School of Artificial Intelligence, China University of Mining and Technology-Beijing
Key Laboratory of Intelligent Mining and Robotics of Emergency Management Department
Institute of Intelligent Mining and Robotics, China University of Mining and Technology-Beijing

受深部开采冲击地压等地质灾害扰动的影响，存在矿井超前支护系统自感知能力差、智能抗冲自适应能力弱、缺乏决策控制能力等问题。针对上述问题，提出了一种基于数字孪生和深度强化学习的矿井超前液压支架自适应抗冲支护方法。通过多源传感器感知巷道环境和超前液压支架支护状态，在虚拟世界中创建物理实体的数字孪生模型，其中物理模型精确展现超前液压支架的结构特征和细节，控制模型实现超前液压支架的自适应控制，机理模型实现对超前液压支架自适应支护的逻辑描述和机理解释，数据模型存储超前液压支架实体运行数据和孪生数据，仿真模型完成超前液压支架立柱仿真以实现超前液压支架与数字孪生模型虚实交互。根据基于深度Q网络（DQN）的超前液压支架自适应抗冲决策算法，对仿真环境中巷道抗冲支护进行智能决策，并依据决策结果对物理实体和数字孪生模型下达调控指令，实现超前液压支架智能控制。实验结果表明：立柱位移与压力变化一致，说明超前液压支架立柱仿真模型设计合理，从而验证了数字孪生模型的准确性；基于DQN的矿井超前液压支架自适应抗冲决策算法可通过调节液压支架控制器PID参数，自适应调控立柱压力，提升巷道安全等级，实现超前液压支架自适应抗冲支护。

Due to the disturbance of geological disasters such as deep mining and rock burst, there are problems such as poor self perception capability, weak intelligent anti impact adaptive capability, and lack of decision-making and control capability in the advanced support system of the mine. In order to solve the above problems, a adaptive impact resistance support method for advanced hydraulic supports in mines based on digital twins and deep reinforcement learning is proposed. By sensing the roadway environment and advanced hydraulic support status through multiple sensors, a digital twin model of a physical entity is created in a virtual world. The physical model accurately displays the structural features and details of the advanced hydraulic support. The control model realizes adaptive control of the advanced hydraulic support. The mechanism model realizes logical description and mechanism explanation of the adaptive support of the advanced hydraulic support. The data model stores the physical operation data and twin data of the advanced hydraulic support. The simulation model completes the simulation of the advanced hydraulic support column to achieve virtual real interaction between the advanced hydraulic support and the digital twin model. According to the adaptive impact resistance decision-making algorithm based on deep Q-network (DQN) for advanced hydraulic support, intelligent decision-making is made for roadway impact resistance support in the simulation environment. Based on the decision results, control instructions are issued to physical entities and digital twin models to achieve intelligent control of advanced hydraulic support. The experimental results show that the displacement and pressure changes of the column are consistent, indicating that the simulation model design of the advanced hydraulic support column is reasonable, thereby verifying the accuracy of the digital twin model. The adaptive impact resistance decision-making algorithm for advanced hydraulic supports in mines based on DQN can adjust the PID parameters of the hydraulic support controller, adaptively regulate the column pressure, improve the safety level of roadways, and achieve adaptive impact resistance support for advanced hydraulic supports.

国家重点研发计划项目（2022Z03010004）；国家自然科学基金面上项目（52374165）；国家自然科学基金创新研究群体项目（52121003）。

张帆，邵光耀，李昱翰，等. 基于数字孪生和深度强化学习的矿井超前液压支架自适应抗冲支护方法[J]. 工矿自动化，2024，50（6）：23-29, 45.

ZHANG Fan, SHAO Guangyao, LI Yuhan, et al. Adaptive impact resistance support method for advanced hydraulic supports in mines based on digital twins and deep reinforcement learning[J]. Journal of Mine Automation，2024，50（6）：23-29, 45.