为解决现有研究中难以通过随钻获取孔底钻进参数，仅利用孔口数据进行回转钻进操作参数优化准确性不高、提升效果不足的问题，通过构造孔内状态观测器估计孔底钻进参数信息，提出基于钻柱状态的坑道近水平回转钻进智能优化方法。首先分析煤矿井下坑道回转钻进特性，考虑实钻约束条件，提出机械钻速和钻头磨损的优化目标评价方法；随后建立轴向和扭转维度的集中质量钻柱动力学模型，构建基于该模型的钻柱状态空间方程，得到了孔口−孔底钻柱运动状态映射关系；基于此设计了状态观测器，利用李雅普诺夫稳定性分析方法，得到反馈增益矩阵L，以估计孔底钻头的运动状态，并进行仿真分析评价；最后综合孔口采集的数据和孔底状态估计，运用NSGA-II多目标优化算法实现了动力头转速和给进压力的优化，并利用安徽淮南某煤矿实钻数据进行了验证。结果表明，基于钻柱状态估计孔底信息进行优化后的钻速和司钻操作相比预计提升32.47%，仅利用孔口实测数据优化后的钻速仅预计提升15.04%，基于钻柱状态估计的坑道回转钻进智能优化方法更具优势，孔底估计钻进信息对提升钻进水平具有关键作用，研究对煤矿坑道回转钻孔实现高效、智能钻进具有重要理论与实际意义。

To address the issue in existing research where it is difficult to obtain bottom-hole drilling parameters while drilling, and to overcome the problem of low accuracy and insufficient improvement when optimizing rotary drilling operational parameters using only borehole data, an intelligent optimization method for near-horizontal tunnel rotary drilling based on drill string status is proposed. This involves constructing an in-hole state observer to estimate bottom-hole drilling parameter information. Firstly, the characteristics of rotary drilling in underground coal mine tunnels are analyzed, considering the constraints of actual drilling, and an optimization target evaluation method for mechanical drilling speed and drill bit wear is proposed. Subsequently, a concentrated mass drill string dynamics model in axial and torsional dimensions is established, and a drill string state space equation based on this model is constructed, yielding a mapping relationship between the borehole and bottom-hole drill string motion states. Based on this, a state observer is designed, using Lyapunov stability analysis method to obtain the feedback gain matrix L, to estimate the motion state of the bottom-hole drill bit, and conduct simulation analysis and evaluation. Finally, by combining data collected from the borehole and estimated bottom-hole state, the optimization of the power head speed and feed pressure was achieved using the NSGA-II multi-objective optimization algorithm, and verified with actual drilling data from a coal mine in Huainan, Anhui. The results show that the drilling speed and driller operation improved by 32.47% after optimization based on drill string state estimation of bottom-hole information, compared to an estimated 15.04% improvement using only borehole measured data. Thus, the intelligent optimization method for tunnel rotary drilling based on drill string state estimation has more advantages, and the estimated bottom-hole drilling information plays a key role in improving the level of drilling. This research has significant theoretical and practical significance for achieving efficient and intelligent drilling in coal mine tunnel rotary drilling.