CN-122020087-A - Signal disturbance suppression method, system, tunneling and anchoring integrated machine and storage medium

CN122020087ACN 122020087 ACN122020087 ACN 122020087ACN-122020087-A

Abstract

The application provides a signal disturbance suppression method, a system, an excavating and anchoring integrated machine and a storage medium, and relates to the technical field of signal processing, wherein the method comprises the steps of obtaining a sensing signal, and carrying out modal decomposition on the sensing signal to obtain an IMF matrix and a residual error matrix; the method comprises the steps of determining a disturbance submatrix, a hybrid submatrix and a signal submatrix according to an IMF matrix through an energy-entropy self-adaptive algorithm, separating the hybrid submatrix to obtain a first target signal matrix, filtering the signal submatrix to obtain a second target signal matrix, determining a target signal according to the first target signal matrix, the second target signal matrix and a residual matrix, wherein the target signal is a sensing signal subjected to disturbance inhibition. The application effectively separates the effective signals and noise interference in the sensor signals while completely retaining the key characteristics of the effective signals of the sensor, and effectively improves the intelligent level and the safe, accurate and reliable degree of operation of the tunneling and anchoring integrated machine.

Inventors

ZHOU YU
ZHANG YUANSHENG
MA CHAOYANG
LV XIAO
Jiang zhongye
LI RUOXI

Assignees

北京北矿智能科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260128

Claims (10)

1. A method of signal disturbance suppression, the method comprising: acquiring a sensing signal, and performing modal decomposition on the sensing signal to obtain an IMF matrix and a residual error matrix; determining a disturbance submatrix, a hybrid submatrix and a signal submatrix by an energy-entropy self-adaptive algorithm aiming at the IMF matrix; Separating and obtaining a first target signal matrix from the hybrid submatrix; Filtering the signal submatrices to obtain a second target signal matrix; And determining a target signal according to the first target signal matrix, the second target signal matrix and the residual error matrix, wherein the target signal is the sensing signal subjected to disturbance suppression.
2. The method of claim 1, wherein performing modal decomposition on the sensing signal to obtain an IMF matrix and a residual matrix comprises: the IMF matrix comprises M matrix components, wherein the residual matrix comprises M residual components, and M is more than or equal to 1; Iterating the sensing signals, and determining a j matrix component of the IMF matrix and a j residual component of the residual matrix in a j iteration, wherein j is more than or equal to 1 and less than or equal to M; the jth iteration comprises the steps of respectively adding different Gaussian white noise to the sensing signals to obtain N different preprocessing signals, wherein N is more than or equal to 1; respectively carrying out empirical mode decomposition on each preprocessing signal to obtain multi-order signal components corresponding to each preprocessing signal; determining an average IMF component according to first-order signal components corresponding to the preprocessed signals respectively, and determining the average IMF component as a j matrix component of the IMF matrix; Updating the sensing signal according to the average IMF component, and determining the updated sensing signal as a j-th residual component of the residual matrix; And if the j-th residual component is smaller than a preset component threshold value or the iteration number is equal to the preset iteration number, stopping iterating the sensing signal.
3. The signal disturbance rejection method according to claim 2, wherein the determining, for the IMF matrix, a disturbance sub-matrix, a hybrid sub-matrix, and a signal sub-matrix by an energy-entropy adaptive algorithm comprises: respectively determining sample entropy and normalized energy of each matrix component in the IMF matrix; determining the sample entropy with the largest value as the maximum sample entropy; determining the energy ratio of the jth matrix component according to the normalized energy of the jth matrix component and the sum of the normalized energy of the matrix components; determining signal quality corresponding to a j-th matrix component according to the maximum sample entropy, the sample entropy of the j-th matrix component, the normalized energy and the energy ratio; Combining all matrix components with the signal quality larger than a preset maximum signal quality in the IMF matrix into the disturbance submatrix; Combining each matrix component of the IMF matrix, wherein the signal quality is greater than a preset minimum signal quality and less than or equal to the preset maximum signal quality into the hybrid submatrix; And combining all matrix components with the signal quality less than or equal to the preset minimum signal quality in the IMF matrix into the signal submatrix.
4. A method of suppressing signal disturbance according to claim 3, wherein the hybrid submatrix is a time domain matrix, and the separating from the hybrid submatrix includes: performing short-time Fourier transform on the hybrid submatrices, and converting the hybrid submatrices into time-frequency domain matrixes; Extracting all sub-matrixes of the time-frequency domain matrix to form a sub-matrix set; Screening a target submatrix from the submatrix set, wherein the target submatrix is the submatrix with the minimum difference with the time-frequency domain matrix in the submatrix set; Calculating original components corresponding to the hybrid submatrices by using pseudo-inverse of the target submatrices to obtain a time-frequency domain estimation matrix; And performing inverse short-time Fourier transform on the time-frequency domain estimation matrix to obtain the first target signal matrix.
5. The method of signal disturbance suppression according to claim 4, wherein the filtering the signal submatrices to obtain a second target signal matrix includes: Calculating standard deviation and fourth-order central moment of each signal component in the signal submatrix; determining kurtosis indexes corresponding to the signal components according to the standard deviation and the fourth-order central moment corresponding to the signal components respectively; If the kurtosis index is larger than a preset kurtosis threshold, reserving the corresponding signal component; if the kurtosis index is smaller than or equal to the preset kurtosis threshold, carrying out average filtering processing of a preset window length on the corresponding signal component; And combining the reserved signal components and the signal components subjected to the average filtering treatment into the second target signal matrix.
6. The signal disturbance rejection method according to any one of claims 1-5, wherein the determining a target signal from the first target signal matrix, the second target signal matrix, and the residual matrix comprises: And carrying out fusion processing on the first target signal matrix, the second target signal matrix and the residual error matrix to obtain the target signal.
7. The method of signal disturbance rejection according to claim 6, further comprising discarding the disturbance submatrix.
8. A signal disturbance suppression system, characterized by being applied to the signal disturbance suppression method according to any one of claims 1 to 7, the system comprising: the signal acquisition module is used for acquiring a sensing signal, and carrying out modal decomposition on the sensing signal to obtain an IMF matrix and a residual error matrix; The matrix separation module is used for determining a disturbance submatrix, a hybrid submatrix and a signal submatrix according to an energy-entropy self-adaptive algorithm aiming at the IMF matrix; the first processing module is used for separating and obtaining a first target signal matrix from the hybrid submatrices; The second processing module is used for carrying out filtering processing on the signal submatrices to obtain a second target signal matrix; and the signal determining module is used for determining a target signal according to the first target signal matrix, the second target signal matrix and the residual error matrix, wherein the target signal is the sensing signal subjected to disturbance suppression.
9. An excavating and anchoring integrated machine is characterized by comprising a cutting head, a plurality of sensors and the signal disturbance suppression system of claim 8.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the signal disturbance suppression method according to any one of claims 1-7.

Description

Signal disturbance suppression method, system, tunneling and anchoring integrated machine and storage medium Technical Field The application relates to the technical field of signal processing, in particular to a signal disturbance suppression method, a system, an excavating and anchoring integrated machine and a storage medium. Background The tunneling and anchoring integrated machine is used as tunneling equipment and widely applied to underground operation environments, and the running parameters, surrounding rock mechanical properties, space pose parameters and the like of the tunneling and anchoring integrated machine are collected in real time through the multi-type sensors carried by the tunneling and anchoring integrated machine, so that autonomous decision and accurate control of the drilling and anchoring processes are realized. However, during the operation of the tunneling and anchoring integrated machine, the sensor signals collected by the sensor couple strong disturbance noise from mechanical impact, high-frequency harmonic waves, electromagnetic interference and the like, and the noise is difficult to separate by a traditional filtering mode, so that the decision accuracy of the tunneling integrated machine is adversely affected. Disclosure of Invention In view of the above, the present application aims to overcome the shortcomings in the prior art, and provide a signal disturbance suppression method, a system, an excavating and anchoring integrated machine and a storage medium. The application provides the following technical scheme: in a first aspect, the present application provides a signal disturbance suppression method, the method comprising: acquiring a sensing signal, and performing modal decomposition on the sensing signal to obtain an IMF matrix and a residual error matrix; determining a disturbance submatrix, a hybrid submatrix and a signal submatrix by an energy-entropy self-adaptive algorithm aiming at the IMF matrix; Separating and obtaining a first target signal matrix from the hybrid submatrix; Filtering the signal submatrices to obtain a second target signal matrix; And determining a target signal according to the first target signal matrix, the second target signal matrix and the residual error matrix, wherein the target signal is the sensing signal subjected to disturbance suppression. In an embodiment, the performing modal decomposition on the sensing signal to obtain an IMF matrix and a residual matrix includes: the IMF matrix comprises M matrix components, wherein the residual matrix comprises M residual components, and M is more than or equal to 1; Iterating the sensing signals, and determining a j matrix component of the IMF matrix and a j residual component of the residual matrix in a j iteration, wherein j is more than or equal to 1 and less than or equal to M; the jth iteration comprises the steps of respectively adding different Gaussian white noise to the sensing signals to obtain N different preprocessing signals, wherein N is more than or equal to 1; respectively carrying out empirical mode decomposition on each preprocessing signal to obtain multi-order signal components corresponding to each preprocessing signal; determining an average IMF component according to first-order signal components corresponding to the preprocessed signals respectively, and determining the average IMF component as a j matrix component of the IMF matrix; Updating the sensing signal according to the average IMF component, and determining the updated sensing signal as a j-th residual component of the residual matrix; And if the j-th residual component is smaller than a preset component threshold value or the iteration number is equal to the preset iteration number, stopping iterating the sensing signal. In an embodiment, the determining, for the IMF matrix, a disturbing sub-matrix, a hybrid sub-matrix and a signal sub-matrix by an energy-entropy adaptive algorithm includes: respectively determining sample entropy and normalized energy of each matrix component in the IMF matrix; determining the sample entropy with the largest value as the maximum sample entropy; determining the energy ratio of the jth matrix component according to the normalized energy of the jth matrix component and the sum of the normalized energy of the matrix components; determining signal quality corresponding to a j-th matrix component according to the maximum sample entropy, the sample entropy of the j-th matrix component, the normalized energy and the energy ratio; Combining all matrix components with the signal quality larger than a preset maximum signal quality in the IMF matrix into the disturbance submatrix; Combining each matrix component of the IMF matrix, wherein the signal quality is greater than a preset minimum signal quality and less than or equal to the preset maximum signal quality into the hybrid submatrix; And combining all matrix components with the signal quality less than or equal to the preset