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METHOD AND SYSTEM FOR AUTOMATICALLY IDENTIFYING SURROUNDING ROCK LEVEL BY APPLYING WHILE-DRILLING PARAMETERS

机译:应用旋挖参数自动识别围岩层的方法和系统

摘要

Disclosed are a method and system for identifying a surrounding rock level by applying while-drilling parameters. The method comprises the following steps: pre-processing a data set of collected while-drilling parameters (S110); analyzing the pre-processed data set of the while-drilling parameters, determining a contribution rate of each variable in the while-drilling parameters by applying different data dimension reduction methods, carrying out a weighted average calculation on multiple calculated contribution rates by applying an ordered weighted average operator method, carrying out sorting and preferential selection according to the contribution rates obtained after calculation and combination, and determining the main characteristic variables of the while-drilling parameters, and on this basis, classifying sample sets of the main characteristic parameters (S120); and training the main characteristic parameters of different classifications by applying an established neural network and expert knowledge system, acquiring stable weight coefficients and threshold values, and carrying out surrounding rock identification verification on an established neural network mathematical model by applying sample data to same (S130). By means of the method, a machine automatic identification scheme that has a better working efficiency, is more convenient to use and has a higher precision can be realized.
机译:公开了一种通过应用随钻参数来识别围岩高度的方法和系统。该方法包括以下步骤:预处理收集的随钻参数的数据集(S110);以及分析随钻参数的预处理数据集,通过应用不同的数据降维方法确定随钻参数中每个变量的贡献率,通过应用有序的方法对多个计算的贡献率进行加权平均计算加权平均算子方法,根据计算和组合后的贡献率进行排序和优先选择,确定随钻参数的主要特征变量,并在此基础上对主要特征参数的样本集进行分类(S120) );通过应用已建立的神经网络和专家知识系统训练不同类别的主要特征参数,获取稳定的权重系数和阈值,并通过将样本数据应用于已建立的神经网络数学模型,对已建立的神经网络数学模型进行围岩识别验证(S130 )。通过该方法,可以实现工作效率更高,使用更方便,精度更高的机器自动识别方案。

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