Electric power hidden facility detection method and device, storage medium and computer equipment

Abstract

The invention provides an electric power hidden facility detection method and device, a storage medium and computer equipment, and the method comprises the steps: carrying out the decomposition of a radar signal, obtaining a clutter matrix and a target matrix, then decomposing the clutter matrix into a first matrix and a second matrix, constructing a target optimization function through the first matrix, the second matrix and the target matrix, and obtaining a target optimization function; on one hand, the complexity of solving the optimization function can be reduced, and on the other hand, the dependence of the target optimization function on the regularization parameter can be reduced, so that the sensitivity of the target optimization function on the regularization parameter is reduced, and the accuracy of a subsequent solving result is improved; then determining a target matrix when the target optimization function reaches an optimization target, further determining a target radar signal according to the target matrix, finally performing backward projection imaging based on the target radar signal, and realizing imaging detection of the electric power hidden facility through the target radar signal after clutter suppression, thereby improving the imaging quality. Therefore, the detection precision of the electric power hidden facility is improved.

Description

Technical Field

[0001] The present application relates to the field of electric power detection technology, and in particular to a method, device, storage medium and computer equipment for detecting hidden electric power facilities. Background Art

[0002] Hidden power facilities are important power channels. How to accurately detect their distribution and operation is of great significance to the operation of the power system. Traditional methods for detecting hidden power facilities include excavation, acoustic detection, electromagnetic induction, etc., which have many limitations. Ground penetrating radar is an emerging non-destructive detection technology. It has good development prospects due to its advantages of fast detection speed, accurate positioning, and high imaging resolution. It is widely used in the field of hidden power facility detection.

[0003] The detection scene of hidden power targets is complex. In addition to fine sand and cover plates, there are usually gravel and other pipelines such as municipal pipelines and drainage pipes around the cables. The echo signal of the ground penetrating radar contains a lot of clutter interference, which is not easy to directly detect and identify hidden power targets. At present, strong waves such as direct waves and coupled waves can be filtered out by the mean filtering method. However, the suppression effect of clutter generated by other impurities in the detection scene is poor, which affects the imaging effect of radar scanning and leads to low accuracy in the detection of hidden power facilities. Summary of the invention

[0004] The purpose of the present application is to solve at least one of the above-mentioned technical defects, especially the technical defect that the prior art has poor suppression effect on the clutter generated by other impurity objects in the detection scene, thereby affecting the imaging effect of radar scanning and resulting in low accuracy in the detection of hidden power facilities.

[0005] In a first aspect, the present application provides a method for detecting hidden power facilities, the method comprising:

[0006] Acquire a radar signal, and decompose the radar signal to obtain a clutter matrix and a target matrix;

[0007] Decomposing the clutter matrix into a first matrix and a second matrix, and constructing a target optimization function according to the first matrix, the second matrix and the target matrix; wherein the target optimization function takes constraining the sparsity of the target matrix as much as possible and extracting effective signal components in the clutter matrix as optimization goals;

[0008] Solving the target optimization function to determine a target matrix when the target optimization function reaches the optimization target;

[0009] Reconstructing the signal according to the target matrix determined so that the target optimization function reaches the optimization target, so as to obtain the target radar signal;

[0010] Backward projection imaging is performed based on the target radar signal to perform imaging detection on hidden power facilities.

[0011] In one embodiment, the objective optimization function is expressed as:

[0012]

[0013] In the formula, is the first matrix, is the second matrix, represents the product of the first matrix and the second matrix, i.e., the clutter matrix, is the target matrix, is the noise matrix, Represents a radar signal, and is the regularization parameter, It means to sum the absolute values ​​of the 2-norms of the column vectors of the first matrix. It means to sum the absolute values ​​of the 2-norms of the column vectors of the transposed matrix of the second matrix. Represents the sum of the absolute values ​​of each element in the transposed matrix of the target matrix.

[0014] In one embodiment, the step of decomposing the clutter matrix into a first matrix and a second matrix comprises:

[0015] The clutter matrix is ​​grouped using factor group sparse regularization to determine a first matrix and a second matrix, wherein the product of the first matrix and the second matrix is ​​the clutter matrix.

[0016] In one embodiment, the step of performing backprojection imaging based on the target radar signal comprises:

[0017] For each imaging point in the target radar signal, determining the scattering responses of multiple apertures corresponding to the imaging point;

[0018] Determine the effective echo sequence of each aperture according to the scattering response of each aperture;

[0019] Determining the correlation coefficient of each aperture according to the effective echo sequence of each aperture, and weighting the scattering response of each aperture based on the correlation coefficient to enhance the reflected energy of the target area in the target radar signal;

[0020] Performing cross-correlation processing on the weighted scattering responses of each aperture to weaken the reflected energy of the non-target area in the target radar signal and determine the reflection intensity of the imaging point;

[0021] When the reflection intensity of each imaging point in the target radar signal is determined, a radar image is generated based on the reflection intensity of each imaging point.

[0022] In one embodiment, the step of performing cross-correlation processing on the weighted scattering responses of each aperture includes:

[0023] Determine the correlation between the weighted scattering responses of each aperture, and sum the correlation between the weighted scattering responses of each aperture to obtain the initial reflection intensity of the imaging point;

[0024] Determine the coherence factor of the imaging point according to the correlation between the weighted scattering responses of each aperture;

[0025] The initial reflection intensity is weighted by using the coherence factor to determine the reflection intensity of the imaging point.

[0026] In one embodiment, the step of determining the correlation coefficient of each aperture according to the effective echo sequence of each aperture includes:

[0027] Determine the effective echo sequence of the middle aperture among the apertures as the reference sequence;

[0028] The correlation coefficient between the effective echo sequence of each aperture and the reference sequence is calculated, and the correlation coefficient between the effective echo sequence of each aperture and the reference sequence is used as the correlation coefficient of the aperture.

[0029] In one embodiment, the step of determining the scattering responses of multiple apertures corresponding to the imaging point includes:

[0030] According to the coordinates of the imaging point and the coordinates of each aperture, the time delay vector of each aperture is calculated respectively;

[0031] Based on the time delay vector of each aperture, determining the A-scan signal received by each aperture;

[0032] According to the A-scan signals received at each aperture, a scattering response corresponding to each aperture is generated respectively.

[0033] In a second aspect, the present application provides a device for detecting hidden power facilities, the device comprising:

[0034] A signal acquisition module is used to acquire radar signals and decompose the radar signals to obtain a clutter matrix and a target matrix;

[0035] A matrix decomposition module, used for decomposing the clutter matrix into a first matrix and a second matrix, and constructing a target optimization function according to the first matrix, the second matrix and the target matrix; wherein the target optimization function takes constraining the sparsity of the target matrix as much as possible and extracting effective signal components in the clutter matrix as optimization goals;

[0036] A function solving module, used for solving the target optimization function to determine a target matrix when the target optimization function reaches the optimization target;

[0037] A signal reconstruction module, used to perform signal reconstruction according to the target matrix determined so that the target optimization function reaches the optimization target, so as to obtain a target radar signal;

[0038] The imaging detection module is used to perform back projection imaging based on the target radar signal to perform imaging detection on the hidden power facilities.

[0039] In a third aspect, the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method for detecting hidden power facilities as described in any of the above embodiments.

[0040] In a fourth aspect, the present application provides a computer device, comprising: one or more processors, and a memory;

[0041] The memory stores computer-readable instructions, and when the one or more processors execute the computer-readable instructions, the steps of the method for detecting hidden power facilities as described in any one of the above embodiments are performed.

[0042] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0043] The present application provides a method, device, storage medium and computer equipment for detecting hidden power facilities. The method includes: decomposing a radar signal to obtain a clutter matrix and a target matrix, then decomposing the clutter matrix into a first matrix and a second matrix, and constructing a target optimization function with the first matrix, the second matrix and the target matrix. On the one hand, the complexity of solving the optimization function can be reduced, and on the other hand, the dependence of the target optimization function on the regularization parameter can be reduced, thereby reducing the sensitivity of the target optimization function to the regularization parameter and improving the accuracy of the subsequent solution results. Then, the target matrix is ​​determined when the target optimization function reaches the optimization target, and then the target radar signal is determined according to the target matrix. Finally, back-projection imaging is performed based on the target radar signal, and imaging detection of hidden power facilities is achieved through the target radar signal after clutter suppression, thereby improving the imaging quality and improving the accuracy of hidden power facility detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 A schematic diagram of a flow chart of a method for detecting hidden power facilities provided in an embodiment of the present application;

[0046] Figure 2 A schematic flow chart of the steps of performing back projection imaging based on target radar signals provided in an embodiment of the present application;

[0047] Figure 3 A schematic flow chart of the steps of cross-correlation processing of the weighted scattering responses of each aperture provided in an embodiment of the present application;

[0048] Figure 4 A comparison chart of the detection of hidden power facilities based on the measured signal of the cable provided in the embodiment of the present application;

[0049] Figure 5 A comparison diagram of the detection of hidden power facilities based on cable simulation signals provided in an embodiment of the present application;

[0050] Figure 6 A schematic diagram of the structure of a hidden power facility detection device provided in an embodiment of the present application;

[0051] Figure 7 An internal structure diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] In one embodiment, the present application provides a method for detecting hidden power facilities, and the following embodiment is described by applying the method to a server. It can be understood that the method for detecting hidden power facilities can be executed by a single server or a server cluster composed of multiple servers, and the present application does not make specific restrictions on this.

[0054] like Figure 1 As shown, the present application provides a method for detecting hidden power facilities, the method comprising:

[0055] S101: Acquire a radar signal and decompose the radar signal to obtain a clutter matrix and a target matrix.

[0056] The radar signal refers to the signal scanned in the area of ​​the hidden power facilities. In this embodiment, the radar signal is a B-scan scanning signal, which can be obtained by non-destructive scanning by ground penetrating radar. The clutter matrix refers to the clutter signal components in the radar signal, and the target matrix refers to the main signal components in the radar signal.

[0057] In this step, when it is necessary to detect hidden power facilities, a radar scanning tool is used to scan and detect to collect radar signals, and then the radar signals are decomposed using the principal component analysis method to obtain a clutter matrix and a target matrix.

[0058] In one example, the radar signal can be decomposed using the Robust Principal Component Analysis (RPCA) method, which is more robust than other principal component analysis methods in the face of noise and outliers in the data.

[0059] S102: Decomposing the clutter matrix into a first matrix and a second matrix, and constructing a target optimization function according to the first matrix, the second matrix and the target matrix.

[0060] The objective optimization function aims to constrain the sparsity of the target matrix as much as possible and extract the effective signal components in the clutter matrix.

[0061] In this step, the clutter matrix is ​​decomposed into the product of the first matrix and the second matrix to determine the first matrix and the second matrix obtained by decomposition, and then the target optimization function is constructed based on the first matrix, the second matrix and the target matrix. When the target optimization function is constructed by replacing the clutter matrix with the first matrix and the second matrix, on the one hand, the complexity of solving the target optimization function can be reduced, and on the other hand, the dependence on the regularization parameter can be reduced by decomposition to reduce the sensitivity of the target optimization function to the regularization parameter, thereby improving the accuracy of the subsequent solution of the target optimization function. Specifically, the regularization parameter is used to control the weight of the sparse term to balance the low-rank and sparse components.

[0062] S103: Solving the target optimization function to determine a target matrix that enables the target optimization function to achieve the optimization target.

[0063] In this step, the target optimization function can be solved using a solution algorithm such as an alternating direction multiplier method, a singular value threshold method, or an incremental algorithm to find a target matrix, a first matrix, and a second matrix when the target optimization function reaches its optimization target. When the target optimization function reaches its optimization target, the target matrix in the target optimization function at this time is obtained.

[0064] S104: Perform signal reconstruction according to the target matrix determined so that the target optimization function reaches the optimization target, so as to obtain the target radar signal.

[0065] In this step, after obtaining the target matrix when the target optimization function reaches its optimization target, signal reconstruction is performed based on the target matrix to obtain the target radar signal.

[0066] Furthermore, the process of performing signal reconstruction based on the target matrix includes: performing signal recovery based on the target matrix, and filtering the target matrix after signal recovery to obtain the target radar signal.

[0067] S105: Perform back projection imaging based on the target radar signal to perform imaging detection on the hidden power facilities.

[0068] Among them, hidden power facilities refer to power equipment and systems buried inside buildings or underground, including but not limited to cable trenches, cable pipes, grounding systems, transformers, distribution boxes, switch cabinets, etc.

[0069] In this step, the target radar signal is generated based on the target matrix obtained after clutter suppression, and then back-projection imaging is performed based on the target radar signal. This can improve the final imaging effect and accurately reconstruct the distribution of hidden power facilities to complete the imaging detection of hidden power facilities.

[0070] Specifically, backprojection imaging refers to reconstructing a high-resolution image from echo signals acquired multiple times through backprojection technology.

[0071] The present application provides a method, device, storage medium and computer equipment for detecting hidden power facilities. The method includes: decomposing a radar signal to obtain a clutter matrix and a target matrix, then decomposing the clutter matrix into a first matrix and a second matrix, and constructing a target optimization function with the first matrix, the second matrix and the target matrix. On the one hand, the complexity of solving the optimization function can be reduced, and on the other hand, the dependence of the target optimization function on the regularization parameter can be reduced, thereby reducing the sensitivity of the target optimization function to the regularization parameter and improving the accuracy of the subsequent solution results. Then, the target matrix is ​​determined when the target optimization function reaches the optimization target, and then the target radar signal is determined according to the target matrix. Finally, back-projection imaging is performed based on the target radar signal, and imaging detection of hidden power facilities is achieved through the target radar signal after clutter suppression, thereby improving the imaging quality and improving the accuracy of hidden power facility detection.

[0072] In one embodiment, the objective optimization function is expressed as:

[0073]

[0074] In the formula, is the first matrix, is the second matrix, represents the product of the first matrix and the second matrix, i.e., the clutter matrix, is the target matrix, is the noise matrix, Represents a radar signal, and is the regularization parameter, It means to sum the absolute values ​​of the 2-norms of the column vectors of the first matrix. It means to sum the absolute values ​​of the 2-norms of the column vectors of the transposed matrix of the second matrix. Represents the sum of the absolute values ​​of each element in the transposed matrix of the target matrix.

[0075] Among them, the noise matrix is ​​used to simulate the noise interference in the radar signal.

[0076] Specifically, if the clutter matrix is ​​not decomposed into the first matrix and the second matrix, the optimization function is expressed as:

[0077]

[0078]

[0079] In the formula, represents the clutter matrix, represents the target matrix, represents the noise matrix, represents the rank of the clutter matrix, represents the zero norm of the target matrix, is a regularization parameter used to balance sparsity and low rank.

[0080] The optimization function described above has high complexity and is sensitive to the regularization parameter. Therefore, in this embodiment, the clutter matrix is ​​decomposed into a first matrix and a second matrix, and then the rank function of the original clutter matrix is ​​replaced by the Schatten p norm to reduce the complexity and speed up the convergence, and can further constrain the sparsity of the target matrix to achieve a better clutter suppression effect.

[0081] In one example, The calculation expression is:

[0082]

[0083]

[0084] In the formula, Represents the first matrix , express The 2-norm of express The index of the element in , Represents a column vector The elements.

[0085] Similarly, The calculation method can also be based on the above The same can be said for the calculation expression of .

[0086] In one embodiment, the step of decomposing the clutter matrix into a first matrix and a second matrix comprises:

[0087] The clutter matrix is ​​grouped using factor group sparse regularization to determine a first matrix and a second matrix, wherein the product of the first matrix and the second matrix is ​​the clutter matrix.

[0088] Among them, Factor Group Sparsity Regularization (FGSR) is a regularization strategy that aims to make the model exhibit sparsity on these groups by penalizing the parameters of certain groups in the model or function when processing data with multiple groups of features.

[0089] In this embodiment, the clutter matrix is ​​divided into two groups by using the principle of factor group sparse regularization to obtain a first matrix and a second matrix;

[0090] In one example, suppose the clutter matrix has the dimensions , when the clutter matrix Decompose into the first matrix and the second matrix The product of , the first matrix The dimension is , the first matrix can be expressed as:

[0091]

[0092] In the formula, Indicates the length Column vector of .

[0093] Second Matrix The dimension is , the second matrix can be expressed as:

[0094]

[0095] In the formula, Indicates the length is Column vector of .

[0096] It can be understood that by decomposing the clutter matrix into the first matrix and the second matrix, on the one hand, the complexity of solving the optimization function can be reduced, and on the other hand, the dependence of the target optimization function on the regularization parameter can be reduced, thereby reducing the sensitivity of the target optimization function to the regularization parameter and improving the accuracy of the subsequent solution results to improve the imaging quality.

[0097] like Figure 2 As shown, in one embodiment, the step of performing back projection imaging based on the target radar signal includes:

[0098] S201: For each imaging point in the target radar signal, determine the scattering responses of multiple apertures corresponding to the imaging point.

[0099] The scattering response refers to the scattering characteristics of the imaging point to the radar signal of the corresponding aperture, including but not limited to the intensity, direction, phase, etc. of the scattering.

[0100] In this step, for each imaging point in the target radar signal, the scattering response corresponding to the imaging point on each aperture is calculated by determining the time delay of the imaging point on each aperture.

[0101] S202: Determine the effective echo sequence of each aperture according to the scattering response of each aperture.

[0102] The effective echo sequence refers to the main energy concentration part in the scattering response.

[0103] In this step, a section of effective signal can be taken from the scattering response corresponding to the aperture of the imaging point with the time delay position as the center as the effective echo sequence of the aperture. The length of the intercepted signal (i.e., the length of the effective echo sequence) can be a preset value, or can be calculated and determined according to the sampling frequency, the transmission signal frequency, etc., and this application does not impose specific restrictions on this.

[0104] Specifically, the time delay position refers to a moment in the time period when the electromagnetic wave of the aperture travels from the aperture to the imaging point and then reflects back to the aperture.

[0105] In one example, the length of the effective echo sequence can be determined according to the following expression:

[0106]

[0107] In the formula, represents the sampling frequency, Indicates the frequency of the transmitted signal. Indicates rounding down.

[0108] S203: Determine the correlation coefficient of each aperture according to the effective echo sequence of each aperture, and weight the scattering response of each aperture based on the correlation coefficient to enhance the reflected energy of the target area in the target radar signal.

[0109] The correlation coefficient is an indicator used to measure the degree of linear correlation between two variables. The target area refers to the area in the target radar signal that mainly represents the power hidden facilities.

[0110] In this step, a reference sequence can be determined from the effective echo sequences of each aperture, and then the correlation coefficient between the effective echo sequence of each aperture and the reference sequence is calculated, and the correlation coefficient between the effective echo sequence of each aperture and the reference sequence is determined as the correlation coefficient of the aperture, and then the scattering response of the corresponding aperture is weighted based on the correlation coefficient of each aperture to obtain the time delay response. It can be understood that by using the correlation coefficient to weight the scattering response, a relatively high weight can be assigned to a relatively high correlation with the reference sequence, and a relatively low weight can be assigned to a relatively low correlation with the reference sequence. When the energy of the reference sequence itself is relatively large, the weight can be mainly assigned to the high-energy sequence, so that the energy of the side lobe and interference can be reduced to enhance the reflected energy of the target area.

[0111] S204: performing cross-correlation processing on the weighted scattering responses of each aperture to weaken the reflection energy of the non-target area in the target radar signal and determine the reflection intensity of the imaging point.

[0112] Among them, the non-target area refers to the area in the target radar signal that mainly represents non-power concealed facilities.

[0113] In this step, the cross-correlation back-projection (CBP) algorithm can be used to perform cross-correlation processing on the weighted scattering responses of each aperture. Since this algorithm will introduce invalid sidelobe interference, the coherence factor can be calculated and weighted with the processing result of the cross-correlation back-projection algorithm to suppress artifacts and interference.

[0114] S205: When the reflection intensity of each imaging point in the target radar signal is determined, a radar image is generated based on the reflection intensity of each imaging point.

[0115] It can be understood that through a series of processing, the reflected energy of the target area is enhanced and the reflected energy of the non-target area is weakened, thereby improving the quality of imaging and making the detection of hidden power facilities more accurate.

[0116] like Figure 3 As shown, in one embodiment, the step of performing cross-correlation processing on the weighted scattering responses of each aperture includes:

[0117] S301: Determine the correlation between each pair of weighted scattering responses of each aperture, and sum the correlation between each pair of weighted scattering responses of each aperture to obtain the initial reflection intensity of the imaging point.

[0118] In one example, the process of S301 can be implemented using the following expression:

[0119]

[0120] In the formula, represents the initial reflection intensity, Indicates The weighted scattering response of each aperture is Indicates The weighted scattering response of each aperture is represents the number of apertures, and The index representing the aperture.

[0121] S302: Determine the coherence factor of the imaging point according to the correlation between each pair of weighted scattering responses of each aperture.

[0122] Among them, the coherence factor (CF) is a measure used to describe the degree of linear correlation between two signals or data sequences.

[0123] In one example, the process of S302 can be implemented using the following expression:

[0124]

[0125] In the formula, represents the coherence factor, Indicates The weighted scattering response of each aperture is Indicates The weighted scattering response of each aperture is represents the number of apertures, and The index representing the aperture.

[0126] S303: weighting the initial reflection intensity using the coherence factor to determine the reflection intensity of the imaging point.

[0127] In one example, the process of S303 can be implemented using the following expression:

[0128]

[0129] In the formula, represents the reflection intensity, Indicates The weighted scattering response of each aperture is Indicates The weighted scattering response of each aperture is represents the number of apertures, and represents the index of the aperture, Indicates the initial reflection intensity.

[0130] It can be understood that the coherence factor can calculate the correlation of each product term in the above formula, form the correlation processing of CF-CBP cascade, and weaken the reflected energy in the non-target area, so as to achieve high-resolution imaging of hidden power facilities and suppress imaging artifacts.

[0131] In one embodiment, the step of determining the correlation coefficient of each aperture according to the effective echo sequence of each aperture includes:

[0132] S1: Determine the effective echo sequence of the middle aperture among the apertures as the reference sequence.

[0133] S2: Calculate the correlation coefficient between the effective echo sequence of each aperture and the reference sequence, and use the correlation coefficient between the effective echo sequence of each aperture and the reference sequence as the correlation coefficient of the aperture.

[0134] In this embodiment, since the signal strength of the effective echo sequence of the middle aperture is the largest among all the effective echo sequences, the effective echo sequence of the middle aperture is used as the reference sequence so that a larger weight can be assigned to the effective echo sequence with larger energy, thereby enhancing the reflected energy of the target area in the target radar signal.

[0135] In one example, the correlation coefficient may be calculated using the expression of the Pearson correlation coefficient, which is as follows:

[0136]

[0137] In the formula, represents the correlation coefficient, Represents a valid echo sequence, Represents the reference sequence.

[0138] In one embodiment, the step of determining the scattering responses of multiple apertures corresponding to the imaging point includes:

[0139] S1: Calculate the time delay vector of each aperture according to the coordinates of the imaging point and the coordinates of each aperture.

[0140] S2: Based on the time delay vector of each aperture, determine the A-type scan signal received by each aperture.

[0141] S3: Generate a scattering response corresponding to each aperture according to the A-scan signal received by each aperture.

[0142] The time delay vector refers to the time it takes for the electromagnetic wave of the aperture to travel from the aperture to the imaging point and then reflect back to the aperture. The A-scan signal refers to the A-Scan signal, which is a single-channel waveform.

[0143] In this embodiment, the process of respectively calculating the time delay vector of each aperture according to the coordinates of the imaging point and the coordinates of each aperture can be expressed as follows:

[0144]

[0145] In the formula, Indicates The time delay vector of the aperture, and Respectively represent the horizontal and vertical coordinates of the imaging point, and Respectively represent The horizontal and vertical coordinates of the aperture. and represent the relative permittivity and relative conductivity of the soil respectively.

[0146] Next, based on the time delay vector of each aperture, the A-type scan signal received by each aperture is determined, which can be expressed as:

[0147]

[0148] In the formula, Indicates The time delay vector of the aperture, Indicates The A-scan signal received by each aperture.

[0149] In one example, if Figure 4 and Figure 5 As shown, Figure 4 and Figure 5 The radar scanning signal processing of hidden power facilities taking cables as an example is demonstrated, and the signal processing flow proposed in this scheme is verified using the measured cable signal and the cable simulation signal based on GprMax software. In order to verify the superiority of this application, the clutter suppression processing algorithm proposed in this application is compared with the traditional average cancellation method, and the imaging algorithm proposed in this application is compared with the traditional BP (Back-Projection) imaging algorithm and CBP imaging algorithm.

[0150] exist Figure 4 middle, Figure 4 (a) is the measured signal of the cable, Figure 4 (b) is the effect of using the average cancellation method to suppress clutter on the measured cable signal. Figure 4 (c) is a diagram showing the effect of using the power concealed facility detection method proposed in this application to suppress clutter on the measured cable signal. Figure 4 (d) is the BP algorithm imaging result of the measured cable signal. Figure 4 (e) is the CBP algorithm imaging result of the measured cable signal. Figure 4 (f) is the improved BP algorithm imaging result of the measured cable signal. The improved BP algorithm imaging result described here is the imaging method used in the power hidden facility detection method proposed in this application. It can be understood that Figure 4 (a) is the subject of processing, Figure 4 (b) Figure 4 (d) and Figure 4 (e) is a processing effect diagram of the prior art, Figure 4 (c) and Figure 4 (f) is a diagram showing the processing effect after improvement of this application.

[0151] exist Figure 5 middle, Figure 5 (a) is the cable simulation scene. Figure 5 (b) is the cable simulation signal, Figure 5(c) is a diagram showing the effect of using the power concealed facility detection method proposed in this application to suppress clutter on the cable simulation signal. Figure 5 (d) is the BP algorithm imaging result of the cable simulation signal. Figure 5 (e) is the CBP algorithm imaging result of the cable simulation signal. Figure 5 (f) is the improved BP algorithm imaging result of the cable simulation signal. The improved BP algorithm imaging result described here is the imaging method used in the power hidden facility detection method proposed in this application. It can be understood that Figure 5 (b) is the subject of processing, Figure 5 (d) and Figure 5 (e) is a processing effect diagram of the prior art, Figure 5 (c) and Figure 5 (f) is a diagram showing the processing effect after improvement of this application.

[0152] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0153] The following is a description of a hidden power facility detection device provided in an embodiment of the present application. The hidden power facility detection device described below and the hidden power facility detection method described above can refer to each other.

[0154] like Figure 6 As shown, the present application provides a power concealed facility detection device 400, the device comprising:

[0155] The signal acquisition module 401 is used to acquire radar signals and decompose the radar signals to obtain a clutter matrix and a target matrix;

[0156] A matrix decomposition module 402 is used to decompose the clutter matrix into a first matrix and a second matrix, and construct a target optimization function according to the first matrix, the second matrix and the target matrix; wherein the target optimization function takes the sparsity of the target matrix as constrained as much as possible and the effective signal components in the clutter matrix as optimization goals;

[0157] A function solving module 403 is used to solve the target optimization function to determine a target matrix when the target optimization function reaches the optimization target;

[0158] A signal reconstruction module 404 is used to perform signal reconstruction according to the target matrix determined when the target optimization function reaches the optimization target, so as to obtain a target radar signal;

[0159] The imaging detection module 405 is used to perform back projection imaging based on the target radar signal to perform imaging detection on the hidden power facilities.

[0160] In one embodiment, the matrix decomposition module includes: the target optimization function is expressed as:

[0161]

[0162] In the formula, is the first matrix, is the second matrix, represents the product of the first matrix and the second matrix, i.e., the clutter matrix, is the target matrix, is the noise matrix, Represents a radar signal, and is the regularization parameter, It means to sum the absolute values ​​of the 2-norms of the column vectors of the first matrix. It means to sum the absolute values ​​of the 2-norms of the column vectors of the transposed matrix of the second matrix. Represents the sum of the absolute values ​​of each element in the transposed matrix of the target matrix.

[0163] In one embodiment, the matrix decomposition module includes:

[0164] The matrix grouping submodule is used to group the clutter matrix by using factor group sparse regularization to determine a first matrix and a second matrix, wherein the product of the first matrix and the second matrix is ​​the clutter matrix.

[0165] In one embodiment, the imaging detection module includes:

[0166] A scattering determination submodule, for determining, for each imaging point in the target radar signal, the scattering responses of the plurality of apertures corresponding to the imaging point;

[0167] A sequence determination submodule, used to determine the effective echo sequence of each aperture according to the scattering response of each aperture;

[0168] an energy enhancement submodule, for determining a correlation coefficient of each aperture according to the effective echo sequence of each aperture, and weighting the scattering response of each aperture based on the correlation coefficient thereof, so as to enhance the reflected energy of the target area in the target radar signal;

[0169] The energy attenuation submodule is used to perform cross-correlation processing on the weighted scattering responses of each aperture to attenuate the reflected energy of the non-target area in the target radar signal and determine the reflection intensity of the imaging point;

[0170] The image generation submodule is used to generate a radar image based on the reflection intensity of each imaging point when determining the reflection intensity of each imaging point in the target radar signal.

[0171] In one embodiment, the energy reduction submodule includes:

[0172] An intensity determination unit, used to determine the correlation between the weighted scattering responses of each aperture, and sum the correlation between the weighted scattering responses of each aperture to obtain the initial reflection intensity of the imaging point;

[0173] A factor determination unit, used to determine the coherence factor of the imaging point according to the correlation between each pair of weighted scattering responses of each aperture;

[0174] The weighting unit is used to weight the initial reflection intensity by using the coherence factor to determine the reflection intensity of the imaging point.

[0175] In one embodiment, the energy enhancement submodule includes:

[0176] A sequence determination unit, used for determining a valid echo sequence of a middle aperture among the apertures as a reference sequence;

[0177] The coefficient calculation unit is used to calculate the correlation coefficient between the effective echo sequence of each aperture and the reference sequence, and use the correlation coefficient between the effective echo sequence of each aperture and the reference sequence as the correlation coefficient of the aperture.

[0178] In one embodiment, the scatter determination submodule includes:

[0179] A time delay calculation unit, used to calculate the time delay vector of each aperture according to the coordinates of the imaging point and the coordinates of each aperture;

[0180] A signal determination unit, configured to determine the A-scan signal received by each aperture based on the time delay vector of each aperture;

[0181] The scattering generation unit is used to generate a scattering response corresponding to each aperture according to the A-type scanning signal received by each aperture.

[0182] The division of each module in the above-mentioned hidden power facility detection device is only for illustration. In other embodiments, the hidden power facility detection device can be divided into different modules as needed to complete all or part of the functions of the above-mentioned hidden power facility detection device. Each module in the above-mentioned hidden power facility detection device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0183] In one embodiment, the present application also provides a storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the hidden power facility detection method as described in any of the above embodiments.

[0184] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method for detecting hidden power facilities as described in any one of the above embodiments.

[0185] Indicatively, Figure 7 As shown, Figure 7 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 500 may be provided as a server. Figure 7 The computer device 500 includes a processing component 502, which further includes one or more processors, and a memory resource represented by a memory 501, for storing instructions executable by the processing component 502, such as an application. The application stored in the memory 501 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 502 is configured to execute instructions to perform the power hidden facility detection method of any of the above embodiments.

[0186] The computer device 500 may further include a power supply component 503 configured to perform power management of the computer device 500, a wired or wireless network interface 504 configured to connect the computer device 500 to a network, and an input / output (I / O) interface 505. The computer device 500 may operate based on an operating system stored in the memory 501, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0187] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0188] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish an entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only include those elements, but also include other elements that are not clearly listed, or also include elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. Herein, the singular "one", "one" and "described / the" may also include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "include / comprise" or "have" etc. specify the existence of stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the existence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof. At the same time, the term "and / or" used in this specification includes any and all combinations of the relevant listed items.

[0189] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0190] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting hidden power facilities, characterized in that: The method comprises: Acquire a radar signal, and decompose the radar signal to obtain a clutter matrix and a target matrix; Decomposing the clutter matrix into a first matrix and a second matrix, and constructing a target optimization function according to the first matrix, the second matrix and the target matrix; wherein the target optimization function takes constraining the sparsity of the target matrix as much as possible and extracting effective signal components in the clutter matrix as optimization goals; Solving the target optimization function to determine a target matrix when the target optimization function reaches the optimization target; Reconstructing the signal according to the target matrix determined so that the target optimization function reaches the optimization target, so as to obtain the target radar signal; Backward projection imaging is performed based on the target radar signal to perform imaging detection on hidden power facilities.

2. The method for detecting hidden electric power facilities according to claim 1, characterized in that: The objective optimization function is expressed as: In the formula, is the first matrix, is the second matrix, represents the product of the first matrix and the second matrix, i.e., the clutter matrix, is the target matrix, is the noise matrix, Represents a radar signal, and is the regularization parameter, It means to sum the absolute values ​​of the 2-norms of the column vectors of the first matrix. It means to sum the absolute values ​​of the 2-norms of the column vectors of the transposed matrix of the second matrix. Represents the sum of the absolute values ​​of each element in the transposed matrix of the target matrix.

3. The method for detecting hidden electric power facilities according to claim 1, characterized in that: The step of decomposing the clutter matrix into a first matrix and a second matrix comprises: The clutter matrix is ​​grouped using factor group sparse regularization to determine a first matrix and a second matrix, wherein the product of the first matrix and the second matrix is ​​the clutter matrix.

4. The method for detecting hidden electric power facilities according to any one of claims 1 to 3, characterized in that: The step of performing back projection imaging based on the target radar signal comprises: For each imaging point in the target radar signal, determining the scattering responses of multiple apertures corresponding to the imaging point; Determine the effective echo sequence of each aperture according to the scattering response of each aperture; Determining the correlation coefficient of each aperture according to the effective echo sequence of each aperture, and weighting the scattering response of each aperture based on the correlation coefficient to enhance the reflected energy of the target area in the target radar signal; Performing cross-correlation processing on the weighted scattering responses of each aperture to weaken the reflected energy of the non-target area in the target radar signal and determine the reflection intensity of the imaging point; When the reflection intensity of each imaging point in the target radar signal is determined, a radar image is generated based on the reflection intensity of each imaging point.

5. The method for detecting hidden electric power facilities according to claim 4, characterized in that: The step of performing cross-correlation processing on the weighted scattering responses of each aperture comprises: Determine the correlation between the weighted scattering responses of each aperture, and sum the correlation between the weighted scattering responses of each aperture to obtain the initial reflection intensity of the imaging point; Determine the coherence factor of the imaging point according to the correlation between the weighted scattering responses of each aperture; The initial reflection intensity is weighted by using the coherence factor to determine the reflection intensity of the imaging point.

6. The method for detecting hidden electric power facilities according to claim 4, characterized in that: The step of determining the correlation coefficient of each aperture according to the effective echo sequence of each aperture comprises: Determine the effective echo sequence of the middle aperture among the apertures as the reference sequence; The correlation coefficient between the effective echo sequence of each aperture and the reference sequence is calculated, and the correlation coefficient between the effective echo sequence of each aperture and the reference sequence is used as the correlation coefficient of the aperture.

7. The method for detecting hidden electric power facilities according to claim 4, characterized in that: The step of determining the scattering responses of multiple apertures corresponding to the imaging point comprises: According to the coordinates of the imaging point and the coordinates of each aperture, the time delay vector of each aperture is calculated respectively; Based on the time delay vector of each aperture, determining the A-scan signal received by each aperture; According to the A-scan signals received at each aperture, a scattering response corresponding to each aperture is generated respectively.

8. A device for detecting hidden power facilities, characterized in that: The device comprises: A signal acquisition module is used to acquire radar signals and decompose the radar signals to obtain a clutter matrix and a target matrix; A matrix decomposition module, used for decomposing the clutter matrix into a first matrix and a second matrix, and constructing a target optimization function according to the first matrix, the second matrix and the target matrix; wherein the target optimization function takes constraining the sparsity of the target matrix as much as possible and extracting effective signal components in the clutter matrix as optimization goals; A function solving module, used for solving the target optimization function to determine a target matrix when the target optimization function reaches the optimization target; A signal reconstruction module, used to perform signal reconstruction according to the target matrix determined so that the target optimization function reaches the optimization target, so as to obtain a target radar signal; The imaging detection module is used to perform back projection imaging based on the target radar signal to perform imaging detection on the hidden power facilities.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method for detecting hidden power facilities as described in any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method for detecting hidden power facilities as described in any one of claims 1 to 7 are performed.

Images