目前煤矿冲击地压防治工作依托于对应力、能量、位移等数据的监测及人工分析，并据此实施针对性的防治措施。经过近百年不懈的研究与实践，该领域在理论深度、技术创新和装备升级等方面取得了显著进步。然而，在源头治理和智能防控的研究领域，仍面临诸多挑战与难题。在2023年中国科协十大产业技术问题评选中，专家组一致将“如何实现冲击地压煤层智能安全高效开采？”推荐入选，为煤矿冲击地压灾害防治的未来发展指明了方向。冲击地压煤层的智能开采不仅是一个技术难题，更是一个复杂的系统工程问题。基于40余年的理论积淀与工程实践，提出了煤层智能安全高效开采系统架构与关键技术。首先利用数字孪生技术构建精细的数字模型，深入探测和解析矿井所在区域的地质构造，精确测量区域地应力场特性和煤岩物理力学属性，并依托先进算法对地层变化信息进行工程展布，实现地质信息的“透明化”；其次，在数字模型中依据矿井开采设计方案，模拟井筒、巷道和工作面的开掘过程，同时结合井上、井下各类监测系统的实时数据，对矿井数字模型进行动态更新和计算，既能够回顾和分析历史工程效果，又能够预测和推演未来工程活动的影响；再次借助深度学习和AI技术让计算机自主学习矿井的全息数据，根据监测数据和工程活动的实时变化，自主决策开采和防治工程措施，同时构建标准化的通信技术体系，实现矿井内各类设备的互联互通，打造智能化的矿山生态系统；最后开发智能化的采煤装备控制模块，在计算机智能决策的指导下，实现对全矿井各类设备动作的智能控制。通过自主调节采煤和卸压解危的时序，优化采煤、运输、提升等设备的运行速度，全面实现冲击地压煤层的智能开采，为煤矿安全生产和高效运营提供有力保障。

At present, the prevention and control of rock burst in coal mines relies on the monitoring and manual analysis of stress, energy, displacement and other data, and implements targeted prevention measures accordingly. After nearly 100 years of unremitting research and practice, remarkable progress has been made in this field in terms of theoretical depth, technological innovation and equipment upgrading. However, in the field of source control and intelligent prevention and control, there are still many challenges and problems. In the selection of the top ten industrial and technological issues by the China Association for Science and Technology in 2023, the expert group unanimously selected “how to achieve intelligent, safe, and efficient mining of coal seams under rock pressure” as a key topic. It points out the direction for the future development of mine burst disaster prevention and control. The intelligent mining of rock burst coal seam is not only a technical problem, but also a complex system engineering problem. Based on more than 40 years of theoretical accumulation and engineering practice, the framework and key technologies of intelligent, safe and efficient coal seam mining system are proposed. First of all, digital twin technology is used to build a fine digital model, deeply detect and analyze the geological structure of the area where the mine is located, accurately measure the characteristics of the regional geostress field and the physical and mechanical properties of coal and rock, and rely on advanced algorithms to carry out engineering distribution of formation change information to achieve “transparency” of geological information. Secondly, in the digital model, the excavation process of shaft, roadway and working face is simulated according to the mine mining design scheme. At the same time, the real-time data of various monitoring systems in the well and underground are combined with the dynamic update and calculation of the mine digital model, which can not only review and analyze the historical engineering effects, but also predict and deduce the impact of future engineering activities. Thirdly, with the help of deep learning and AI technology, the computer can independently learn the holographic data of the mine, make independent decisions on mining and prevention engineering measures according to the real-time changes of monitoring data and engineering activities, and build a standardized communication technology system to realize the interconnection of various equipment in the mine and create an intelligent mine ecosystem. Finally, the intelligent mining equipment control module is developed. Under the guidance of computer intelligent decision, the intelligent control of all kinds of equipment in the whole mine is realized. By independently adjusting the timing of coal mining and pressure relief, optimizing the running speed of coal mining, transportation, lifting and other equipment, fully realizing the intelligent mining of rock burst coal seams, all these provide strong guarantee for the safe production and efficient operation of coal mines.