Horizon control, maintaining the alignment of the shearer’s exploitation gradient with the coal seam gradient, is a key technique in longwall mining automation. To identify the coal seam gradient, a geological model of the coal seam was constructed using in-seam seismic surveying technology. By synthesizing the control resolution of the range arm and the geometric characteristics of the coal seam, a gradient identification method based on piecewise linear representation (PLR) is proposed. To achieve the maximum exploitation rate within the shearer’s capacity, the control resolution of the range arm is selected as the threshold parameter of PLR. The control resolution significantly influenced the number of line segments and the fitting error. With the decrease of the control resolution from 0.01 to 0.02 m, the number of line segments decreased from 65 to 15, which was beneficial to horizon control. However, the average fitting error increased from 0.055 to 0.14 m, which would induce a decrease in the exploitation rate. To avoid significant deviation between the cutting range and the coal seam, the control resolution of the range arm must be lower than 0.02 m. In a field test, the automated horizon control of the longwall face was realized by coal seam gradient identification.

水平控制，保持采煤机开采坡度与煤层坡度一致，是长壁开采自动化的关键技术。为识别煤层梯度，利用煤层内地震勘测技术构建了煤层地质模型。综合测距臂控制分辨率和煤层几何特征，提出一种基于分段线性表示（PLR）的梯度识别方法。为了在采煤机的能力范围内实现最大开采率，选择范围臂的控制分辨率作为PLR的阈值参数。控制分辨率显着影响线段数和拟合误差。随着控制分辨率从 0.01 降低到 0.02 m，线段数从65条减少到15条，有利于层位控制。然而，平均拟合误差从 0.055 增加到 0.14 m，这将导致开采率下降。为避免截割范围与煤层出现明显偏差，测距臂的控制分辨率必须低于0.02 m。在现场试验中，通过煤层梯度识别实现了长壁工作面的自动化层位控制。