Roof pressure prediction method of coal working face based on spatiotemporal correlation analysis
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摘要: 顶板压力一般通过液压支架工作阻力进行度量，基于深度学习的顶板压力预测方法效果受训练样本集影响极大，而训练样本集的构建依赖于时间窗口的选择和紧密关联液压支架群的识别，但现有方法依靠人工经验来确定时间窗口，且忽略了不同液压支架之间的关联性，严重阻碍了顶板压力预测精度的提高。针对上述问题，提出了一种基于时空关联分析的采煤工作面顶板压力预测方法。首先，通过计算同一液压支架工作阻力序列在时间维度上的灰色关联度，选择最优时间窗口。然后，通过计算不同液压支架工作阻力序列在空间维度上的灰色关联度，获得最优辅助矩阵，识别出紧密关联液压支架群。最后，基于最优时间窗口和最优辅助矩阵，确定每个训练样本的标签和对应特征，完成训练样本集构建，以对长短时记忆(LSTM)模型进行训练来预测顶板压力。实验结果表明，与依赖人工经验构建训练样本集完成LSTM模型训练的方法相比，本文方法有效降低了顶板压力预测误差。Abstract: The roof pressure is usually measured by the hydraulic support working resistance, and the effect of the roof pressure prediction method based on depth learning is greatly affected by the training sample set. The construction of the training sample set depends on the selection of the time window and the identification of the closely related hydraulic support group. However, the existing methods rely on manual experience to determine the time window, and ignore the correlation between different hydraulic supports, which seriously hinders the improvement of the roof pressure prediction precision. In order to solve the above problems, a roof pressure prediction method of coal working face based on spatiotemporal correlation analysis is proposed. Firstly, the optimal time window is selected by calculating the grey correlation degree of working resistance series of the same hydraulic support in the time dimension. Secondly, the optimal auxiliary matrix is obtained by calculating the grey correlation degree of working resistance sequences of different hydraulic support in spatial dimension, and the closely related hydraulic support group is identified. Finally, based on the optimal time window and the optimal auxiliary matrix, the label and corresponding characteristics of each training sample are determined, and the training sample set is constructed to train the long short time memory (LSTM) model to predict the roof pressure. The experimental results show that the proposed method can reduce the prediction error of roof pressure effectively compared with the method which relies on manual experience to construct training sample sets to complete LSTM model training.
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图 1 基于时空关联分析的顶板压力预测方法流程
Figure 1. Flow of roof pressure prediction method based on spatiotemporal correlation analysis
表 1 部分液压支架工作阻力序列时间关联度
Table 1. Time correlation degrees of working resistance sequences for partial hydraulic supports
k 40号 45号 50号 55号 60号 65号 70号 75号 80号 85号 90号 95号 100号 105号 110号 1 0.834 0.830 0.802 0.836 0.807 0.816 0.816 0.809 0.820 0.838 0.798 0.857 0.851 0.857 0.863 2 0.817 0.817 0.777 0.821 0.788 0.793 0.792 0.788 0.795 0.816 0.774 0.832 0.840 0.845 0.857 3 0.800 0.791 0.757 0.800 0.761 0.771 0.765 0.761 0.772 0.793 0.754 0.811 0.825 0.832 0.848 4 0.784 0.778 0.737 0.784 0.742 0.753 0.747 0.745 0.752 0.777 0.745 0.800 0.816 0.823 0.847 5 0.770 0.766 0.727 0.764 0.726 0.741 0.731 0.730 0.737 0.768 0.731 0.790 0.810 0.823 0.847 6 0.756 0.754 0.720 0.754 0.713 0.729 0.724 0.726 0.726 0.758 0.718 0.784 0.810 0.820 0.846 7 0.752 0.745 0.707 0.746 0.705 0.724 0.723 0.723 0.725 0.756 0.712 0.780 0.803 0.821 0.847 8 0.749 0.743 0.700 0.745 0.700 0.720 0.719 0.718 0.719 0.754 0.712 0.781 0.802 0.817 0.844 9 0.743 0.741 0.699 0.737 0.695 0.718 0.718 0.717 0.719 0.755 0.708 0.778 0.800 0.816 0.845 10 0.742 0.739 0.694 0.738 0.694 0.717 0.720 0.721 0.719 0.756 0.706 0.779 0.798 0.816 0.844 11 0.742 0.740 0.695 0.737 0.693 0.720 0.718 0.724 0.725 0.754 0.704 0.777 0.797 0.815 0.842 12 0.743 0.741 0.696 0.732 0.693 0.723 0.720 0.727 0.724 0.753 0.703 0.775 0.796 0.816 0.841 13 0.745 0.746 0.696 0.733 0.699 0.722 0.726 0.728 0.728 0.758 0.704 0.774 0.796 0.813 0.840 14 0.752 0.747 0.705 0.738 0.703 0.722 0.728 0.727 0.731 0.755 0.701 0.773 0.794 0.813 0.842 15 0.754 0.750 0.706 0.739 0.707 0.728 0.729 0.731 0.728 0.761 0.702 0.773 0.798 0.814 0.845 16 0.759 0.755 0.711 0.745 0.711 0.733 0.733 0.729 0.729 0.764 0.705 0.777 0.799 0.814 0.840 17 0.759 0.759 0.714 0.749 0.716 0.735 0.736 0.725 0.731 0.762 0.708 0.778 0.801 0.815 0.841 18 0.765 0.763 0.718 0.756 0.723 0.737 0.735 0.732 0.735 0.769 0.716 0.784 0.801 0.816 0.840 19 0.768 0.767 0.722 0.756 0.729 0.742 0.743 0.737 0.737 0.768 0.715 0.788 0.805 0.818 0.844 20 0.770 0.772 0.721 0.766 0.731 0.744 0.744 0.745 0.742 0.769 0.719 0.790 0.808 0.818 0.847 
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表 2 部分液压支架工作阻力序列时间关联度排名
Table 2. Ranks of time correlation degrees of working resistance sequences for partial hydraulic supports
k 40号 45号 50号 55号 60号 65号 70号 75号 80号 85号 90号 95号 100号 105号 110号 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 6 7 5 6 7 7 10 9 6 8 5 5 5 4 7 6 11 11 8 9 10 11 14 14 14 12 7 9 6 7 8 7 13 15 12 11 13 13 15 17 16 14 11 11 9 6 4 8 15 16 15 12 15 17 18 19 19 19 10 10 10 10 11 9 17 17 16 18 17 19 19 20 20 17 13 14 13 13 10 10 20 20 20 16 18 20 16 18 18 15 14 12 15 12 12 11 19 19 19 17 19 18 20 16 15 18 17 15 17 16 15 12 18 18 17 20 20 14 17 12 17 20 18 17 19 14 16 13 16 14 18 19 16 16 13 11 13 13 16 18 18 20 19 14 14 13 14 15 14 15 12 13 9 16 20 20 20 19 14 15 12 12 13 14 12 12 11 8 12 11 19 19 16 17 9 16 9 10 11 13 11 10 9 10 11 9 15 16 14 18 18 17 10 9 10 10 9 9 7 15 10 10 12 13 12 15 17 18 8 8 9 7 8 8 8 7 8 5 8 8 11 11 20 19 7 6 6 8 6 6 6 6 7 7 9 7 8 9 13 20 5 5 7 5 5 5 5 5 5 6 6 6 7 8 6
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表 3 部分液压支架最优时间窗口
Table 3. Optimal time windows for partial hydraulic supports
液压支架 40号 45号 50号 55号 60号 65号 70号 75号 80号 85号 90号 95号 100号 105号 110号 最优时间窗口 5 5 6 6 6 5 5 5 5 5 6 6 8 8 7
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表 4 部分液压支架工作阻力序列空间关联度
Table 4. Spatial correlation degrees among working resistance sequences for partial hydraulic supports
液压支架 液压支架 40号 45号 50号 55号 60号 65号 70号 75号 80号 85号 90号 95号 100号 105号 110号 40号 1.000 0.902 0.863 0.877 0.872 0.862 0.856 0.849 0.855 0.854 0.842 0.854 0.853 0.854 0.849 45号 0.895 1.000 0.863 0.876 0.875 0.864 0.855 0.840 0.852 0.852 0.835 0.844 0.845 0.841 0.836 50号 0.852 0.860 1.000 0.860 0.855 0.842 0.834 0.826 0.832 0.830 0.826 0.827 0.824 0.827 0.820 55号 0.878 0.881 0.868 1.000 0.899 0.876 0.867 0.854 0.863 0.852 0.849 0.854 0.849 0.845 0.839 60号 0.868 0.873 0.862 0.898 1.000 0.900 0.883 0.859 0.869 0.857 0.850 0.854 0.850 0.847 0.838 65号 0.860 0.869 0.852 0.878 0.902 1.000 0.902 0.874 0.878 0.867 0.855 0.855 0.853 0.850 0.843 70号 0.852 0.859 0.842 0.866 0.883 0.901 1.000 0.886 0.886 0.874 0.863 0.862 0.858 0.855 0.844 75号 0.846 0.846 0.836 0.854 0.860 0.873 0.887 1.000 0.888 0.876 0.862 0.861 0.854 0.849 0.844 80号 0.850 0.856 0.840 0.861 0.868 0.876 0.886 0.886 1.000 0.904 0.875 0.880 0.872 0.865 0.849 85号 0.852 0.857 0.840 0.853 0.858 0.867 0.876 0.876 0.906 1.000 0.886 0.890 0.878 0.868 0.855 90号 0.841 0.843 0.839 0.852 0.854 0.856 0.866 0.865 0.876 0.888 1.000 0.887 0.870 0.858 0.845 95号 0.852 0.851 0.839 0.856 0.857 0.856 0.864 0.862 0.883 0.890 0.886 1.000 0.901 0.886 0.865 100号 0.839 0.840 0.823 0.840 0.840 0.842 0.849 0.843 0.865 0.869 0.857 0.892 1.000 0.894 0.867 105号 0.839 0.834 0.823 0.833 0.835 0.836 0.844 0.836 0.855 0.856 0.843 0.874 0.892 1.000 0.886 110号 0.838 0.834 0.822 0.832 0.831 0.834 0.838 0.836 0.843 0.847 0.835 0.857 0.869 0.890 1.000
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表 5 不同方法预测误差对比
Table 5. Comparison of prediction errors under different methods
% 方法 40号 45号 50号 55号 60号 65号 70号 75号 80号 85号 90号 95号 100号 105号 110号 传统方法 12.90 11.90 18.26 12.53 11.96 13.96 13.81 13.86 12.89 12.25 14.43 10.88 11.71 8.82 6.99 本文方法 11.99 11.44 17.34 12.17 10.93 12.24 12.52 13.07 12.57 11.57 14.24 9.99 11.08 8.31 6.99
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