Manual regulation and control decision model of middle hydraulic support cluster automation in the intelligent working face
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摘要: 智采工作面在液压支架自动跟机完成后，会出现丢架、直线度不平整、支架歪斜等异常工况，需要人工调控，目前研究缺乏对智采工作面生产过程中液压支架自动化后人工调控工况的知识发现，不利于工人快速判断需人工调控的液压支架架号。针对上述问题，从判别液压支架自动化后动作不达标液压支架架号出发，提出了一种智采工作面中部液压支架集群自动化后人工调控决策模型。 首先，对工作面历史数据进行分析，得出液压支架自动跟机完成后3个特征值(即自动跟机拉架距离、自动跟机前后的推移油缸行程变化量、采煤机位置支架号与被判断支架号的绝对差值)可作为判别液压支架自动跟机后是否进行人工调控的重要特征。根据上述结论，给出了液压支架集群自动化后人工调控决策模型结构，其中数据采集模块用于提供原始数据；数据预处理模块对原始数据进行异常值处理、筛选、排序和相关性分析等数据准备工作；特征工程模块对上述3个特征值进行计算及标准化处理，为分类模型提供样本集；分类模型对样本集进行划分后，利用ID3决策树进行分类，最后输出正常工况下的液压支架架号与需人工调控的液压支架架号。模型评估结果表明，与传统K最近邻(KNN)、支持向量机(SVM)、逻辑回归(LR)等分类算法相比，基于ID3决策树的智采工作面中部液压支架工况分类模型的训练集准确率为92.27%，测试集准确率为93.75%，能够较好地判别自动化后人工调控液压支架架号。Abstract: The intelligent working face has abnormal working conditions such as lost support, uneven straightness, and support skew after the automatic following of hydraulic support. Therefore, manual regulation and control are needed. At present, the research lacks the knowledge discovery of manual regulation and control working conditions after the hydraulic support automation in the production process of the intelligent working face. This is not conducive for workers to quickly judge the number of hydraulic support requiring manual regulation and control. In order to solve the above problems, based on the identification of the number of hydraulic support that is not up to the standard after the hydraulic support automation, a manual regulation and control decision model of middle hydraulic support cluster automation in the intelligent working face is put forward. Firstly, the historical data of the working face is analyzed. It is concluded that after the automatic following of the hydraulic support is finished, three characteristic values can be used as important characteristics for judging whether the hydraulic support carries out manual regulation and control after the automatic following of the hydraulic support. The characteristic values include the distance of the automatic following of the hydraulic support, the stroke variation of the pushing oil cylinder before and after the automatic following of the hydraulic support, and the absolute difference between the number of the hydraulic support at the position of the shearer and the number of the judged hydraulic support. According to the above conclusion, the structure of the manual control decision model after the hydraulic support cluster automation is proposed. The data acquisition module is used for providing the original data. The data preprocessing module prepares the original data by outlier processing, filtering, sorting and correlation analysis. The characteristic engineering module calculates and standardizes the above three characteristic values to provide a sample set for the classification model. After the classification model divides the sample set, the ID3 decision tree is used for classification. Finally, the number of hydraulic supports needing normal working conditions and the number of hydraulic supports nedeing manual control are output. The results of the model evaluation show that, compared with the traditional K-nearest neighbor (KNN), support vector machine (SVM), logical regression (LR) classification algorithms, the training set accuracy of the ID3 decision tree based classification model for the working conditions of hydraulic supports in the middle of the intelligent working face is 92.27%. The test set accuracy is 93.75%. The model can better distinguish the manual control hydraulic support number after automation.
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图 1 第20号液压支架在正常工况下特征值变化曲线
Figure 1. Change curves of characteristic value of No.20 hydraulic support under normal working conditions
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图 2 第20号液压支架自动跟机及自动化后人工干预调控工况下特征值变化曲线
Figure 2. Change curves of characteristic value of No.20 hydraulic support automatic following and manual lifting operation after automation
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图 3 液压支架集群自动化后人工调控决策建模流程
Figure 3. Manual regulation and control decision modeling process after automation of hydraulic support cluster
表 1 样本数量统计
Table 1 Quantity statistics of samples
样本 样本数量 占比/% 正常工况样本 4 986 84.73 自动化后人工调控工况样本 899 15.27 
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表 2 模型准确率统计
Table 2 Model accuracy statistics
% 算法 训练集准确率 测试集准确率 决策树 92.27 93.75 KNN 91.51 91.82 SVM 93.84 90.82 LR 89.00 90.86
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