Water seepage detection method for hydraulic building

Abstract

The invention discloses a hydraulic structure water seepage detection method, and belongs to the technical field of electromagnetic detection. The method comprises the following steps: firstly, carrying out gridding division on the surface of the hydraulic structure, transmitting the same electromagnetic wave to each grid, and recording a reflected wave signal and a signal round-trip time interval; then, calculating a medium characteristic coefficient and a medium centrifugal value according to a time interval, and constructing a medium centrifugal matrix; calculating an intensity centrifugal value according to the transmitted and reflected wave signals, and constructing an intensity centrifugal matrix; fourier transform is carried out on the reflected wave signal, a phase centrifugal value is calculated after grading processing, and a phase centrifugal matrix is constructed; and finally, performing fusion enhancement on the features of the three centrifugal matrixes by using a water seepage evaluation neural network, thereby obtaining a water seepage evaluation value of the hydraulic structure. Through multi-dimensional extraction of the water seepage characteristics, the precision of water seepage detection of the hydraulic structure is effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of electromagnetic detection, and in particular to a method for detecting water seepage in hydraulic structures. Background Art

[0002] With the acceleration of urbanization, hydraulic structures play an increasingly important role in infrastructure construction. However, hydraulic structures often face water seepage problems during use, which not only affects their structural safety, but may also cause damage to the surrounding environment. Traditional water seepage detection methods mostly rely on manual inspections, but the water seepage location is usually internal, and it is difficult to detect internal water seepage using manual inspections. In addition, existing non-destructive detection technologies, such as ultrasonic testing, can identify water seepage problems to a certain extent, but there are still some technical problems in their application.

[0003] Existing ultrasonic detection technology mainly determines the defects and water seepage inside the structure by emitting ultrasonic signals and analyzing their reflected waves. However, due to the complex materials and structures of hydraulic structures, ultrasonic signals are easily affected by many factors during propagation, such as the density, humidity and temperature changes of concrete. This causes the reflected ultrasonic waves to present different states for hydraulic structures in different situations, making it difficult to accurately extract the characteristics of water seepage, resulting in low accuracy in water seepage detection in hydraulic structures. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, a method for detecting water seepage in a hydraulic structure provided by the present invention solves the problem of low accuracy in detecting water seepage in a hydraulic structure in the prior art.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for detecting water seepage in hydraulic structures, comprising the following steps:

[0006] S1. Grid the surface of the hydraulic structure to obtain grids, emit the same electromagnetic wave in each grid, obtain the reflected wave signal of each grid, and record the round-trip time interval of the signal, where L is the number of grids in the horizontal direction and W is the number of grids in the vertical direction;

[0007] S2. Calculate the medium characteristic coefficient for each grid according to the signal round trip time interval, obtain the medium centrifugal value, and construct the medium centrifugal matrix;

[0008] S3, according to the electromagnetic wave emission and reflected wave signals of each grid, the intensity centrifugal value of each grid is calculated to construct an intensity centrifugal matrix;

[0009] S4, performing Fourier transform on the reflected wave signal of each grid to obtain an amplitude-phase pair sequence, performing hierarchical processing on the amplitude-phase pair sequence, calculating the phase centrifugal value, and constructing a phase centrifugal matrix;

[0010] S5. The seepage assessment neural network is used to fuse and enhance the two types of features of the medium centrifugal matrix, the intensity centrifugal matrix and the phase centrifugal matrix respectively to obtain the seepage assessment value of the hydraulic structure.

[0011] Further, S2 includes the following sub-steps:

[0012] S21. Calculate the dielectric characteristic coefficient for each grid according to the signal round trip time interval: , where ε i is the dielectric characteristic coefficient of the ith grid, c is the speed of light, d is the detection depth, △t i is the round trip time interval of the signal in the i-th grid;

[0013] S22, calculating the average value of the dielectric characteristic coefficients of each grid to obtain a dielectric characteristic mean value;

[0014] S23, calculating the medium centrifugal value of each grid according to the medium characteristic coefficient and the medium characteristic mean of each grid;

[0015] S24. Taking the medium centrifugal value of each grid as an element, a medium centrifugal matrix is ​​constructed according to the grid arrangement.

[0016] Furthermore, the formula for calculating the medium centrifugal value in S23 is: , where η ε,i is the medium centrifugal value of the i-th grid, ε c is the mean value of the medium characteristic, ε max is the maximum dielectric characteristic coefficient of each grid, and i is a positive integer.

[0017] Furthermore, S3 includes the following sub-steps:

[0018] S31, performing time domain discrete sampling on the transmitted electromagnetic wave and the reflected wave signals respectively to obtain a transmitted electromagnetic wave time domain sequence and a reflected wave time domain sequence;

[0019] S32, respectively calculating the mean of the time domain sequence of the transmitted electromagnetic wave and the time domain sequence of the reflected wave to obtain the mean value of the transmitted intensity and the mean value of the reflected intensity;

[0020] S33, taking the ratio of the average emission intensity to the average reflection intensity of the same grid as the intensity ratio of the grid;

[0021] S34, calculating a first intensity centrifugal component according to the intensity ratio of each grid;

[0022] S35, calculating the standard deviation of the reflected wave time domain sequence to obtain the reflection intensity standard deviation;

[0023] S36, calculating the second intensity centrifugal component according to the reflection intensity standard deviation of each grid;

[0024] S37, weighting the first intensity centrifugal component and the second intensity centrifugal component to obtain an intensity centrifugal value;

[0025] S38. Taking the intensity centrifugal value of each grid as an element, and constructing an intensity centrifugal matrix according to the grid arrangement.

[0026] Furthermore, the formula for calculating the first intensity centrifugal component in S34 is: , where η θ,i is the first intensity centrifugal component of the i-th grid, θ i is the intensity ratio of the i-th grid, θ max is the maximum intensity ratio of each grid, i is a positive integer;

[0027] The formula for calculating the second intensity centrifugal component in S36 is: , where η σ,i is the second intensity centrifugal component of the i-th grid, σ i is the standard deviation of the reflection intensity of the ith grid, σ max is the standard deviation of the maximum reflection intensity of each grid.

[0028] Further, S4 includes the following sub-steps:

[0029] S41, performing Fourier transform on the reflected wave signal of each grid, arranging them from large to small in amplitude, and obtaining an amplitude-phase pair sequence;

[0030] S42, dividing the amplitude-phase pair sequence into three parts, obtaining a primary amplitude-phase pair subsequence, a secondary amplitude-phase pair subsequence, and a tertiary amplitude-phase pair subsequence;

[0031] S43, adding the phases in the amplitude-phase pair subsequences of each level to obtain a first-level total phase, a second-level total phase, and a third-level total phase;

[0032] S44, calculating the phase centrifugal component of the same level according to the total phase of the same level of each grid;

[0033] S45, weighting the first-order, second-order, and third-order phase centrifugal components to obtain a phase centrifugal value;

[0034] S46. The phase centrifugal value of each grid is used as an element, and a phase centrifugal matrix is ​​constructed according to the grid arrangement.

[0035] Furthermore, the formula for calculating the phase centrifugal component in S44 is: , where η φ,i is the phase centrifugal component of the same level of the i-th grid, φ i is the total phase of the same level of the i-th grid, φ max is the maximum total phase selected from the same level of each grid, and i is a positive integer.

[0036] Further, the water seepage assessment neural network in S5 includes: a first grid feature extraction unit, a second grid feature extraction unit, a third grid feature extraction unit, a first type of grid feature fusion unit, a second type of grid feature fusion unit, a first activation function unit, a second activation function unit and a weighting unit;

[0037] The input end of the first grid feature extraction unit is used to input the medium centrifugal matrix; the input end of the second grid feature extraction unit is used to input the intensity centrifugal matrix; the input end of the third grid feature extraction unit is used to input the phase centrifugal matrix;

[0038] The input end of the first type of grid feature fusion unit is respectively connected to the first output end of the first grid feature extraction unit, the first output end of the second grid feature extraction unit, and the first output end of the third grid feature extraction unit;

[0039] The input end of the second type of grid feature fusion unit is respectively connected to the second output end of the first grid feature extraction unit, the second output end of the second grid feature extraction unit, and the second output end of the third grid feature extraction unit;

[0040] The input end of the first activation function unit is connected to the output end of the first type of grid feature fusion unit; the input end of the second activation function unit is connected to the output end of the second type of grid feature fusion unit;

[0041] The input end of the weighting unit is connected to the output end of the first activation function unit and the output end of the second activation function unit respectively, and the output end thereof serves as the output end of the water seepage assessment neural network.

[0042] Furthermore, the expressions of the three grid feature extraction units are: , , where H Max is the first type of grid feature, H Avg is the second type of grid feature, MaxPool is the maximum pooling layer, AvgPool is the average pooling layer, Conv is the convolution operation, X in It is the input matrix of the mesh feature extraction unit.

[0043] Further, the first type of grid feature fusion unit is used to perform Hadamard product multiplication processing on the first type of grid features output by the first grid feature extraction unit, the first type of grid features output by the second grid feature extraction unit, and the first type of grid features output by the third grid feature extraction unit to obtain a first fused grid feature;

[0044] The second type grid feature fusion unit is used to perform Hadamard product multiplication on the second type grid features output by the first grid feature extraction unit, the second type grid features output by the second grid feature extraction unit, and the second type grid features output by the third grid feature extraction unit to obtain second fused grid features.

[0045] The beneficial effects of the present invention are:

[0046] 1. The present invention divides the surface of the hydraulic structure into grids to achieve zoned detection. At the same time, for the same hydraulic structure, the structure and material are the same. After the present invention divides it into multiple grids, it is convenient to compare the reflection signals between the multiple grids, and obtain the medium centrifugal value, intensity centrifugal value and phase centrifugal value, highlighting the characteristics of water seepage and improving the detection accuracy of water seepage in hydraulic structures.

[0047] 2. Calculate the medium characteristic coefficient based on the signal round-trip time interval and construct the medium centrifugal matrix. In this way, the medium characteristics of each grid area can be quantified. Then, according to the electromagnetic wave emission and reflection wave signals of each grid, calculate the intensity centrifugal value, which reflects the intensity characteristics of the electromagnetic wave in different grid areas. Finally, calculate the phase centrifugal value. The change in phase can reflect the subtle changes inside the building structure and is closely related to the water seepage situation.

[0048] 3. The present invention adopts a water seepage assessment neural network to fuse and enhance the two types of features of the medium centrifugal matrix, the intensity centrifugal matrix and the phase centrifugal matrix respectively, to extract features from multiple dimensions, and to perform feature fusion enhancement to improve the accuracy of water seepage detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for detecting water seepage in hydraulic structures;

[0050] Figure 2 Schematic diagram of the structure of the water seepage assessment neural network. DETAILED DESCRIPTION

[0051] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0052] like Figure 1 As shown, a method for detecting water seepage in a hydraulic structure comprises the following steps:

[0053] S1. Grid the surface of the hydraulic structure to obtain grids, emit the same electromagnetic wave in each grid, obtain the reflected wave signal of each grid, and record the round-trip time interval of the signal, where L is the number of grids in the horizontal direction and W is the number of grids in the vertical direction;

[0054] S2. Calculate the medium characteristic coefficient for each grid according to the signal round trip time interval, obtain the medium centrifugal value, and construct the medium centrifugal matrix;

[0055] S3, according to the electromagnetic wave emission and reflected wave signals of each grid, the intensity centrifugal value of each grid is calculated to construct an intensity centrifugal matrix;

[0056] S4, performing Fourier transform on the reflected wave signal of each grid to obtain an amplitude-phase pair sequence, performing hierarchical processing on the amplitude-phase pair sequence, calculating the phase centrifugal value, and constructing a phase centrifugal matrix;

[0057] S5. The seepage assessment neural network is used to fuse and enhance the two types of features of the medium centrifugal matrix, the intensity centrifugal matrix and the phase centrifugal matrix respectively to obtain the seepage assessment value of the hydraulic structure.

[0058] For example, taking an arch dam with a width of 3 meters and a length of 50 meters as an example, it can be divided into 0.5-meter lengths in the width direction and 0.5-meter lengths in the length direction, and the result is grids, each grid size is .

[0059] In this embodiment, when the frequency of the electromagnetic wave is set to 1 GHz, the detection depth is about 5 meters.

[0060] In this embodiment, S2 includes the following sub-steps:

[0061] S21. Calculate the dielectric characteristic coefficient for each grid according to the signal round trip time interval: , where ε i is the dielectric characteristic coefficient of the ith grid, c is the speed of light, d is the detection depth, △t i is the round trip time interval of the signal in the i-th grid;

[0062] S22, calculating the average value of the dielectric characteristic coefficients of each grid to obtain a dielectric characteristic mean value;

[0063] S23, calculating the medium centrifugal value of each grid according to the medium characteristic coefficient and the medium characteristic mean of each grid;

[0064] S24. Taking the medium centrifugal value of each grid as an element, a medium centrifugal matrix is ​​constructed according to the grid arrangement.

[0065] In this embodiment, the formula for calculating the medium centrifugal value in S23 is: , where η ε,i is the medium centrifugal value of the i-th grid, ε c is the mean value of the medium characteristic, ε max is the maximum dielectric characteristic coefficient of each grid, and i is a positive integer.

[0066] In the present invention, the signal round trip time interval is the interval time from transmitting the electromagnetic wave to receiving the reflected wave, that is, the propagation time of the wave.

[0067] The present invention calculates the medium characteristic coefficient for each grid to reflect the medium characteristics of each grid area, takes the average of each medium characteristic coefficient to reflect the overall medium characteristics, and obtains the medium centrifugal value of the grid according to the deviation of the medium characteristic coefficient of each grid from the medium characteristic mean to reflect the deviation of the medium characteristics of the grid.

[0068] The dielectric property coefficient of water is about 20 times that of dry concrete. Therefore, when water seepage exists in hydraulic structures, its dielectric property coefficient is larger than that of impermeable hydraulic structures.

[0069] In this embodiment, S3 includes the following sub-steps:

[0070] S31, performing time domain discrete sampling on the transmitted electromagnetic wave and the reflected wave signals respectively to obtain a transmitted electromagnetic wave time domain sequence and a reflected wave time domain sequence;

[0071] S32, respectively calculating the mean of the time domain sequence of the transmitted electromagnetic wave and the time domain sequence of the reflected wave to obtain the mean value of the transmitted intensity and the mean value of the reflected intensity;

[0072] S33, taking the ratio of the average emission intensity to the average reflection intensity of the same grid as the intensity ratio of the grid;

[0073] S34, calculating a first intensity centrifugal component according to the intensity ratio of each grid;

[0074] S35, calculating the standard deviation of the reflected wave time domain sequence to obtain the reflection intensity standard deviation;

[0075] S36, calculating the second intensity centrifugal component according to the reflection intensity standard deviation of each grid;

[0076] S37, weighting the first intensity centrifugal component and the second intensity centrifugal component to obtain an intensity centrifugal value;

[0077] S38. Taking the intensity centrifugal value of each grid as an element, and constructing an intensity centrifugal matrix according to the grid arrangement.

[0078] In this embodiment, the formula for calculating the first intensity centrifugal component in S34 is: , where η θ,i is the first intensity centrifugal component of the i-th grid, θ i is the intensity ratio of the i-th grid, θ max is the maximum intensity ratio of each grid, and i is a positive integer.

[0079] When there is water seepage in hydraulic structures, the presence of moisture will cause greater attenuation of electromagnetic waves during propagation. Therefore, the present invention takes the ratio of the mean emission intensity to the mean reflection intensity of the same grid as the intensity ratio. When there is water seepage in the grid, the intensity ratio is greater. The deviation of each intensity ratio from the mean intensity ratio is calculated to obtain the first intensity centrifugal component, highlighting the water seepage characteristics.

[0080] The formula for calculating the second intensity centrifugal component in S36 is: , where η σ,i is the second intensity centrifugal component of the i-th grid, σ i is the standard deviation of the reflection intensity of the ith grid, σ max is the standard deviation of the maximum reflection intensity of each grid.

[0081] When water seepage occurs in hydraulic structures, the medium becomes uneven, and electromagnetic waves experience different propagation speeds and attenuation during propagation, resulting in distortion of the waveform of the reflected wave. Therefore, the present invention calculates the standard deviation to reflect the distortion, calculates the deviation of the reflection intensity standard deviation from the mean, obtains the second intensity centrifugal component, and highlights the water seepage characteristics.

[0082] In this embodiment, S4 includes the following sub-steps:

[0083] S41, performing Fourier transform on the reflected wave signal of each grid, arranging them from large to small in amplitude, and obtaining an amplitude-phase pair sequence;

[0084] S42, dividing the amplitude-phase pair sequence into three parts, obtaining a primary amplitude-phase pair subsequence, a secondary amplitude-phase pair subsequence, and a tertiary amplitude-phase pair subsequence;

[0085] S43, adding the phases in the amplitude-phase pair subsequences of each level to obtain a first-level total phase, a second-level total phase, and a third-level total phase;

[0086] S44, calculating the phase centrifugal component of the same level according to the total phase of the same level of each grid;

[0087] S45, weighting the first-order, second-order, and third-order phase centrifugal components to obtain a phase centrifugal value;

[0088] S46. The phase centrifugal value of each grid is used as an element, and a phase centrifugal matrix is ​​constructed according to the grid arrangement.

[0089] After Fourier transform of the reflected wave signal, amplitude and phase information are obtained, which are arranged from large to small in amplitude to obtain an amplitude-phase pair sequence, and the amplitude-phase pair sequence is divided into three parts. For example, the first three amplitude-phase pairs are divided into the first level, the first three amplitude-phase pairs except the first level are divided into the second level, and the others are classified into the third level.

[0090] The dielectric properties of water can cause a phase delay in the electromagnetic wave at the interface, thus affecting the phase information of the reflected wave. This phase change can be used to infer the presence and extent of water seepage.

[0091] In this embodiment, the formula for calculating the phase centrifugal component in S44 is: , where η φ,i is the phase centrifugal component of the same level of the i-th grid, φ i is the total phase of the same level of the i-th grid, φ max is the maximum total phase selected from the same level of each grid, and i is a positive integer.

[0092] In the formula for calculating the phase centrifugal component, when dealing with the first-order total phase, φ i is the first-order total phase of the i-th grid, φ max is the maximum value selected from the first-level total phase of each grid; when processing the second-level total phase, φ i is the secondary total phase of the ith grid, φ max is the maximum value selected from the second-level total phase of each grid; when processing the third-level total phase, φ i is the total phase of the three levels of the i-th grid, φ max is the maximum value selected from the three-level total phase of each grid.

[0093] The presence of water will cause the phase delay of the reflected wave and affect the phase information of the reflected wave. The present invention hierarchically processes the amplitude-phase pair sequence to obtain the first level, the second level, and the third level. In the same level, according to the deviation between the total phase of each grid and the total phase mean of the level, the phase centrifugal component of the same level is calculated to reflect the change of the phase of different grid pairs.

[0094] In this embodiment, the weighting formula in S45 is: , where γ is the phase centrifugal value, γ1 is the first-order phase centrifugal component, γ2 is the second-order phase centrifugal component, and γ3 is the third-order phase centrifugal component.

[0095] In the present invention, the medium centrifugal value, the intensity centrifugal value and the phase centrifugal value are arranged in a grid arrangement manner to construct a corresponding matrix, so that the positions of all features of the same grid correspond.

[0096] like Figure 2 As shown, the water seepage assessment neural network in S5 includes: a first grid feature extraction unit, a second grid feature extraction unit, a third grid feature extraction unit, a first type of grid feature fusion unit, a second type of grid feature fusion unit, a first activation function unit, a second activation function unit and a weighting unit;

[0097] The input end of the first grid feature extraction unit is used to input the medium centrifugal matrix; the input end of the second grid feature extraction unit is used to input the intensity centrifugal matrix; the input end of the third grid feature extraction unit is used to input the phase centrifugal matrix;

[0098] The input end of the first type of grid feature fusion unit is respectively connected to the first output end of the first grid feature extraction unit, the first output end of the second grid feature extraction unit, and the first output end of the third grid feature extraction unit;

[0099] The input end of the second type of grid feature fusion unit is respectively connected to the second output end of the first grid feature extraction unit, the second output end of the second grid feature extraction unit, and the second output end of the third grid feature extraction unit;

[0100] The input end of the first activation function unit is connected to the output end of the first type of grid feature fusion unit; the input end of the second activation function unit is connected to the output end of the second type of grid feature fusion unit;

[0101] The input end of the weighting unit is connected to the output end of the first activation function unit and the output end of the second activation function unit respectively, and the output end thereof serves as the output end of the water seepage assessment neural network.

[0102] In this embodiment, the expressions of the three grid feature extraction units are: , , where H Max is the first type of grid feature, H Avg is the second type of grid feature, MaxPool is the maximum pooling layer, AvgPool is the average pooling layer, Conv is the convolution operation, X in It is the input matrix of the mesh feature extraction unit.

[0103] In this embodiment, the convolution operation can be performed using The convolutional layer.

[0104] In the present invention, two types of grid features are extracted by each grid feature extraction unit, and then the first type of grid feature fusion unit is used to process three first type grid features, and then the second type of grid feature fusion unit is used to process three second type grid features, so as to achieve mutual fusion and enhancement of features, and then the seepage component is obtained through the activation function unit, and the two seepage components are combined to obtain the seepage assessment value of the hydraulic structure.

[0105] In this embodiment, the first-class grid feature fusion unit is used to perform Hadamard product multiplication on the first-class grid features output by the first grid feature extraction unit, the first-class grid features output by the second grid feature extraction unit, and the first-class grid features output by the third grid feature extraction unit to obtain a first fused grid feature, that is, to multiply the three two-dimensional first-class grid features element-wise and fuse them into a two-dimensional first fused grid feature.

[0106] The second-category grid feature fusion unit is used to perform Hadamard product multiplication on the second-category grid features output by the first grid feature extraction unit, the second-category grid features output by the second grid feature extraction unit, and the second-category grid features output by the third grid feature extraction unit to obtain a second fused grid feature, that is, to multiply the three two-dimensional second-category grid features element-wise and fuse them into a two-dimensional second fused grid feature.

[0107] In this embodiment, the first activation function unit is expressed as: , the expression of the second activation function unit is: , where r1 is the first water seepage component, r2 is the second water seepage component, Tanh is the hyperbolic tangent function, and h Max,k is the kth eigenvalue in the first fused grid feature, h Avg,k is the kth eigenvalue in the second fused grid feature, ω Max,k h Max,k The weight of b Max,k h Max,k The bias, ω Avg,k h Avg,k The weight of b Avg,k h Avg,k , k is a positive integer, and K is the number of eigenvalues.

[0108] In the present invention, the weighting unit is used to weight the first water seepage component and the second water seepage component to obtain the water seepage assessment value of the hydraulic structure. The expression of the weighting unit is: , where y is the water seepage assessment value of hydraulic structures, ω r1 is the weight of the first water seepage component r1, ω r2 is the weight of the second water seepage component r2.

[0109] In this embodiment, the weights and biases in the water seepage assessment neural network are obtained by training using the existing gradient descent method.

[0110] The present invention divides the surface of a hydraulic structure into grids to achieve zoned detection. At the same time, for the same hydraulic structure, the structure and material are the same. After the present invention divides it into multiple grids, it is convenient to compare the reflection signals between the multiple grids, and obtain the medium centrifugal value, intensity centrifugal value and phase centrifugal value, highlighting the characteristics of water seepage and improving the detection accuracy of water seepage in hydraulic structures.

[0111] The medium characteristic coefficient is calculated based on the signal round-trip time interval and the medium centrifugal matrix is ​​constructed. In this way, the medium characteristics of each grid area can be quantified. Then, based on the electromagnetic wave emission and reflection wave signals of each grid, the intensity centrifugal value is calculated, which reflects the intensity characteristics of the electromagnetic wave in different grid areas. Finally, the phase centrifugal value is calculated. The change in phase can reflect the subtle changes inside the building structure and is closely related to the water seepage situation.

[0112] The present invention adopts a water seepage assessment neural network to fuse and enhance two types of features, namely, the medium centrifugal matrix, the intensity centrifugal matrix and the phase centrifugal matrix, respectively, to achieve feature extraction from multiple dimensions, and perform feature fusion enhancement to improve the accuracy of water seepage detection.

[0113] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for detecting water seepage in hydraulic structures, characterized in that: The following steps are involved: S1. Grid the surface of the hydraulic structure to obtain grids, emit the same electromagnetic wave in each grid, obtain the reflected wave signal of each grid, and record the round-trip time interval of the signal, where L is the number of grids in the horizontal direction and W is the number of grids in the vertical direction; S2. Calculate the medium characteristic coefficient for each grid according to the signal round trip time interval, obtain the medium centrifugal value, and construct the medium centrifugal matrix; S3, according to the electromagnetic wave emission and reflected wave signals of each grid, the intensity centrifugal value of each grid is calculated to construct an intensity centrifugal matrix; S4, performing Fourier transform on the reflected wave signal of each grid to obtain an amplitude-phase pair sequence, performing hierarchical processing on the amplitude-phase pair sequence, calculating the phase centrifugal value, and constructing a phase centrifugal matrix; S5. The seepage assessment neural network is used to fuse and enhance the two types of features of the medium centrifugal matrix, the intensity centrifugal matrix and the phase centrifugal matrix respectively to obtain the seepage assessment value of the hydraulic structure.

2. The method for detecting water seepage in hydraulic structures according to claim 1, characterized in that: The S2 comprises the following sub-steps: S21. Calculate the dielectric characteristic coefficient for each grid according to the signal round trip time interval: , where ε i is the dielectric characteristic coefficient of the ith grid, c is the speed of light, d is the detection depth, △t i is the round trip time interval of the signal in the i-th grid; S22, calculating the average value of the dielectric characteristic coefficients of each grid to obtain a dielectric characteristic mean value; S23, calculating the medium centrifugal value of each grid according to the medium characteristic coefficient and the medium characteristic mean of each grid; S24. Taking the medium centrifugal value of each grid as an element, a medium centrifugal matrix is ​​constructed according to the grid arrangement.

3. The method for detecting water seepage in hydraulic structures according to claim 2, characterized in that: The formula for calculating the medium centrifugal value in S23 is: , where η ε,i is the medium centrifugal value of the i-th grid, ε c is the mean value of the medium characteristic, ε max is the maximum dielectric characteristic coefficient of each grid, and i is a positive integer.

4. The method for detecting water seepage in hydraulic structures according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31, performing time domain discrete sampling on the transmitted electromagnetic wave and the reflected wave signals respectively to obtain a transmitted electromagnetic wave time domain sequence and a reflected wave time domain sequence; S32, respectively calculating the mean of the time domain sequence of the transmitted electromagnetic wave and the time domain sequence of the reflected wave to obtain the mean value of the transmitted intensity and the mean value of the reflected intensity; S33, taking the ratio of the average emission intensity to the average reflection intensity of the same grid as the intensity ratio of the grid; S34, calculating a first intensity centrifugal component according to the intensity ratio of each grid; S35, calculating the standard deviation of the reflected wave time domain sequence to obtain the reflection intensity standard deviation; S36, calculating the second intensity centrifugal component according to the reflection intensity standard deviation of each grid; S37, weighting the first intensity centrifugal component and the second intensity centrifugal component to obtain an intensity centrifugal value; S38. Taking the intensity centrifugal value of each grid as an element, and constructing an intensity centrifugal matrix according to the grid arrangement.

5. The method for detecting water seepage in hydraulic structures according to claim 4, characterized in that: The formula for calculating the first intensity centrifugal component in S34 is: , where η θ,i is the first intensity centrifugal component of the i-th grid, θ i is the intensity ratio of the i-th grid, θ max is the maximum intensity ratio of each grid, i is a positive integer; The formula for calculating the second intensity centrifugal component in S36 is: , where η σ,i is the second intensity centrifugal component of the i-th grid, σ i is the standard deviation of the reflection intensity of the ith grid, σ max is the standard deviation of the maximum reflection intensity of each grid.

6. The method for detecting water seepage in hydraulic structures according to claim 1, characterized in that: The S4 comprises the following sub-steps: S41, performing Fourier transform on the reflected wave signal of each grid, arranging them from large to small in amplitude, and obtaining an amplitude-phase pair sequence; S42, dividing the amplitude-phase pair sequence into three parts, obtaining a primary amplitude-phase pair subsequence, a secondary amplitude-phase pair subsequence, and a tertiary amplitude-phase pair subsequence; S43, adding the phases in the amplitude-phase pair subsequences of each level to obtain a first-level total phase, a second-level total phase, and a third-level total phase; S44, calculating the phase centrifugal component of the same level according to the total phase of the same level of each grid; S45, weighting the first-order, second-order, and third-order phase centrifugal components to obtain a phase centrifugal value; S46. The phase centrifugal value of each grid is used as an element, and a phase centrifugal matrix is ​​constructed according to the grid arrangement.

7. The method for detecting water seepage in hydraulic structures according to claim 6, characterized in that: The formula for calculating the phase centrifugal component in S44 is: , where η φ,i is the phase centrifugal component of the same level of the i-th grid, φ i is the total phase of the same level of the i-th grid, φ max is the maximum total phase selected from the same level of each grid, and i is a positive integer.

8. The method for detecting water seepage in hydraulic structures according to claim 1, characterized in that: The water seepage assessment neural network in S5 includes: a first grid feature extraction unit, a second grid feature extraction unit, a third grid feature extraction unit, a first type of grid feature fusion unit, a second type of grid feature fusion unit, a first activation function unit, a second activation function unit and a weighting unit; The input end of the first grid feature extraction unit is used to input the medium centrifugal matrix; the input end of the second grid feature extraction unit is used to input the intensity centrifugal matrix; the input end of the third grid feature extraction unit is used to input the phase centrifugal matrix; The input end of the first type of grid feature fusion unit is respectively connected to the first output end of the first grid feature extraction unit, the first output end of the second grid feature extraction unit, and the first output end of the third grid feature extraction unit; The input end of the second type of grid feature fusion unit is respectively connected to the second output end of the first grid feature extraction unit, the second output end of the second grid feature extraction unit, and the second output end of the third grid feature extraction unit; The input end of the first activation function unit is connected to the output end of the first type of grid feature fusion unit; the input end of the second activation function unit is connected to the output end of the second type of grid feature fusion unit; The input end of the weighting unit is connected to the output end of the first activation function unit and the output end of the second activation function unit respectively, and the output end thereof serves as the output end of the water seepage assessment neural network.

9. The method for detecting water seepage in hydraulic structures according to claim 8, characterized in that: The expressions of the three grid feature extraction units are: , , where H Max is the first type of grid feature, H Avg is the second type of grid feature, MaxPool is the maximum pooling layer, AvgPool is the average pooling layer, Conv is the convolution operation, X in It is the input matrix of the mesh feature extraction unit.

10. The method for detecting water seepage in hydraulic structures according to claim 8, characterized in that: The first-type grid feature fusion unit is used to perform Hadamard product multiplication processing on the first-type grid features output by the first grid feature extraction unit, the first-type grid features output by the second grid feature extraction unit, and the first-type grid features output by the third grid feature extraction unit to obtain a first fused grid feature; The second-type grid feature fusion unit is used to perform Hadamard product multiplication on the second-type grid features output by the first grid feature extraction unit, the second-type grid features output by the second grid feature extraction unit, and the second-type grid features output by the third grid feature extraction unit to obtain second fused grid features.

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