Research on data-driven collaborative control method for mining and transportation in fully mechanized mining face
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摘要:
目前针对采煤机与刮板输送机协同控制的研究初步建立了采运系统协同控制机制，但均未考虑非结构化综采工作面环境下，影响采运系统稳定运行因素的不确定性和耦合特性，以及煤流状态和刮板输送机负载电流受井下电气系统影响而无法真实反映刮板输送机负载变化的情况。针对上述问题，提出了一种基于刮板输送机负载电流强化和随机自注意力胶囊神经网络（RSACNN）的综采工作面采运协同控制方法。针对刮板输送机电动机电流的电气耦合特性，运用电流强化模型对原始刮板输送机电流进行预处理，得到能够反映煤流系统真实负载的电流分量。针对综采工作面采运系统运行状态参数与采煤机牵引速度存在着高度非线性和不确定性关系，难以建立精确数学模型的问题，基于胶囊神经网络（CNN）可保存综采工作面采运系统运行状态突变等细粒度特征的特性，建立了基于RSACNN的综采工作面采运协同控制模型。实验结果表明：RSACNN算法与自注意力胶囊神经网络（SACNN）算法、CNN算法的调速结果相比，预测的采煤机牵引速度精度更高，预测速度与真实速度的拟合度分别提高了0.032 05和0.075 04；平均绝对误差分别降低了17.7%，22.6%；平均绝对百分误差分别降低了49.9%，71.5%；均方根误差分别降低了13.3%，34.6%。
Abstract:Currently, research on the collaborative control of shearers and scraper conveyors has preliminarily established a collaborative control mechanism for mining and transportation systems. But none of them have taken into account the uncertainty and coupling features of factors that affect the stable operation of mining and transportation systems in unstructured fully mechanized mining face environments. And the coal flow state and scraper conveyor load current are affected by the underground electrical system and cannot truly reflect the changes in scraper conveyor load. In order to solve the above problems, a collaborative control method for mining and transportation in fully mechanized mining face based on scraper conveyor load current intensification and random self-attention capsule network (RSACNN) is proposed. Based on the electrical coupling features of the electric motor current of the scraper conveyor, a current intensification model is used to preprocess the original scraper conveyor current and obtain the current component that can reflect the real load of the coal flow system. There is a highly nonlinear and uncertain relationship between the operating state parameters of the mining and transportation system in the fully mechanized mining face and the traction speed of the shearer. It is difficult to establish an accurate mathematical model. In order to solve the above problem, based on capsule neural network (CNN), the features of fine-grained features such as sudden changes in the operating state of the mining and transportation system in the fully mechanized mining face can be preserved. A collaborative control model for mining and transportation in the fully mechanized mining face based on RSACNN is established. The verification results show that compared with the self-attention capsule neural network (SACNN) method and the CNN method, the proposed RSACNN method has higher precision in predicting the traction speed of the shearer. The fitting values between the predicted speed and the actual speed have increased by 0.032 05 and 0.075 04 respectively. The average absolute error decreases by 17.7% and 22.6% respectively. The average absolute percentage error decreases by 49.9% and 71.5% respectively. The root mean square error decreases by 13.3% and 34.6% respectively.
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图 1 基于电流强化的刮板输送机负载映射方法
Figure 1. Load mapping method of scraper conveyor based on current intensification
图 2 综采工作面采运系统煤流监测点位
Figure 2. Coal flow monitoring points of fully mechanized mining face
图 4 综采工作面采运协同控制模型
Figure 4. Collaborative control model of mining and transportation in fully mechanized mining face
图 6 胶囊神经网络动态路由算法流程
Figure 6. Dynamic routing algorithm process of capsule neural network
图 12 不同算法下采煤机牵引速度预测曲线
Figure 12. Shearer traction speed prediction curves under different algorithms
图 13 采煤机牵引速度控制结果ROC曲线
Figure 13. Receiver operating characteristic curve of shearer traction speed control results
图 14 3种算法控制结果决定系数
Figure 14. Determination coefficients of three algorithms control result
表 1 综采工作面煤流系统运行状态参数
Table 1. Operation parameters of coal flow system in fully mechanized mining face
被测对象 特征编码 特征名称 单位 刮板输送机 l0 煤流量 m3 l1 垂直电动机电流 A l2 垂直电动机转速 r/min l3 刮板输送机速度 m/min l4 机尾电动机电流 A l5 机尾电动机转速 r/min 采煤机 l6 牵引变频器输出电流 A l7 采煤机牵引方向 左、右 l8 采煤机实际速度 m/min l9 采煤机位置架 架 l10 采煤机右截割电流 A l11 采煤机左截割电流 A 
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表 2 综采工作面煤流系统运行状态数据集
Table 2. Data set of coal flow system operating status in fully mechanized mining face
样本
序号状态参数 决策d 刮板输送机 采煤机 l0 l1 l2 l3 l4 l5 l6 l7 l8 l9 l10 l11 1 0.457 124 1197 0 110 1204 112.2 128 10.0 168 60.2 61.0 9.5 2 0.459 127 1199 0 74 1203 115.1 32 9.5 162 73.8 60.3 8.3 3 0.512 144 1200 1 79 1210 113.4 0 8.3 161 66.9 64.8 7.1 4 0.491 126 1198 1 85 1207 116.2 32 7.1 159 70.7 76.5 6.1 5 0.450 133 1197 0 87 1211 117.1 0 6.1 160 62.7 55.7 11.9 5 001 0.450 133 1200 1 122 1200 80.6 64 11.9 10 63.0 47.7 12.1 5 002 0.450 128 1197 1 89 1202 89.0 32 12.1 11 71.7 52.2 9.6 5 003 0.463 129 1202 0 77 1199 92.0 256 9.6 13 72.3 53.3 9.5 5 004 0.488 135 1200 0 80 1204 101.3 32 9.5 14 87.0 51.2 9.2 5 005 0.471 131 1201 0 77 1212 94.3 128 9.2 16 67.4 56.8 0.8 9 997 0.366 110 1198 1 106 1199 133.2 32 2.1 14 46.2 76.2 3.2 9 998 0.481 101 1199 1 114 1200 125.7 0 3.2 10 49.8 85.1 0.6 9 999 0.445 118 1198 1 116 1202 117.6 256 0.6 9 49.3 73.4 2.1 10 000 0.401 114 1199 0 120 1198 116.5 4 2.1 8 57.6 90.9 3.7
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表 3 不同算法预测结果比较
Table 3. Comparison of prediction results of different algorithms
算法 平均绝对
误差/（mm·min−1）平均绝对百分
误差/%均方根误差/
（mm·min−1）RSACNN 71.23 1.073 87.63 SACNN 86.51 2.142 101.03 CNN 92.03 3.771 134.01
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