in areas such as virtual engine development and data security [1] - Support is provided for the application of cutting-edge technologies like artificial intelligence in game development and the transformation of advanced gaming technologies to other fields [1] - The provincial government will allocate funds to support high-quality projects that achieve significant breakthroughs in gaming technology, with a maximum one-time subsidy of 5 million yuan available [1]