Shield tunneling machine working posture real-time prediction method based on big data

Abstract

The invention belongs to the technical field of shield tunneling machines, and discloses a shield tunneling machine working posture real-time prediction method based on big data, which comprises the following steps: obtaining overall data: obtaining engineering, equipment and stratum parameters; analyzing and preprocessing the shield data; and establishing a shield attitude prediction model. In order to convert original data into a data format for deep learning, based on a data trend of shield feature engineering, a data standardization preprocessing process is divided into four parts: data segmentation, discrete point processing, missing value processing and data noise reduction. According to the prediction method, the shield data is subjected to standardized preprocessing, the workload of computer processing can be effectively reduced, the working efficiency is improved, and compared with a traditional artificial intelligence prediction model, a Butterworth noise reduction method is provided in a targeted mode, and it is guaranteed that the working posture of the shield tunneling machine is accurately predicted in real time.

Description

technical field [0001] The invention belongs to the technical field of shield machines, in particular to a method for real-time prediction of the working attitude of shield machines based on big data. Background technique [0002] In recent years, the scale and quantity of underground projects represented by large-scale water conservancy tunnels, traffic tunnels and urban subways have shown a rising trend. The shield tunneling method is the preferred construction method for constructing urban underground tunnels. It has the characteristics of environmental protection, safety, and high efficiency. The basic principle of the shield tunneling method construction is that the steel components of the cylinder are tunneled underground along the designed axis, and are synchronized during the tunneling process. Lay support segments. Compared with the TBM rock tunneling machine, the tunneling process of the shield machine is more refined, and the operating system is more complicated....

AI Technical Summary

Problems solved by technology

At present, a certain amount of attempts have been made in this area at home and abroad, but most of these attempts are based on a single attitude parameter, and no standard data preprocessing algorithm for EPB shield machines has been clearly proposed, and a relatively mature intelligent tunneling system is lacking.

[0004] At present, some achievements have been made in the prediction and correction of the attitude of the shield machine. However, from the perspective of the prediction target, the output variables of the above prediction research are mostly single values. Secondly, from the perspective of input data, although the input of the above prediction model The data contains many variables, but due to the low frequency and small scale of the data, and the lack of a dedicated data preprocessing scheme, it is difficult to adapt to complex strata. In summary, there are still the following difficulties for the intelligent prediction of the shield machine attitude: ① The attitude prediction of the shield machine belongs to the problem of multi-dimensional sequence data input (sequence data of numerous operating parameters of the shield machine) and multi-objective solution (multiple at

Images