Railway ballast bed detection scattered energy extraction method and device

Abstract

The application discloses a railway ballast bed detection scattered energy extraction method and device, and relates to the technical field of track signal processing. The method comprises the following steps: removing interference from a return signal data matrix to obtain a first data matrix; calculating the correlation coefficient between adjacent two columns of data in the first data matrix; removing reflection energy from the first data matrix according to the correlation coefficient between adjacent two columns of data in the first data matrix to obtain a second data matrix; performing square calculation on the element values in the second data matrix to obtain a third data matrix; performing filtering processing on the matrix data below the sleepers in the third data matrix to obtain a fourth data matrix; and calculating the mean value or the expected value of the element values in each preset numerical column in the fourth matrix to obtain the intensity of the scattered energy in the railway ballast bed detection section. The application can accurately extract the railway ballast bed detection scattered energy.

Description

Method and apparatus for extracting scattered energy from railway ballast track bed Technical Field

[0001] This invention relates to the field of track signal processing technology, and in particular to a method and apparatus for extracting scattered energy from railway ballast track. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] Ballasted track is the main track structure for conventional railways and some high-speed railways. With increasing service life, the ballast inside the crushed stone track bed pulverizes under repeated dynamic loads from trains. Debris and transport waste gradually infiltrate the voids within the track bed, causing it to become dirty and deteriorated, losing its elasticity and drainage function, thus affecting train operation quality and safety. Railway infrastructure maintenance personnel need to regularly assess the dirt and grime condition of the track bed and carry out repairs on severely deteriorated sections.

[0004] Current methods for detecting gravel track beds using ground-penetrating radar (GPR) evaluate the track bed by acquiring the instantaneous energy of the detection data. The steps include background denoising, filtering, gain adjustment, Hilbert transform, envelope extraction, and smoothing. However, in actual testing, sudden electrical changes in the track bed caused by rainfall and waterlogging affect the accuracy of the results, making it impossible to accurately determine the track bed's contamination status. Furthermore, some sections of track that have undergone cleaning and maintenance generate a large number of reflected echoes due to electrical abrupt changes between the cleaned and uncleaned layers, reducing the detection effectiveness in the cleaned sections and weakening the GPR's ability to detect track bed contamination. Currently, there is a lack of effective methods for extracting the scattered energy from railway gravel track bed detection. Summary of the Invention

[0005] This invention provides a method for extracting scattered energy from railway ballast track detection, which accurately extracts the scattered energy from railway ballast track detection and improves the efficiency of extracting scattered energy from railway ballast track detection. The method includes:

[0006] The ground-penetrating radar sends electromagnetic wave signals and receives the return signals after the electromagnetic wave signals are reflected back by the track, thus obtaining a return signal data matrix. In the return signal data matrix, the element values ​​represent the amplitude of the return signal. The number of columns in the return signal data matrix is ​​equal to the number of the first sampling points over the sampling distance, and the number of rows in the return signal data matrix is ​​equal to the number of the second sampling points in the sampling channel. The number of the second sampling points is determined according to the required accuracy and resolution in the depth direction of the track bed being inspected.

[0007] The window size is determined based on the sleeper spacing, and interference is removed from the returned signal data matrix based on the window size to obtain the first data matrix.

[0008] The correlation coefficient between two adjacent columns of data in the first data matrix is ​​calculated based on the element values ​​in the first data matrix and the number of rows in the first data matrix.

[0009] Based on the correlation coefficient between adjacent columns of the first data matrix, the first data matrix is ​​processed to remove reflected energy, resulting in the second data matrix.

[0010] The third data matrix is ​​obtained by squaring the element values ​​in the second data matrix.

[0011] The matrix data below the sleeper in the third data matrix is ​​extracted and filtered to obtain the fourth data matrix;

[0012] Based on the preset values, the mean or expected value of the element values ​​in each preset value column of the fourth matrix is ​​calculated to obtain the fifth data matrix. The intensity of scattered energy in the railway ballast track detection section is obtained based on the element values ​​in the fifth data matrix.

[0013] This invention also provides a device for extracting scattered energy from railway ballast track detection, used to accurately extract the scattered energy from railway ballast track detection and improve the efficiency of extracting scattered energy from railway ballast track detection. The device includes:

[0014] The data acquisition module is used to send electromagnetic wave signals using ground-penetrating radar, receive the return signals after the electromagnetic wave signals are reflected back by the track, and obtain a return signal data matrix. The element values ​​in the return signal data matrix represent the amplitude of the return signal. The number of columns in the return signal data matrix is ​​equal to the number of the first sampling points over the sampling distance, and the number of rows in the return signal data matrix is ​​equal to the number of the second sampling points in the sampling channel. The number of the second sampling points is determined according to the required accuracy and resolution in the depth direction of the track bed being inspected.

[0015] The first processing module is used to determine the window size based on the sleeper spacing, and to remove interference from the returned signal data matrix based on the window size to obtain the first data matrix.

[0016] The second processing module is used to calculate the correlation coefficient between two adjacent columns of data in the first data matrix based on the element values ​​in the first data matrix and the number of rows in the first data matrix.

[0017] The third processing module is used to remove reflection energy from the first data matrix based on the correlation coefficient between two adjacent columns of data in the first data matrix to obtain the second data matrix.

[0018] The fourth processing module is used to square the element values ​​in the second data matrix to obtain the third data matrix;

[0019] The fifth processing module is used to extract the matrix data below the sleeper in the third data matrix, perform filtering processing, and obtain the fourth data matrix;

[0020] The sixth processing module is used to calculate the mean or expected value of the element values ​​of each preset value column in the fourth matrix according to the preset values ​​to obtain the fifth data matrix, and to obtain the intensity of scattered energy in the railway ballast track detection section according to the element values ​​in the fifth data matrix.

[0021] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for extracting scattered energy from railway ballast track detection.

[0022] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for extracting scattered energy from railway ballast track detection.

[0023] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for extracting scattered energy from railway ballast track detection.

[0024] In this embodiment of the invention, a ground-penetrating radar is used to transmit electromagnetic wave signals, and the return signals reflected back from the track are received to obtain a return signal data matrix. The element values ​​in the return signal data matrix represent the amplitude of the return signal. The number of columns in the return signal data matrix is ​​equal to the number of first sampling points over the sampling distance, and the number of rows in the return signal data matrix is ​​equal to the number of second sampling points in the sampling channel. The number of second sampling points is determined based on the required depth-direction accuracy resolution for detecting the track bed. The window size is determined based on the sleeper spacing, and interference is removed from the return signal data matrix based on the window size to obtain a first data matrix. The first data matrix is ​​then analyzed based on the element values ​​and... The correlation coefficient between adjacent columns of the first data matrix is ​​calculated based on the number of rows. Based on this correlation coefficient, the first data matrix is ​​processed to remove reflected energy, resulting in the second data matrix. The element values ​​in the second data matrix are squared to obtain the third data matrix. Data below the sleeper in the third data matrix is ​​filtered to obtain the fourth data matrix. The mean or expected value of the element values ​​in each preset column of the fourth matrix is ​​calculated based on preset values ​​to obtain the fifth data matrix. The intensity of scattered energy within the railway ballast track detection section is obtained from the element values ​​in the fifth matrix. This method accurately extracts the scattered energy from railway ballast track detection, improving the efficiency of this process. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0026] Figure 1 is a flowchart of a method for extracting scattered energy from railway ballast track bed detection according to an embodiment of the present invention;

[0027] Figure 2 is a schematic diagram of the line results of a test site provided in an embodiment of the present invention;

[0028] Figure 3 is a schematic diagram illustrating the effect of a data processing procedure provided in an embodiment of the present invention;

[0029] Figure 4 is a schematic diagram of a railway ballast track detection and scattering energy extraction device provided in an embodiment of the present invention;

[0030] Figure 5 is a schematic diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0032] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0033] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0034] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0035] Research has revealed that ballasted track is the primary track structure for conventional railways and some high-speed railways in my country. With increasing service life, the ballast within the crushed stone track bed pulverizes under repeated dynamic loads from trains. Debris and transport waste gradually infiltrate the voids within the track bed, causing it to become dirty and deteriorated, losing its elasticity and drainage function, thus affecting train operation quality and safety. Railway infrastructure maintenance personnel must regularly assess the dirt and grime condition of the track bed and carry out repairs on severely deteriorated sections.

[0036] Current methods for detecting gravel track beds using ground-penetrating radar (GPR) evaluate the track bed by acquiring the instantaneous energy of the detection data. The steps include background denoising, filtering, gain adjustment, Hilbert transform, envelope extraction, and smoothing. However, in actual testing, sudden electrical changes in the track bed caused by rainfall and waterlogging affect the accuracy of the results, making it impossible to accurately determine the track bed's contamination status. Furthermore, some sections of track that have undergone cleaning and maintenance generate a large number of reflected echoes due to electrical abrupt changes between the cleaned and uncleaned layers, reducing the detection effectiveness in the cleaned sections and weakening the GPR's ability to detect track bed contamination. Currently, there is a lack of effective methods for extracting the scattered energy from railway gravel track bed detection.

[0037] Based on the above research, as shown in Figure 1, this embodiment of the invention provides a method for extracting scattered energy from railway ballast track, including:

[0038] S101: Using ground-penetrating radar to send electromagnetic wave signals, receiving the return signals after the electromagnetic wave signals are reflected back by the track, and obtaining the return signal data matrix; where the element values ​​in the return signal data matrix represent the amplitude of the return signal, the number of columns in the return signal data matrix is ​​equal to the number of the first sampling points over the sampling distance, and the number of rows in the return signal data matrix is ​​equal to the number of the second sampling points in the sampling channel. The number of the second sampling points is determined according to the required accuracy resolution in the depth direction of the track bed being inspected;

[0039] S102: Determine the window size based on the sleeper spacing, and remove interference from the returned signal data matrix based on the window size to obtain the first data matrix;

[0040] S103: Calculate the correlation coefficient between two adjacent columns of data in the first data matrix based on the element values ​​in the first data matrix and the number of rows in the first data matrix;

[0041] S104: Based on the correlation coefficient between two adjacent columns of data in the first data matrix, the first data matrix is ​​processed to remove reflected energy, thus obtaining the second data matrix;

[0042] S105: Squaring the element values ​​in the second data matrix to obtain the third data matrix;

[0043] S106: Extract the matrix data below the sleeper from the third data matrix and perform filtering to obtain the fourth data matrix;

[0044] S107: Based on the preset values, calculate the mean or expected value of the element values ​​in each preset value column of the fourth matrix to obtain the fifth data matrix. Based on the element values ​​in the fifth data matrix, obtain the intensity of scattered energy in the railway ballast track detection section.

[0045] In this embodiment of the invention, a ground-penetrating radar is used to transmit electromagnetic wave signals, and the return signals reflected back from the track are received to obtain a return signal data matrix. The element values ​​in the return signal data matrix represent the amplitude of the return signal. The number of columns in the return signal data matrix is ​​equal to the number of first sampling points over the sampling distance, and the number of rows in the return signal data matrix is ​​equal to the number of second sampling points in the sampling channel. The number of second sampling points is determined based on the required depth-direction accuracy resolution for detecting the track bed. The window size is determined based on the sleeper spacing, and interference is removed from the return signal data matrix based on the window size to obtain a first data matrix. The first data matrix is ​​then analyzed based on the element values ​​and... The correlation coefficient between adjacent columns of the first data matrix is ​​calculated based on the number of rows. Based on this correlation coefficient, the first data matrix is ​​processed to remove reflected energy, resulting in the second data matrix. The element values ​​in the second data matrix are squared to obtain the third data matrix. Data below the sleeper in the third data matrix is ​​filtered to obtain the fourth data matrix. The mean or expected value of the element values ​​in each preset column of the fourth matrix is ​​calculated based on preset values ​​to obtain the fifth data matrix. The intensity of scattered energy within the railway ballast track detection section is obtained from the element values ​​in the fifth matrix. This method accurately extracts the scattered energy from railway ballast track detection, improving the efficiency of this process.

[0046] The following is a detailed explanation of the above-mentioned method for extracting scattered energy from railway ballast track.

[0047] Regarding S101 above, the element values ​​in the returned signal data matrix represent the amplitude of the returned signal. The number of columns in the returned signal data matrix is ​​equal to the number of the first sampling points over the sampling distance. The number of rows in the returned signal data matrix is ​​equal to the number of the second sampling points in the sampling channel. The number of the second sampling points is determined based on the required depth direction accuracy resolution of the track bed.

[0048] Regarding S102 above, determining the window size based on the sleeper spacing includes, for example, determining the window size based on a multiple of the sleeper spacing.

[0049] Specifically, for track bed detection, interference signals are mainly caused by impedance mismatch between the antenna and the medium, which is a type of standing wave interference. It exhibits isochronous characteristics in the data. Therefore, in one embodiment of this invention, interference removal is performed on the returned signal data matrix according to the window size to obtain a first data matrix, including:

[0050] Based on the window size, the following formula is used to remove interference from the returned signal data matrix, resulting in the first data matrix:

[0051]

[0052] Among them, y i (n) represents the value of the i-th column element in the first data matrix, x i (n) represents the value of the i-th column element in the returned signal data matrix, Q represents the window size, and x k (n) represents the value of the element in the k-th column of the returned signal data matrix.

[0053] Regarding S103 above, for track bed detection, the layer reflection generally has a certain length, the reflection waveforms of data from adjacent sampling channels are correlated, while the scattered energy is random. By analyzing the cross-correlation function of adjacent channels and suppressing the highly correlated reflection energy in the data, most of the reflection energy can be removed and the proportion of scattered energy can be increased.

[0054] Therefore, in one embodiment of the present invention, the correlation coefficient between two adjacent columns of data in the first data matrix is ​​calculated based on the element values ​​in the first data matrix and the number of rows in the first data matrix. For example, this includes: calculating the correlation coefficient between two adjacent columns of data in the first data matrix using the following formula based on the element values ​​in the first data matrix and the number of rows in the first data matrix:

[0055]

[0056] Where N is the number of second sampling points, r yiyi+1 Let y be the correlation coefficient between the values ​​of the i-th column and the (i+1)-th column of the first data matrix.i (n) represents the value of the i-th column element in the first data matrix, y i+1 (n) represents the value of the (i+1)th column element in the first data presentation.

[0057] Regarding S104 above, based on the correlation coefficient between two adjacent columns of data in the first data matrix, the first data matrix is ​​processed to remove reflected energy, resulting in a second data matrix. This process includes, for example, subtracting two columns of element values ​​in the first data matrix whose correlation coefficient is greater than a preset value to obtain a first new column of element values; replacing the two columns of element values ​​in the first data matrix with the first new column of element values ​​to obtain the second data matrix. For example, when the correlation coefficient between two adjacent sampling channels (i.e., two adjacent columns of element values) is greater than 0.8 (a preset value), the reflected energy waveforms of the two adjacent channels are similar. By subtracting, reflected energy is removed, highlighting the proportion of scattered energy.

[0058] For the above S105, the energy can be calculated by squaring, calculating the Hilbert follow-energy, calculating the envelope, and calculating the absolute value of the amplitude, thus obtaining the third data matrix.

[0059] Regarding S106 above, different precision resolutions will result in different row numbers in the third data matrix corresponding to the data below the sleeper. Therefore, in one embodiment of the present invention, the matrix data below the sleeper in the third data matrix is ​​extracted and filtered to obtain a fourth data matrix. For example, this includes: determining the number of matrix rows corresponding to the data below the sleeper from the third data matrix according to the precision resolution; extracting the matrix data below the sleeper from the third data matrix according to the number of matrix rows corresponding to the data below the sleeper; filtering the matrix data below the sleeper using an FIR filter or an IIR filter to filter out low-frequency signal data and high-frequency signal data in the matrix data below the sleeper to obtain a fourth data matrix; wherein, the filtering process includes at least one of bandpass, low-pass, and high-pass filters.

[0060] Regarding the aforementioned S107, the scattered energy exhibits a certain degree of randomness. By statistically analyzing the scattered energy of the track bed within a certain section, its mean and expected values ​​can be used to determine the intensity of the scattered energy within that section. Therefore, in one embodiment of the present invention, based on preset values, the mean or expected value of the element values ​​of each preset value column in the fourth matrix is ​​calculated to obtain the fifth data matrix. This includes: calculating the mean of the element values ​​of each preset value column in the fourth matrix based on preset values ​​to obtain a second new element value column; replacing each preset value column with the second new element value column corresponding to each preset value column to obtain the fifth data matrix; or calculating the expected value of the element values ​​of each preset value column in the fourth matrix based on preset values ​​to obtain a third new element value column; replacing each preset value column with the third new element value column corresponding to each preset value column to obtain the fifth data matrix.

[0061] To further facilitate understanding of the method for extracting scattered energy from railway ballast track in this invention, the following description, in conjunction with specific examples, illustrates the method for extracting scattered energy from railway ballast track in this invention.

[0062] For example, taking a 30m test track as an example, the track includes a 1-10m clean area, a 10-20m upper clean and lower dirty area, and a 20-30m dirty area. The track result diagram is shown in Figure 2. After watering the area to simulate natural rainfall, the track bed environment includes the effects of rainfall and stratification. The 10-20m stratified area contains interfaces with electrical abrupt changes. The return signal measured by ground-penetrating radar is shown in Figure 3(a). The three areas on the grayscale map cannot be distinguished by the return signal. Interference removal processing is performed on the data in Figure 3(a), and the result is shown in Figure 3(b). The result shows that the standing wave interference is removed, the deep information of the data is highlighted, and the overall signal-to-noise ratio is enhanced. Cross-correlation processing is performed on the data in Figure 3(b) to remove the reflection information in the data, and the result is shown in Figure 3(c). The small horizontal interlayer information in the data is suppressed, and most of the reflection information is removed. Energy calculation is performed on the result in Figure 3(c), and the information above the sleepers is extracted, resulting in the result shown in Figure 3(d). The results in Figure 3(d) are statistically analyzed in segments. The statistical distance is 1m. The average energy within the region is calculated to obtain the scattered energy results within the segment.

[0063] This invention is applicable to track bed detection in sections where electrical properties change abruptly due to rainfall, water abundance, or abrupt changes in strata. It improves the accuracy of detection during the rainy season and expands the applicability of the detection technology without causing additional problems. Furthermore, this invention avoids complex time-frequency analysis by using time-domain energy calculation. The algorithm is efficient and has low hardware dependence. The most time-consuming step in the processing occurs during the data transfer from disk to memory. With an SSD, processing 2000km of data takes approximately 1 hour.

[0064] This invention also provides a device for extracting scattered energy from railway ballast track, as described in the following embodiments. Since the principle by which this device solves the problem is similar to the method for extracting scattered energy from railway ballast track, the implementation of this device can refer to the implementation of the method for extracting scattered energy from railway ballast track, and will not be repeated here.

[0065] As shown in Figure 4, a railway ballast track bed scattering energy extraction device provided in an embodiment of the present invention includes:

[0066] The data acquisition module 401 is used to send electromagnetic wave signals using a ground-penetrating radar, receive the return signals after the electromagnetic wave signals are reflected back by the track, and obtain a return signal data matrix. The element values ​​in the return signal data matrix represent the amplitude of the return signal. The number of columns in the return signal data matrix is ​​equal to the number of the first sampling points over the sampling distance, and the number of rows in the return signal data matrix is ​​equal to the number of the second sampling points in the sampling channel. The number of the second sampling points is determined according to the required depth direction accuracy resolution of the track bed being inspected.

[0067] The first processing module 402 is used to determine the window size according to the sleeper spacing, and to remove interference from the returned signal data matrix according to the window size to obtain the first data matrix.

[0068] The second processing module 403 is used to calculate the correlation coefficient between two adjacent columns of data in the first data matrix based on the element values ​​in the first data matrix and the number of rows in the first data matrix.

[0069] The third processing module 404 is used to perform reflection energy removal processing on the first data matrix based on the correlation coefficient between two adjacent columns of data in the first data matrix to obtain the second data matrix;

[0070] The fourth processing module 405 is used to square the element values ​​in the second data matrix to obtain the third data matrix;

[0071] The fifth processing module 406 is used to extract the matrix data below the sleeper in the third data matrix and perform filtering to obtain the fourth data matrix;

[0072] The sixth processing module 407 is used to calculate the mean or expected value of the element values ​​of each preset value column in the fourth matrix according to the preset values ​​to obtain the fifth data matrix, and to obtain the intensity of scattered energy in the railway ballast track detection section according to the element values ​​in the fifth data matrix.

[0073] In one possible implementation, the first processing module is specifically used to perform interference removal on the returned signal data matrix according to the window size using the following formula to obtain a first data matrix:

[0074]

[0075] Among them, y i (n) represents the value of the i-th column element in the first data matrix, x i (n) represents the value of the i-th column element in the returned signal data matrix, Q represents the window size, and x k (n) represents the value of the element in the k-th column of the returned signal data matrix.

[0076] In one possible implementation, the second processing module is specifically configured to calculate the correlation coefficient between adjacent columns of data in the first data matrix using the following formula, based on the element values ​​in the first data matrix and the number of rows in the first data matrix:

[0077]

[0078] Where N is the number of second sampling points, r yiyi+1 Let y be the correlation coefficient between the values ​​of the i-th column and the (i+1)-th column of the first data matrix. i (n) represents the value of the i-th column element in the first data matrix, y i+1 (n) represents the value of the (i+1)th column element in the first data presentation.

[0079] In one possible implementation, the third processing module is specifically used to subtract two columns of element values ​​in the first data matrix whose correlation coefficient is greater than a preset coefficient value to obtain a first new column of element values; and to replace the two columns of element values ​​in the first data matrix with the first new column of element values ​​to obtain a second data matrix.

[0080] In one possible implementation, the fifth processing module is specifically used to determine the number of matrix rows corresponding to the data below the sleeper from the third data matrix according to the resolution; extract the matrix data below the sleeper from the third data matrix according to the number of matrix rows corresponding to the data below the sleeper; perform filtering processing on the matrix data below the sleeper using an FIR filter or an IIR filter to filter out low-frequency signal data and high-frequency signal data in the matrix data below the sleeper, thereby obtaining a fourth data matrix; wherein, the filtering processing includes at least one of bandpass, low-pass, and high-pass filters.

[0081] In one possible implementation, the sixth processing module is specifically used to: calculate the average of the element values ​​of each preset value column in the fourth matrix according to a preset value, to obtain a second new element value column; replace each preset value column with the second new element value column corresponding to each preset value column to obtain a fifth data matrix; or calculate the expected value of the element values ​​of each preset value column in the fourth matrix according to a preset value, to obtain a third new element value column; replace each preset value column with the third new element value column corresponding to each preset value column to obtain a fifth data matrix.

[0082] Based on the aforementioned inventive concept, as shown in Figure 5, the present invention also proposes a computer device 500, including a memory 510, a processor 520, and a computer program 530 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 530, it implements the aforementioned method for extracting scattered energy from railway ballast track detection.

[0083] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for extracting scattered energy from railway ballast track detection.

[0084] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for extracting scattered energy from railway ballast track detection.

[0085] In this embodiment of the invention, a ground-penetrating radar is used to transmit electromagnetic wave signals, and the return signals reflected back from the track are received to obtain a return signal data matrix. The element values ​​in the return signal data matrix represent the amplitude of the return signal. The number of columns in the return signal data matrix is ​​equal to the number of first sampling points over the sampling distance, and the number of rows in the return signal data matrix is ​​equal to the number of second sampling points in the sampling channel. The number of second sampling points is determined based on the required depth-direction accuracy resolution for detecting the track bed. The window size is determined based on the sleeper spacing, and interference is removed from the return signal data matrix based on the window size to obtain a first data matrix. The first data matrix is ​​then analyzed based on the element values ​​and... The correlation coefficient between adjacent columns of the first data matrix is ​​calculated based on the number of rows. Based on this correlation coefficient, the first data matrix is ​​processed to remove reflected energy, resulting in the second data matrix. The element values ​​in the second data matrix are squared to obtain the third data matrix. Data below the sleeper in the third data matrix is ​​filtered to obtain the fourth data matrix. The mean or expected value of the element values ​​in each preset column of the fourth matrix is ​​calculated based on preset values ​​to obtain the fifth data matrix. The intensity of scattered energy within the railway ballast track detection section is obtained from the element values ​​in the fifth matrix. This method accurately extracts the scattered energy from railway ballast track detection, improving the efficiency of this process.

[0086] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0090] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for extracting scattered energy from railway ballast track, characterized in that, include: A ground-penetrating radar (GPR) is used to transmit electromagnetic wave signals. The returned signals, reflected off the track, are received to obtain a return signal data matrix. In this matrix, each element represents the amplitude of the returned signal. The number of columns equals the number of first sampling points along the sampling distance, and the number of rows equals the number of second sampling points in the sampling channel. The number of second sampling points is determined based on the required depth-direction accuracy resolution for the track bed. The window size is determined based on the sleeper spacing, and interference is removed from the return signal data matrix according to this window size to obtain a first data matrix. The data is then processed based on the element values ​​of the first data matrix and the first data... The correlation coefficient between adjacent columns of data in the first data matrix is ​​calculated based on the number of rows in the matrix. Based on this correlation coefficient, the first data matrix is ​​processed to remove reflected energy, resulting in the second data matrix. The element values ​​in the second data matrix are squared to obtain the third data matrix. Data below the sleeper in the third data matrix is ​​extracted and filtered to obtain the fourth data matrix. The mean or expected value of the element values ​​in each preset column of the fourth matrix is ​​calculated based on preset values ​​to obtain the fifth data matrix. The intensity of scattered energy within the railway ballast track detection section is obtained based on the element values ​​in the fifth matrix.

2. The method for extracting scattered energy from railway ballast track as described in claim 1, characterized in that, The returned signal data matrix is ​​subjected to interference removal based on the window size to obtain a first data matrix, including: The returned signal data matrix is ​​subjected to interference removal using the following formula based on the window size to obtain the first data matrix: Among them, y i (n) represents the value of the i-th column element in the first data matrix, x i (n) represents the value of the i-th column element in the returned signal data matrix, Q represents the window size, and x k (n) represents the value of the element in the k-th column of the returned signal data matrix.

3. The method for extracting scattered energy from railway ballast track as described in claim 1, characterized in that, The correlation coefficient between two adjacent columns of data in the first data matrix is ​​calculated based on the element values ​​and the number of rows in the first data matrix. This includes calculating the correlation coefficient between two adjacent columns of data in the first data matrix using the following formula: Where N is the number of second sampling points, r yiyi+1 Let y be the correlation coefficient between the values ​​of the i-th column and the (i+1)-th column of the first data matrix. i (n) represents the value of the i-th column element in the first data matrix, y i+1 (n) represents the value of the (i+1)th column element in the first data matrix.

4. The method for extracting scattered energy from railway ballast track as described in claim 1, characterized in that, Based on the correlation coefficient between two adjacent columns of data in the first data matrix, the first data matrix is ​​processed to remove reflected energy, resulting in a second data matrix. This process includes: subtracting two columns of element values ​​in the first data matrix whose correlation coefficient is greater than a preset coefficient value to obtain a first new element value column; and replacing the two columns of element values ​​in the first data matrix with the first new element value column to obtain the second data matrix.

5. The method for extracting scattered energy from railway ballast track as described in claim 1, characterized in that, The fourth data matrix is ​​obtained by filtering the matrix data below the sleeper in the third data matrix, including: determining the number of matrix rows corresponding to the data below the sleeper in the third data matrix according to the accuracy resolution; extracting the matrix data below the sleeper in the third data matrix according to the number of matrix rows corresponding to the data below the sleeper; filtering the matrix data below the sleeper using an FIR filter or an IIR filter to remove low-frequency signal data and high-frequency signal data in the matrix data below the sleeper, thus obtaining the fourth data matrix; wherein the filtering process includes at least one of bandpass, low-pass, and high-pass filters.

6. The method for extracting scattered energy from railway ballast track as described in claim 1, characterized in that, Based on preset values, the mean or expected value of the element values ​​in each preset value column of the fourth matrix is ​​calculated to obtain the fifth data matrix. This includes: calculating the mean of the element values ​​in each preset value column of the fourth matrix based on preset values ​​to obtain a second new element value column; replacing each preset value column with the second new element value column corresponding to each preset value column to obtain the fifth data matrix; or calculating the expected value of the element values ​​in each preset value column of the fourth matrix based on preset values ​​to obtain a third new element value column; replacing each preset value column with the third new element value column corresponding to each preset value column to obtain the fifth data matrix.

7. A device for extracting scattered energy from railway ballast track, characterized in that, include: The data acquisition module is used to send electromagnetic wave signals using ground-penetrating radar and receive the return signals after the electromagnetic wave signals are reflected back from the track, thus obtaining a return signal data matrix. In this matrix, the element values ​​represent the amplitude of the return signal; the number of columns in the return signal data matrix equals the number of first sampling points over the sampling distance; and the number of rows in the return signal data matrix equals the number of second sampling points in the sampling channel. The number of second sampling points is determined based on the required depth-direction accuracy resolution for detecting the track bed. The first processing module is used to determine the window size based on the sleeper spacing and to perform interference removal on the return signal data matrix according to the window size, obtaining a first data matrix. The second processing module is used to process the element values ​​in the first data matrix and the rows of the first data matrix... The first data matrix is ​​processed by a first processing module to calculate the correlation coefficient between two adjacent columns of data. The second data matrix is ​​processed by a third processing module to remove reflected energy from the first data matrix based on the correlation coefficient between two adjacent columns of data. The third data matrix is ​​processed by a fourth processing module to square the element values ​​in the second data matrix. The fourth data matrix is ​​processed by a fifth processing module to extract the matrix data below the sleeper from the third data matrix and filter it to obtain the fourth data matrix. The fifth data matrix is ​​processed by a sixth processing module to calculate the mean or expected value of the element values ​​in each preset value column of the fourth matrix based on preset values. The intensity of scattered energy in the railway ballast track detection section is obtained based on the element values ​​in the fifth data matrix.

8. The railway ballast track detection and scattering energy extraction device as described in claim 7, characterized in that, The first processing module is specifically used to remove interference from the returned signal data matrix according to the window size using the following formula, to obtain the first data matrix: Among them, y i (n) represents the value of the i-th column element in the first data matrix, x i (n) represents the value of the i-th column element in the returned signal data matrix, Q represents the window size, and x k (n) represents the value of the element in the k-th column of the returned signal data matrix.

9. The railway ballast track detection and scattering energy extraction device as described in claim 7, characterized in that, The second processing module is specifically used to calculate the correlation coefficient between two adjacent columns of data in the first data matrix based on the element values ​​in the first data matrix and the number of rows in the first data matrix using the following formula: Where N is the number of second sampling points, r yiyi+1 Let y be the correlation coefficient between the values ​​of the i-th column and the (i+1)-th column of the first data matrix. i (n) represents the value of the i-th column element in the first data matrix, y i+1 (n) represents the value of the (i+1)th column element in the first data matrix.

10. The railway ballast track detection and scattering energy extraction device as described in claim 7, characterized in that, The third processing module is specifically used to subtract the two columns of element values ​​in the first data matrix whose correlation coefficient is greater than a preset coefficient value to obtain a first new column of element values; and to replace the two columns of element values ​​in the first data matrix with the first new column of element values ​​to obtain a second data matrix.

11. The railway ballast track detection and scattered energy extraction device as described in claim 7, characterized in that, The fifth processing module is specifically used to determine the number of matrix rows corresponding to the data below the sleeper from the third data matrix according to the accuracy resolution; to extract the matrix data below the sleeper from the third data matrix according to the number of matrix rows corresponding to the data below the sleeper; to filter the matrix data below the sleeper using an FIR filter or an IIR filter to filter out low-frequency signal data and high-frequency signal data in the matrix data below the sleeper, thereby obtaining the fourth data matrix; wherein, the filtering process includes at least one of the following: bandpass, low-pass, and high-pass.

12. The railway ballast track detection and scattering energy extraction device as described in claim 7, characterized in that, The sixth processing module is specifically used to calculate the mean of the element values ​​of each preset value column in the fourth matrix according to the preset values, to obtain a second new element value column; replace each preset value column with the second new element value column corresponding to each preset value column to obtain a fifth data matrix; or calculate the expected value of the element values ​​of each preset value column in the fourth matrix according to the preset values, to obtain a third new element value column; replace each preset value column with the third new element value column corresponding to each preset value column to obtain a fifth data matrix.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

15. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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