地学前缘 ›› 2025, Vol. 32 ›› Issue (5): 484-492.DOI: 10.13745/j.esf.sf.2025.4.69

• 资源战略仿真 • 上一篇    下一篇

基于时空大数据的矿产资源产业链平台构建与智能分析研究

刘超1,2(), 赵汀1,2,*(), 王安建1,2, 代涛1,2, 闫强1,2, 杨振山3, 王永志4   

  1. 1.中国地质科学院矿产资源研究所 自然资源部成矿作用与资源评价重点实验室, 北京 100037
    2.中国地质科学院, 北京 100037
    3.中国科学院地理科学与资源研究所, 北京 100101
    4.吉林大学 地球探测科学与技术学院, 吉林 长春 130061
  • 收稿日期:2025-05-15 修回日期:2025-09-12 出版日期:2025-09-25 发布日期:2025-10-14
  • 通信作者: 赵汀
  • 作者简介:刘 超(1986—),女,博士研究生,高级工程师,研究方向为矿产资源战略研究及信息化。E-mail: 1971076064@qq.com
  • 基金资助:
    深地国家科技重大专项(2025ZD1007005);深地国家科技重大专项(2025ZD1007000);深地国家科技重大专项(2025ZD1007904);深地国家科技重大专项(2025ZD1007900);中国地质调查局地质调查项目“全球矿产资源储量动态评估(DD20230564)”

Construction and intelligent analysis research of mineral resource industry chain platform based on spatiotemporal big data

LIU Chao1,2(), ZHAO Ting1,2,*(), WANG Anjian1,2, DAI Tao1,2, YAN Qiang1,2, YANG Zhenshan3, WANG Yongzhi4   

  1. 1. MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
    2. Chinese Academy of Geological Sciences, Beijing 100037, China
    3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    4. College of Geo-exploration Science and Technology, Jilin University, Changchun 130061, China
  • Received:2025-05-15 Revised:2025-09-12 Online:2025-09-25 Published:2025-10-14
  • Contact: ZHAO Ting

摘要:

本文针对我国作为全球矿产资源消费与贸易大国所面临的供应链管理信息化问题,提出构建一个基于时空大数据的矿产资源产业链智能分析平台,并以铁矿石为例展开实证研究。我国矿产资源需求持续高位运行,但国内供应波动、国际市场环境多变等因素加剧供应链风险,亟需通过智能技术提升产业链协同管理与风险应对能力。本文系统阐述了平台构建的理论基础与技术路径,集成多源异构数据,结合空间分析、时间序列分析及时空联动方法,建立涵盖“数据融合—机制解析—方案输出”的全流程分析模型。平台引入人工智能、云计算、复杂网络和遥感等技术,支持多情景仿真与动态预警,具备应对市场价格波动等突发情况的能力。实证部分通过模拟铁矿石价格大幅上涨情境,验证了平台在影响评估与策略生成方面的有效性。最后,本文展望未来研究方向,包括拓展至有色金属与能源矿产领域、优化算法模型,以及响应新能源转型对资源需求结构的影响。该平台为提升国家矿产资源安全保障和决策科学化提供了重要技术支撑。

关键词: 时空大数据, 矿产资源产业链, 智能分析平台, 铁矿石

Abstract:

China’s demand for mineral resources remains persistently high; however, factors such as fluctuations in domestic supply and the volatile international market environment have exacerbated supply chain risks. It is therefore urgent to enhance the collaborative management of the industrial chain and risk response capabilities through intelligent technologies.This paper systematically elaborates on the theoretical foundation and technical pathways for building the platform. It integrates multi-source heterogeneous data, combines spatial analysis, time series analysis, and spatiotemporal linkage methods, and establishes a full-process analysis model covering “data fusion-mechanism analysis-solution output”. The platform incorporates technologies including artificial intelligence, cloud computing, complex networks, and remote sensing, supports multi-scenario simulation and dynamic early warning, and is capable of responding to emergencies such as market price fluctuations.In the empirical section, by simulating the scenario of a sharp surge in iron ore prices, the effectiveness of the platform in impact assessment and strategy formulation is verified. Finally, the paper looks ahead to future research directions, which include expanding the application to the fields of non-ferrous metals and energy minerals, optimizing algorithm models, and responding to the impact of the new energy transition on the resource demand structure.This platform provides crucial technical support for enhancing national mineral resource security guarantees and the scientific nature of decision-making.

Key words: spatio-temporal big data, mineral resource industry chain, intelligent analysis platform, iron ore

中图分类号: