煤矿井下智能钻机发展与展望

Development and Prospects of Intelligent Drilling Rigs in Underground Coal Mines

  • 摘要:
    背景 煤矿井下智能钻机是瓦斯抽采、水害防治的核心装备。随着开采深度增加,高瓦斯、高地应力等极端工况对传统模式提出挑战。
    进展 系统回顾了我国在“十三五”至“十四五”期间智能钻机的发展历程,并将其划分为3个阶段:半自动化阶段已突破可视距离遥控与自动加卸钻杆技术,实现了人机分离与安全作业,效率较人工操作提升约30%;全流程自动化阶段正在攻克自适应钻进与自主决策算法,使钻机具备基于实时地质信息动态调整钻进策略的能力,同时打通钻孔施工、钻渣清运、筛管下放与封孔注浆等工序链条,通过多机协同控制实现一键启停自动化作业,辅助时间占比降至20%以下;智能化初期阶段正在尝试引入地面远程集控与精准定向导向技术,赋予钻机初步的“感知−分析−决策”能力,将目标层钻遇率提升至90%以上、轨迹偏差控制在1 m以内。尽管技术发展取得显著进展,但智能化进程仍面临地质感知建模精度不足、智能算法自适应能力偏弱、核心执行部件可靠性偏低、跨设备数据互联互通存在壁垒等问题。
    展望 未来,智能钻机将在透明地质保障、AI算法深度融合及技术标准统一等重点方向寻求突破,朝着系统级智能自主决策、钻探机器人集群无人化作业与电驱执行数字化方向持续演进,为煤炭工业的高质量与可持续发展提供关键技术支撑。

     

    Abstract:
    Background Intelligent drilling rigs in coal mines is a critical equipment underpinning the safe and efficient exploitation of coal resources and the intelligent transformation of mining operations.
    Progress  This paper systematically reviews the evolution of this technology in China from the “13th Five-Year Plan” to the “14th Five-Year Plan” period, categorizing its development into three distinct stages. In the semi-automatic stage, breakthroughs in remote control and automatic drill pipe handling technologies were achieved, realizing man-machine separation and safe operation, and improving drilling efficiency by approximately 25% over manual operations. The full-process automation stage is currently making breakthroughs in adaptive drilling and autonomous decision-making algorithms, enabling the rig to dynamically adjust drilling strategies based on real-time geological information. Meanwhile, it has connected the closed-loop processes of drilling, slag removal, screen pipe installation and grouting, realizing one-button start-stop automated operation through multi-machine cooperative control, and reducing auxiliary operation time to below 20%. The early intelligent stage is attempting to introduce surface remote centralized control and precise directional steering technologies, endowing the rig with preliminary “perception-analysis-decision” capabilities, increasing the target-layer penetration rate to over 90% and controlling trajectory deviation to within 1 m. Despite notable progress, the transition toward higher-level intelligence still faces challenges such as insufficient accuracy in geological modeling, limited adaptability of intelligent algorithms, low reliability of core components, and barriers in data interconnectivity.
    Prospects  Future developments are expected to seek breakthroughs in key areas such as transparent geology modeling, deep integration of AI algorithms, and standardization of technical protocols. The technology will continue to evolve toward system-level autonomous decision-making, unmanned operations via robotic drilling clusters, and electric drive-based digital execution, thereby providing crucial technical support for the high-quality and sustainable development of the coal industry.

     

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