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金属矿山 ›› 2025, Vol. 54 ›› Issue (11): 242-249.

• 安全与环保 • 上一篇    下一篇

面向生态监管的矿区生态环境知识图谱构建及应用

王行风1,2 陈国良1,2   

  1. 1.中国矿业大学环境与测绘学院,江苏 徐州 221116;2.自然资源部国土环境与灾害监测重点实验室,江苏 徐州 221116
  • 出版日期:2025-11-15 发布日期:2025-12-02
  • 作者简介:王行风(1972—),男,副教授,博士,硕士研究生导师。
  • 基金资助:
    深地国家科技重大专项(编号:2024ZD1004101);国家自然科学基金项目(编号:42274048)。

 Construction and Application of Ecological Environment Knowledge Graph for Ecological Supervision in Mining Area

WANG Xingfeng1,2 CHEN Guoliang1,2   

  1. 1.School of Environment Science and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,China; 2.Key Laboratory of Land Environment and Disaster Monitoring,Ministry of Natural Resources,Xuzhou 221116,China
  • Online:2025-11-15 Published:2025-12-02

摘要: 以“大数据、大模型”为代表的新一代智能化信息技术的应用,为生态环境研究提供了新的视角。构建 以“人工智能”为核心的生态环境监管新模式已成为现代生态监管研究的重要课题。矿区生态环境“轻发现、重事后、 强评估、弱预警”的传统监管分析方式难以满足管理部门对精准识别、快速高效以及提前发现的迫切需求。为此,提 出了面向生态监管的矿区生态环境知识图谱(Knowledge Graph of Ecological Environment in Mining Area,KGEE-MA)构 建框架,分析了协同感知、认知学习、图谱表达和智慧应用等关键环节,定义了扰动源、生态要素、采动灾害、感知数据、 分析指标以及评估模型本体的概念层次关系、语义关系以及时空关系。以永夏矿区为例,对所提技术与方法进行了 应用验证研究。结果表明:① KGME-MA有助于识别采动扰动影响的敏感生态因子、生态孕险因子,提前发现资源采 动过程中的可疑生态靶点、潜在孕灾区以及生态扰动剧烈区;② 生态环境知识图谱可为矿区这一特殊空间场景的生 态损伤的智能推理、提前发现和精准识别提供一定的支持。上述研究反映出,所构建的矿区生态环境知识图谱可为 生态环境“智慧监管”提供新的技术手段,为解决现代生态治理问题提供新思路,可丰富矿山生态演变和生态修复理 论,对于指导矿区生态分析评价、生态综合治理以及生态复垦等具有一定的参考价值。

关键词: 矿区生态环境 知识图谱 智慧识别 智慧监管 主动发现

Abstract: The application of new generation information technologies represented by "big data and big model" provide a new perspective for ecological research.Building a new model of ecological environment supervision characterized by "artificial intelligence" has become an important topic in modern ecological supervision research.The traditional analysis method of "light discovery,heavy post monitoring,strong assessment,and weak warning" for the ecological environment of mining areas is difficult to meet the practical needs of management departments for accurate identification,rapid and efficient detection,and early detection.To meet the practical needs of intelligent recognition and active discovery of the ecological environment status in mining areas,a framework for constructing a Knowledge Graph of Ecological Environment in Mining Area (KGEE-MA) for ecological supervision was proposed.The key technologies of collaborative perception,cognitive learning,graph expression,and intelligent application were analyzed,and the conceptual hierarchy,semantic relationship,and spatiotemporal relationship of disturbance sources,ecological elements,mining disasters,perception data,analysis indicators,and evaluation model ontology were defined.Taking the Yongxia mining area as an example,the proposed technical framework was applied and verified,and the results showed that:① KGME-MA can help identify sensitive ecological factors and ecological risk factors affected by min ing disturbance,and detect suspicious ecological targets,potential disaster areas,and areas with severe ecological disturbance in advance during resource mining;② KGME-MA can provide certain support for intelligent reasoning,early detection,and accu rate identification of ecological damage in the cognitive field of mining areas,a special spatial scenario.The above research re flects that the ecological environment knowledge graph of mining areas constructed in this article can provide new technical means for "smart supervision" of the ecological environment,provide new ideas for solving modern ecological governance problems,enrich the theory of mining ecological evolution and ecological restoration,and have certain reference value for guiding ecological analysis and evaluation,ecological comprehensive management,and ecological reclamation in mining areas.

Key words: ecological environment in mining area,knowledge graph,intelligence identification,smart supervision,active discovery

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