针对煤矿综采工作面甲烷监测点少导致工作面区域难以实现甲烷浓度监测高覆盖问题，提出了一种基于稀疏群智感知的工作面甲烷浓度场感知方法，通过部分子区域甲烷浓度监测数据，探索子区域相关性，并推断形成综采工作面甲烷浓度场。在对综采工作面进行子区域划分的基础上，首先，提出了基于分布加权自注意力机制的深度强化学习（DSA-DQN）甲烷浓度感知子区域优选算法，区域优选算法借助分布加权自注意力机制捕捉工作面环境的关键信息，以辅助基于深度强化学习的感知区域优选算法优化与决策；其次，在未感知区域甲烷浓度推断方面，提出了基于粒子群优化的支持向量回归克里金插值（PS-Kriging）甲烷浓度推断方法，将支持向量回归与克里金插值法相结合，解决传统方法在插值过程中可能存在陷入局部最优解的问题，使用粒子群优化改进支持向量回归模型的核参数和惩罚参数，提高了模型的推断精度；最后，选择山西某矿综采工作面，通过现场测量和仿真模拟相结合的方式进行验证。试验结果表明：提出的工作面甲烷浓度场稀疏群智感知方法，通过优选出部分关键子区域对工作面全部区域甲烷浓度进行推断，平均绝对误差为0.07%。

In view of the lack of methane monitoring points in coal mine fully mechanized mining face, it is difficult to achieve for high coverage methane concentration monitoring in the mining face area. We propose a methane concentration field sensing method in the mining face based on sparse crowd sensing. Through the monitoring data of methane concentration in some sub-cells, the correlation of sub-cells is explored, and the methane concentration field of fully mechanized mining face is inferred. Firstly, based on the sub-cell division of fully mechanized mining face, we propose a deep reinforcement learning based on distributed weighted self-attention mechanism (DSA-DQN) methane concentration sensing cell optimization selection algorithm. The cell optimization selection algorithm uses the distributed weighted self-attention mechanism (DSA) to capture the key information of the mining face environment, to assist the optimization and decision-making of the cell optimization selection algorithm based on DQN. Secondly, in the aspect of methane concentration inference in the unsensing area, a Kriging interpolation based on particle swarm optimization support vector regression (PS-Kriging) methane concentration inference method is proposed. The support vector regression is combined with the Kriging interpolation method to solve the problem that the traditional method fall into the local optimal solution in the interpolation process. Particle swarm optimization is used to improve the kernel parameters and penalty parameters of the support vector regression model, which improves the inference accuracy of the model. Finally, we choosed a fully mechanized mining face in a Shanxi province mine, and made experimental verification through the combination of on-site measurement and simulation experiments. The experimental results show that the sparse crowd sensing method can infer the methane concentration in all cells of the mining face by selecting some key sub cells, with an mean absolute error of 0.07%.