Correlation analysis method for multi-source tunneling parameters of shield tunneling machine

A technology of correlation analysis and tunneling parameters, which is applied in the field of multi-source tunneling parameter correlation analysis of shield machines, can solve the problems that the prediction results include passive quantities, parameters cannot be adjusted, and parameter correlations are easy to be ignored, so as to improve efficiency and Accuracy of predictions, improvement of robustness and quality, effects of tuning up analytical efficiency

Pending Publication Date: 2022-01-07
JINAN RAILWAY TRANSPORT GRP CO LTD +2
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Problems solved by technology

[0004] The correlation analysis involved in the above-mentioned patents is mainly to select the excavation parameters according to the linear relationship analysis r

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  • Correlation analysis method for multi-source tunneling parameters of shield tunneling machine
  • Correlation analysis method for multi-source tunneling parameters of shield tunneling machine
  • Correlation analysis method for multi-source tunneling parameters of shield tunneling machine

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Embodiment Construction

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0067] The basic steps of the correlation analysis of the driving parameters in the prior art are as follows:

[0068] (1) Select six excavation parameters including propulsion speed, penetration, cutterhead torque, total thrust, cutterhead speed, slurry chamber pressure (slurry shield) or earth chamber pressure (earth pressure balance shield) for analysis;

[0069] (2) The excavation process of each ring is divided into excavation time and non-excavation ...

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Abstract

The invention relates to a correlation analysis method for multi-source tunneling parameters of a shield tunneling machine. The method comprises the following steps of: 1, acquiring multi-source data of the shield tunneling machine, and preprocessing the data; 2, carrying out preliminary screening of tunneling parameters based on a variance selection method; 3, analyzing a linear relation between every two parameters; 4, analyzing a nonlinear relation between every two parameters; 5, analyzing a linear relation between passive parameters and active parameters based on a multiple linear regression model; 6, analyzing a nonlinear relation between the passive parameters and the active parameters based on a polynomial regression model; and 7, carrying out model training between passive parameters and active parameters on residual parameters screened in the step 6 by utilizing a random forest algorithm, and selecting an optimal parameter combination according to feature importance. Compared with the prior art, the correlation analysis method has the advantages of being high in accuracy, more reliable, visual, high in efficiency, high in analysis capability and the like.

Description

technical field [0001] The invention relates to the technical field of shield machine parameter processing, in particular to a correlation analysis method for multi-source tunneling parameters of a shield machine. Background technique [0002] At present, shield tunneling construction is still dominated by manual operations, and it is difficult for operators to comprehensively analyze various parameters and make timely and accurate adjustments in the face of a large number of tunneling parameters of shield machines, which will affect tunneling efficiency. In order to make up for the lack of manual operation, machine learning has become an important means to solve the prediction of tunneling parameters of shield machines. Many scholars use neural network, LSTM, random forest and other machine learning algorithms to train models and realize the prediction of tunneling parameters. For machine learning, the selection of the model and the input volume of the model are two key fac...

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Application Information

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IPC IPC(8): G06F30/17G06F30/27G06F17/18G06F119/14
CPCG06F30/17G06F30/27G06F17/18G06F2119/14
Inventor 陈思斌黄永亮王伯芝门燕青刘凤洲陈文明邢慧堂刘学增曹玉鑫丁爽桑运龙师刚
Owner JINAN RAILWAY TRANSPORT GRP CO LTD
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