工信厅信发函〔2023〕187号
各省、自治区、直辖市及计划单列市、新疆生产建设兵团工业和信息化主管部门（大数据产业主管部门），各有关单位：
为深入实施国家大数据战略，落实《“十四五”大数据产业发展规划》（工信部规〔2021〕179号），切实提升产业发展水平，加快培育数据要素市场，工业和信息化部将组织开展2023年大数据产业发展示范申报工作。现将有关事项通知如下：
工业和信息化部关于印发“十四五”大数据产业发展规划的通知
工信部规〔2021〕179号
各省、自治区、直辖市及计划单列市、新疆生产建设兵团工业和信息化主管部门（大数据产业主管部门），各省、自治区、直辖市通信管理局，有关中央企业，部属有关单位：
Digital 1\vin models are computerized clones of physical assets that can be used for in-depth analysis. Industrial production lines tend to have multiple sensors to generate near real-time status information for production. Industrial Internet of Things datasets are difficult to analyze and infer valuable insights such as points of failure, estimated overhead. etc. In this paper we introduce a simple way of formalizing knowledge as digital twin models coming from sensors in industrial production lines. We present a way on to extract and infer knowledge from large scale production line data, and enhance manufacturing process management with reasoning capabilities, by introducing a semantic query mechanism. Our system primarily utilizes a graph-based query language equivalent to conjunctive queries and has been enriched with inference rules.
igital twin (DT) is one of the most promising enabling technologies for realizing smart grids. Characterized by seamless and active—data-driven, real-time, and closed-loop—integration between digital and physical spaces, a DT is much more than a blueprint, simulation tool, or cyber-physical system (CPS). Numerous state-of-the-art technologies such as internet of things (IoT), 5G, big data, and artificial intelligence (AI) serve as a basis for DT. DT for power systems aims at situation awareness and virtual test to assist the decision-making on power grid operation and management under normal or urgent condi-tions. This paper, from both science paradigms and engineering practice, outlines the backgrounds, challenges, framework, tools, and possible directions of DT as a preliminary exploration. To our best knowledge, it is also the first exploration on DT in the context of power systems. Starting from the fundamental and most frequently used power flow (PF) analysis, some typical application scenarios are presented. Our work is expected to contribute some novel discoveries, as well as some high-dimensional analytics, to the engineering community. Besides, the connection of DT with big data analytics and AI may has deep impact on data science.
电力大数据“赋能业务、智引未来”，驱动泛在电力物联网和坚强智能电网深度融合，逐步形成多能互补、智能互动、泛在互联的能源互联网，实现以电力能源为中心，多种能源协同、供给与消费协同、集中式与分布式协同，大众广泛参与的智慧能源网络和能源生态系统。
针对煤矿安全生产信息化的发展及现状，总结“互联网+”背景下煤矿安全信息化的内涵，提出互联
网+煤矿安全信息化就是将物联网、云计算、大数据等新一代IT 创新变革技术及概念与煤炭工业深度融合，实现煤矿生产的安全、低耗及高效。同时，从煤矿物联网、煤矿云计算、煤矿大数据三方面分析互联网+煤矿安全信息化的关键技术，并在此基础上提出了由信息感知层、数据传输层、云计算资源层、应用服务层、用户接口层组成的互联网+煤矿安全信息化应用构架及专有云构建模式，使煤矿物联网产生的大数据在弹性可扩展的云计算平台支撑下，为煤矿防灾、减灾，改善煤矿安全现状提供高效、科学的决策依据与技术手段。最后分析了煤矿安全信息化在互联网+背景下面临的机遇与挑战。
针对煤矿建设过程中存在的信息孤岛、子系统割裂等问题ꎬ提出了以“自主感知与控制－信息
传输系统一操作平台－井下系统平台－生产经营管控平台” 为主线的智慧煤矿建设主体架构ꎬ通过底
层的自主感知系统对井下的“人－机－环－管” 信息进行全面感知ꎬ并对感知信息的数据格式、通信协
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