基于文献计量的智慧矿山研究热点与趋势分析

    Research hotspots and trends analysis in smart mining based on bibliometrics

    • 摘要: 在全球矿业数字化转型背景下,智慧矿山研究成为推动我国矿山行业高质量发展的重要路径。本文以中国知网2014—2024年364篇核心期刊文献为样本,运用CiteSpace工具系统分析国内智慧矿山研究的演进脉络与前沿动态。研究发现:研究历程历经“基础探索-技术突破-生态构建”三阶段跃迁,早期聚焦数字矿山理论与信息化架构,中期深化智能装备与自动化技术研发,当前转向5G、AI与云计算的集成创新;研究热点呈现“基础技术-核心应用-新兴融合”三层结构,数字孪生、三维建模、智能管控系统等持续主导,5G通信、边缘计算等技术研究增速显著;研究趋势体现技术集成化、场景全流程化及可持续发展三大转向,逐步形成“感知-分析-决策-执行”闭环生态系统。结果表明,智慧矿山研究已进入跨学科协同创新阶段,未来需强化智能决策算法优化、异构数据融合治理及人机协同模式探索,同时推进数据治理标准化与绿色开采技术融合,以系统性创新驱动矿业从“经验驱动”向“数据驱动”转型。本文为学界把握研究动态、产业界制定智能化战略提供理论参照,助力我国矿业实现安全、高效、绿色的全面升级。

       

      Abstract: In the context of the global digital transformation of the mining industry, smart mining research has emerged as a critical pathway to achieve high-quality development in China’s mining sector. This paper systematically analyzes the evolutionary trajectory and frontier trends of domestic smart mining research by utilizing 364 core journal articles indexed in the China National Knowledge Infrastructure (CNKI) from 2014 to 2024, with CiteSpace-based visualization analysis. The findings reveal three key insights. The research progression follows a three-phase transition: “foundational exploration, technological breakthroughs, and ecosystem construction”, initially focusing on digital mine theories and information architectures, later advancing intelligent equipment and automation technologies, and currently shifting toward integrated innovations combining 5G, AI, and cloud computing. The research hotspots exhibit a hierarchical structure encompassing “foundational technologies, core applications, and emerging convergences”, where digital twins, 3D geological modeling, and intelligent control systems remain dominant, while emerging topics such as 5G communication, edge computing, and heterogeneous data fusion show accelerated growth. The research trends highlight three paradigm shifts: from single-technology development to integrated innovation, from localized automation to full-process intelligent management, and from production-centric approaches to sustainable ecosystems. A closed-loop “perception-analysis-decision-execution” framework is evolving as the core of smart mining ecosystems. The results demonstrate that smart mining research has entered an interdisciplinary innovation phase, necessitating advancements in intelligent decision-making algorithms, human-machine collaboration, and data governance standardization. Future efforts should prioritize green mining technologies and autonomous intelligent equipment to drive the industry’s transition from “experience-driven” to “data-driven” practices. This paper provides theoretical insights for academia and industry to navigate technological priorities and foster safe, efficient, and ecologically sustainable mining transformation.

       

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