A method and system for monitoring the displacement of power grid towers in mining areas

Abstract

A method and system for monitoring the displacement of power towers in mining areas, including: selecting ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain in the mining area; determining distributed scatterer points among the candidate points of distributed scatterers; calculating the surface deformation of the mining area based on the distributed scatterer points, and monitoring the displacement of the towers according to the surface deformation. The scheme accurately and timely reflects the ground subsidence and geological changes in the mining area of ​​the transmission line, and provides fast and large-area surface deformation information for the displacement monitoring of power towers. It also provides data support for disaster prevention and mitigation work of the power grid.

Description

Technical Field

[0001] The invention belongs to a theoretical technical method, and specifically relates to a method and system for monitoring the displacement of power towers in mining areas. Background Art

[0002] In order to speed up the construction of UHV power grid, the planning areas and industrial areas along the lines are gradually increasing, and the optional path corridors are becoming less and less. UHV and other transmission line projects inevitably have to cross the coal mining area. The stability of the tower foundation has a great impact on the safety of the entire regional power grid. Large areas of mining and subsidence areas cause serious ecological and environmental problems in mining areas, causing damage to buildings, transportation, and line facilities. On the other hand, avoiding or dealing with them will greatly increase the investment cost. Therefore, the monitoring and early warning of ground subsidence and deformation in mining areas are particularly important. At present, the commonly used surface displacement monitoring technologies mainly include leveling, GPS technology, synthetic aperture radar interferometry InSAR (Interferometricsynthetic aperture radar) technology, etc. Based on the current problems, from the urgent need for geological disaster monitoring and early warning along the line during the construction and operation of transmission lines, a method is urgently needed to supplement the existing power grid tower displacement monitoring network. Summary of the invention

[0003] In order to solve the above-mentioned deficiencies in the prior art, the present invention proposes a method and system for monitoring the displacement of power towers in mining areas.

[0004] The technical solution provided by the present invention is:

[0005] A method for monitoring the displacement of power towers in mining areas, comprising:

[0006] Select ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain of the mining area;

[0007] Determining a distributed scatterer point among the distributed scatterer candidate points;

[0008] The surface deformation amount of the mining area is calculated based on the distributed scatterer points, and the displacement of the tower is monitored according to the surface deformation amount.

[0009] Preferably, the selecting of ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain of the mining area includes:

[0010] Performing image registration on the image data of the terrain of the mining area acquired in advance to obtain image registration data;

[0011] A ground target is selected as a candidate point of a distributed scatterer based on the image registration data.

[0012] Preferably, the step of performing image registration on the image data of the pre-acquired topography of the mining area to obtain image registration data comprises:

[0013] Based on the synthetic aperture radar image data of the terrain of the mining area acquired in advance, one image data is selected as the main image, and the remaining image data are selected as the slave images;

[0014] Performing coarse registration processing on the slave image to determine an initial offset, a search window, and coarse registration image data;

[0015] Determining fine registration image data according to fine registration processing based on the initial offset, the search window, and the coarse registration image data;

[0016] Determine different unknown parameters based on the precisely registered image data and the main image according to polynomial fitting;

[0017] Determining image registration data based on the unknown parameters, the polynomial fitting and the precise registration image data;

[0018] Wherein, the fine registration includes: refining the result of the rough registration to provide a reliable observation value for the polynomial fitting.

[0019] Preferably, the polynomial fitting is as follows:

[0020] s'=a1s+b1l+c1sl+d1

[0021] l'=a2s+b2l+c2sl+d2

[0022] Where s is the horizontal coordinate value of the pixel in the main image, l is the vertical coordinate value of the main image, s' is the horizontal coordinate value of the pixel in the slave image, l' is the vertical coordinate value of the pixel in the slave image, and a1, b1, c1, d1, a2, b2, c2, and d2 are different unknown parameters.

[0023] Preferably, the selecting a ground target as a distributed scatterer candidate point based on the image registration data comprises:

[0024] A coherence map is generated based on the image registration data and ground targets with stable scattering characteristics are selected as candidate points of distributed scatterers.

[0025] Preferably, the determining of distributed scatterer points among the distributed scatterer candidate points comprises:

[0026] Generate a differential interference phase map based on the coherence map and determine the atmospheric phase disturbance of each candidate point of the distributed scatterer based on the high-pass characteristics in the time domain and the low-pass characteristics in the space domain;

[0027] Removing the atmospheric phase disturbance from the differential interferometry phase image using a Kriging interpolation method;

[0028] According to the removed distributed scatterer candidate points of the atmospheric phase, the points with an overall correlation coefficient greater than a threshold of 0.6 are retained as distributed scatterer points based on the overall correlation coefficient method.

[0029] Preferably, the calculating of the surface deformation amount of the mining area based on the distributed scatterer points includes:

[0030] Constructing a set of equations for each pair of adjacent distributed scatterer points based on the distributed scatterer points;

[0031] The solution of the equation group is obtained by the periodogram method and the linear deformation velocity and elevation error at each distributed scatterer point are obtained by using the phase unwrapping algorithm.

[0032] Preferably, the linear deformation velocity and elevation error are calculated as follows:

[0033]

[0034] In the formula, is the differential phase between two points in the k-th scene differential interference pattern; λ is the sensor wavelength; Δv (x,y) Indicates the relative rate of change between two points; T k is the time baseline of the k-th scene differential interferogram; is the vertical spatial baseline of the k-th scene differential interferogram; R is the slant distance of the reference point; θ is the radar side viewing angle of the reference point; Δε (x,y) is the relative elevation error between two points; is the model residual in the k-th scene differential interferogram.

[0035] Another object of the present invention is to provide a displacement monitoring system for power towers in mining areas, comprising: a collection and selection module, a selection and determination module and a deformation data determination module;

[0036] The acquisition and selection module is used to select ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain of the mining area;

[0037] The selection and determination module is used to determine the distributed scatterer points among the distributed scatterer candidate points based on the overall correlation coefficient method;

[0038] The deformation data determination module is used to calculate the surface deformation amount of the mining area based on the distributed scatterer points, and monitor the displacement of the tower according to the surface deformation amount.

[0039] Preferably, the acquisition and selection module includes: a registration submodule and a preliminary selection submodule;

[0040] The registration submodule is used to perform image registration on the image data of the terrain of the mining area acquired in advance to obtain image registration data;

[0041] The preliminary selection submodule is used to select ground targets as candidate points of distributed scatterers based on the image registration data.

[0042] Preferably, the registration submodule comprises: a first processing unit, a second processing unit, a third processing unit, a parameter determination unit and an image determination unit;

[0043] The first processing unit is used to select one image data as a main image and select the remaining image data as slave images based on the synthetic aperture radar image data of the mining area terrain acquired in advance;

[0044] The second processing unit is used to perform coarse registration processing on the slave image to determine an initial offset, a search window and coarse registration image data;

[0045] The third processing unit is used to determine the fine registration image data according to the fine registration processing based on the initial offset, the search window and the coarse registration image data;

[0046] The parameter determination unit is used to determine different unknown parameters according to polynomial fitting based on the fine registration image data and the main image;

[0047] The image determination unit is used to determine the image registration data based on the unknown parameters, polynomial fitting and precise registration image data;

[0048] Wherein, the fine registration includes: refining the result of the rough registration to provide a reliable observation value for the polynomial fitting.

[0049] Preferably, the selection and determination module includes: a selection submodule;

[0050] The selection submodule is used to generate a coherence map based on the image registration data and select ground targets with stable scattering characteristics as candidate points of distributed scatterers.

[0051] Preferably, the deformation data determination module includes: a calculation submodule;

[0052] The calculation submodule is used to calculate the linear deformation velocity and elevation error according to the following formula:

[0053]

[0054] In the formula, is the differential phase between two points in the k-th scene differential interference pattern; λ is the sensor wavelength; Δv (x,y)Indicates the relative rate of change between two points; T k is the time baseline of the k-th scene differential interferogram; is the vertical spatial baseline of the k-th scene differential interferogram; R is the slant distance of the reference point; θ is the radar side viewing angle of the reference point; Δε (x,y) is the relative elevation error between two points; is the model residual in the k-th scene differential interferogram.

[0055] Compared with the closest prior art, the technical solution provided by the present invention has the following beneficial effects:

[0056] The technical solution of the present invention selects ground targets as candidate points of distributed scatterers by pre-acquiring image data of the terrain of the mining area; determines distributed scatterer points among the candidate points of distributed scatterers; calculates the surface deformation of the mining area based on the distributed scatterer points, and monitors the displacement of the pole tower according to the surface deformation. The solution accurately and timely reflects the ground subsidence and geological changes in the mining area of ​​the transmission line, and quantitatively monitors the horizontal and vertical displacement of the tower base of the power grid pole tower in the mining area of ​​the transmission line.

[0057] The technical solution of the present invention provides data basis for accurate early warning, timely prevention and control, and post-disaster emergency disposal of geological disasters in mining areas of transmission lines, effectively enhances the ability of the power grid system to cope with geological disasters in mining areas, and is of great significance to improving the safe and stable operation level of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the method of the present invention;

[0059] Figure 2 It is a flow chart of the interference processing of the long-term interferometry technology (MTInSAR) of the present invention;

[0060] Figure 3 is the interference pattern of the present invention;

[0061] Figure 4 is an example of a differential interference phase diagram of the present invention;

[0062] Figure 5 The interference network combination of the present invention;

[0063] Figure 6 The data coverage of the monitoring work area of ​​the present invention;

[0064] Figure 7 It is the regional average sedimentation rate diagram of the present invention;

[0065] Figure 8 It is the transmission tower area settlement information of the present invention. DETAILED DESCRIPTION

[0066] In order to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Based on the urgent need for geological disaster monitoring and early warning along the transmission line construction and operation, and in view of the dilemma that the current ground monitoring technology of transmission lines cannot achieve wide-area high-frequency and high-temporal and spatial resolution monitoring of local key areas, it is necessary to use the advantages of InSAR technology to supplement the existing power grid tower displacement monitoring network. The present invention uses the advantages of InSAR technology and PS-InSAR technology in deformation measurement to provide fast and large-area surface deformation information for power grid tower displacement monitoring. Time series InSAR technology can effectively remove the influence of atmospheric phase and terrain phase, and obtain long-term continuous and stable deformation sequences, providing data support for power grid disaster prevention and mitigation work.

[0068] Implementation 1.

[0069] Based on the collection of SAR data sets of transmission lines in goaf areas, this paper uses long-term interferometry (MTInSAR) technology to perform high-precision quantitative analysis on image data sets to estimate values ​​such as deformation rate near transmission lines in goaf areas. The MTInSAR processing flow mainly includes the selection and generation of differential interferometry pairs, the selection of coherent point targets, the unwrapping of interference fringe patterns, and the acquisition of time series deformation variables in the study area. Among them, Figure 2 This is the interference processing flow chart of the long-term interferometry technology (MTInSAR).

[0070] The basic steps of long-term interferometry (MTInSAR) processing mainly include: data acquisition, selection of the main image of the work area and registration of the main and secondary images, generation of differential interferometric phase map; selection of candidate points for distributed scatterers; estimation of deformation and elevation error; atmospheric phase correction; re-estimation of deformation and elevation error at DS points. In the distributed scatterer processing process, the difficulty of the entire technical process lies in obtaining the estimation of the deformation and DEM error of the study area by solving the equation group.

[0071] from Figure 1 It can be seen that the present invention proposes a method for monitoring the displacement of power towers in mining areas, comprising:

[0072] S1, selecting ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain of the mining area;

[0073] Furthermore, selecting ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain of the mining area includes:

[0074] Performing image registration on the image data of the terrain of the mining area acquired in advance to obtain image registration data;

[0075] Ground targets are selected as candidate points of distributed scatterers based on image registration data.

[0076] Furthermore, image registration is performed on the image data of the terrain of the mining area acquired in advance to obtain image registration data, including:

[0077] Based on the synthetic aperture radar image data of the terrain of the mining area acquired in advance, one image data is selected as the main image, and the remaining image data are selected as the slave images;

[0078] Performing coarse registration processing on the slave image to determine the initial offset, the search window and the coarse registration image data;

[0079] Determining fine registration image data according to fine registration processing based on the initial offset, the search window, and the coarse registration image data;

[0080] Determine different unknown parameters based on the precisely registered image data and the main image according to polynomial fitting;

[0081] Determining image registration data based on unknown parameters, polynomial fitting, and precise registration of image data;

[0082] The fine registration includes: refining the result of the rough registration to provide a reliable observation value for the polynomial fitting.

[0083] Furthermore, the polynomial fitting is as follows:

[0084] s'=a1s+b1l+c1sl+d1

[0085] l'=a2s+b2l+c2sl+d2

[0086] Where s is the horizontal coordinate value of the pixel in the main image, l is the vertical coordinate value of the main image, s' is the horizontal coordinate value of the pixel in the slave image, l' is the vertical coordinate value of the pixel in the slave image, and a1, b1, c1, d1, a2, b2, c2, and d2 are different unknown parameters.

[0087] Furthermore, selecting a ground target as a candidate point of a distributed scatterer based on the image registration data includes:

[0088] The coherence map is generated based on the image registration data and ground targets with stable scattering characteristics are selected as candidate points of distributed scatterers.

[0089] S2. determining a distributed scatterer point among candidate distributed scatterer points;

[0090] Further, determining the distributed scatterer point among the distributed scatterer candidate points includes:

[0091] Generate a differential interference phase map based on the coherence map and determine the atmospheric phase disturbance of each candidate point of the distributed scatterer based on the high-pass characteristics in the time domain and the low-pass characteristics in the space domain;

[0092] Removing the atmospheric phase disturbance from the differential interferometry phase image using a Kriging interpolation method;

[0093] According to the removed distributed scatterer candidate points of the atmospheric phase, the points with an overall correlation coefficient greater than a threshold of 0.6 are retained as distributed scatterer points based on the overall correlation coefficient method.

[0094] S3. Calculate the surface deformation of the mining area based on the distributed scatterer points, and monitor the displacement of the tower according to the surface deformation.

[0095] Furthermore, the surface deformation variables of the mining area are calculated based on the distributed scatterer points, including:

[0096] Constructing a set of equations for each pair of adjacent distributed scatterer points based on the distributed scatterer points;

[0097] The solution of the equations is obtained by the periodogram method, and the phase unwrapping algorithm is used to obtain the linear deformation velocity and elevation error at each distributed scatterer point.

[0098] Furthermore, the linear deformation velocity and elevation error are calculated as follows:

[0099]

[0100] In the formula, is the differential phase between two points in the k-th scene differential interference pattern; λ is the sensor wavelength; Δv (x,y) Indicates the relative rate of change between two points; T k is the time baseline of the k-th scene differential interferogram; is the vertical spatial baseline of the k-th scene differential interferogram; R is the slant distance of the reference point; θ is the radar side viewing angle of the reference point; Δε (x,y) is the relative elevation error between two points; is the model residual in the k-th scene differential interferogram.

[0101] Specifically, the process of long-term interferometry technology (MTInSAR) is as follows:

[0102] (1) Obtain N SAR images

[0103] (2) Select the main image and the main and auxiliary images for registration

[0104] Due to the different satellite attitudes during imaging, the azimuth resolution, slant range resolution and coverage of each image will change slightly during multiple imaging. Therefore, in order to ensure the calculation efficiency and accuracy during interferometric processing, it is necessary to first perform an affine transformation on the SAR image to ensure that the size and position of the pixels can correspond one to one.

[0105] This transformation process is called image registration. During the registration process, one image needs to be designated as the master image and the rest as slave images. The core algorithms mainly include coarse registration, fine registration, polynomial fitting, and resampling. After resampling, the slave image will have the same image coverage, spatial resolution, and number of pixels as the master image. The registration algorithm mainly includes four steps: coarse registration, fine registration, fitting of registration polynomials, and resampling.

[0106] The purpose of coarse registration is to provide a more reliable initial offset and search window for the fine registration algorithm. Coarse registration generally uses orbital parameters for rapid solution, and its accuracy is within ten pixels, and can even reach the accuracy level of two pixels. Based on the coarse registration results and the primary and secondary images, the fine registration further refines the offset to achieve a registration accuracy of more than 1 / 8 pixel (the actual registration accuracy is generally around 0.05 pixels), so that the primary and secondary images meet the interference requirements. In order to improve the computational efficiency, this patent uses Fast Fourier Transform (FFT) in the calculation process. The purpose of fine registration is to provide reliable observations for polynomial fitting. The fitted polynomial reflects the functional relationship between the coordinates of each pixel of the slave image and the coordinates of the corresponding pixel of the master image. Considering the imaging characteristics of the repeated orbit of the radar image, the fitting polynomial must be able to express the offset, rotation and stretching characteristics of the image. The polynomial form used in this patent is:

[0107] s'=a1s+b1l+c1sl+d1

[0108] l'=a2s+b2l+c2sl+d2

[0109] Among them, (s, l) is the pixel coordinate in the master image, (s', l') is the coordinate of the pixel in the slave image, and these two sets of coordinates are taken from the results of precise registration.

[0110] In the above formula, eight unknown parameters need to be solved, namely a1, b1, c1, d1 and a2, b2, c2, d2. As many as hundreds of observation equations can be provided in the fine registration, the least squares algorithm can be used for parameter estimation. It is worth noting that due to the algorithmic characteristics of the fine registration algorithm itself, the offset of some points will contain estimation errors. In order to reduce the impact of the error, the offset needs to be refined. The process of offset refinement is to divide the equation with larger residuals among hundreds of observation equations and perform iterative calculations. After refinement, the master-slave image registration is generally 1 / 8 pixel or smaller. After obtaining the optimal estimates of the above eight unknown parameters, the slave image can be interpolated to the pixel space corresponding to the master image, that is, resampling. The interpolation algorithms include bilinear interpolation, spline function interpolation, inverse distance weighted interpolation, etc. Although theoretically, each interpolation algorithm has its own advantages and disadvantages, experiments show that different interpolation algorithms do not have a great impact on the interference results, elevation or deformation parameter inversion.

[0111] (3) Differential interferometry phase image generation

[0112] First, an interference combination network is constructed from the given N+1 SAR images. According to the traditional small baseline subset method, two influencing factors, seasonal changes in the time baseline and precipitation, are added to the three coherence influencing factors of the conventional time baseline, the vertical component of the spatial baseline and the Doppler centroid frequency difference to estimate the coherence of the interference pair; then, according to the calculated coherence matrix, image pairs with high coherence are selected to participate in the subsequent time series deformation inversion. The interference image pairs that constitute the interference combination network are used to generate several interference phase maps according to the synthetic aperture radar interferometry processing method. Using an external DEM or a DEM generated by several interference pairs with good coherence, the terrain phase is eliminated to generate a differential interference phase map, such as Figure 4 Among them, Figure 5 is the interference network combination of the present invention, and the interference pattern is generated before the differential interference phase pattern is generated, such as Figure 3 described.

[0113] (4) Selecting candidate distributed scatterers (DSC)

[0114] Select ground targets with stable scattering characteristics as candidate points of distributed scatterers. Compared with distributed scatterers, the characteristics of DS (distributed scatterer) point target mechanism involve the coherent accumulation of all smaller scatterers in the resolution unit, and none of these scatterers has a dominant scattering characteristic. This greatly improves the shortcomings of the traditional PS (permanent scatterer) that the number of selected is very limited, resulting in the observation results not being able to objectively reflect the overall changes in the study area. SBAS technology reduces the influence of spatiotemporal decorrelation by selecting short-time and space baseline image pairs. It is necessary to increase the signal-to-noise ratio of the scatterer through conventional multi-view or multi-spatial filtering processing, which is a very important step in the distributed scatterer algorithm and is related to the accuracy of subsequent deformation analysis. Directly using SAR interferometric phase images to select phase-stable scattering points has a large error, and the amplitude dispersion has a certain relationship with the degree of phase divergence. When the amplitude dispersion is less than 0.25, the amplitude dispersion can be used to estimate the degree of phase divergence. In order to compare the amplitude values ​​of the same ground target point on different SAR images, each image needs to be radiometrically corrected. The amplitude value is analyzed pixel by pixel, the ratio of the amplitude mean value to the standard deviation of each pixel is calculated, and the appropriate evaluation index and threshold are selected to screen out the candidate points of distributed scatterers. This method is greatly affected by the number of images. When the number of images is small, the amplitude stability cannot be correctly counted, resulting in large errors.

[0115] (5) Atmospheric phase correction

[0116] After estimating the linear deformation velocity and DEM error at each DSC point and removing this part of the phase, the remaining phase consists of nonlinear deformation phase, atmospheric disturbance phase and noise phase, among which the atmospheric phase and nonlinear deformation phase have different distribution characteristics in the time domain and space domain: the correlation length of nonlinear deformation in the space domain is small, and it has low-frequency characteristics in the time domain; the correlation length of atmospheric disturbance in the space domain is large, and it presents a random distribution in the time domain, which can be understood as a white noise process. Therefore, the atmospheric phase can be filtered in the time domain using a triangular window filter at each DSC point according to its high-pass characteristics in the time domain and low-pass characteristics in the space domain, and the high-frequency components in the time domain are extracted. The space domain is filtered on each interference pair to extract the low-frequency components in the space domain, thereby obtaining the atmospheric disturbance phase at the DSC point. The Kriging interpolation method is used to estimate the atmospheric disturbance phase at all pixel points on all interference pairs, and the calculated atmospheric phase is removed from the differential interferometry phase map.

[0117] (6) Screening DS points

[0118] After removing the atmospheric disturbance phase, the overall correlation coefficient is used to select distributed scatterers, and the DSC points with an overall coherence coefficient greater than a certain threshold are retained as DS points. The threshold is 0.6 to 0.8.

[0119] (7) Elevation error and deformation estimation

[0120] At the selected DS points, the differential interferometric phase can be expressed as the sum of the deformation phase, elevation error phase, orbit error phase, atmospheric disturbance phase and noise phase. It is assumed that the surface deformation is mainly linear deformation, and the elevation error phase is linearly related to the elevation error. However, since the differential interferometric phase at each DS point is a entangled phase at this time, and there is a phase drift on different differential interferograms, it is impossible to directly solve the equations at each DS point to calculate the linear deformation velocity and DEM error. At this time, it is necessary to construct a Delaunay triangulation network to connect the DS points, establish a differential phase model between adjacent DS points, and reduce the influence of nonlinear deformation and atmospheric disturbance phase. For each pair of adjacent DS points, several equations can be obtained to form a nonlinear system of equations. The solution of the system of equations - the difference in linear deformation velocity and DEM error between adjacent DS points - can be searched by methods such as periodograms, and the overall correlation coefficient is calculated. The linear deformation velocity and DEM error at each DS point in the discrete grid are obtained by the phase unwrapping algorithm.

[0121] Specifically, in the constructed Delaunay triangulation, the differential phase expression for every two DS points (x, y) on a certain interference pair k is:

[0122]

[0123] The five components on the right side of the equation correspond to linear deformation, elevation error, nonlinear deformation, atmosphere, and noise. Among these five items, the atmosphere is low-frequency information in space, and differential processing will make the atmospheric error very small. In addition, the contribution of nonlinear deformation and noise to the total phase is also small. Therefore, in the general PSI algorithm, the last three items in the equation are combined and treated as model residuals. If the settlement of the point target conforms to the linear settlement model, the above equation can be further rewritten as

[0124]

[0125] in,

[0126] is the differential phase between two points in the k-th scene differential interference pattern;

[0127] λ is the sensor wavelength;

[0128] Δv (x,y) Indicates the relative rate of change between two points;

[0129] T k is the time baseline of the k-th scene differential interferogram;

[0130] B k is the vertical spatial baseline of the k-th scene differential interference pattern;

[0131] R is the slope distance from the reference point;

[0132] θ is the radar side view angle of the reference point;

[0133] Δε (x,y) is the relative elevation error between two points;

[0134] is the model residual in the k-th scene differential interferogram.

[0135] For each scene of differential interferogram, the unknowns include Δv (x,y) and Δε (x,y) Three items, including The phase principal value is a known quantity, but the integer unknowns are unknown. Then the N-view differential interferogram can provide N observation equations, and the number of unknowns is N+2. The number of observation equations is less than the number of unknowns, and it is impossible to solve them directly by least squares. Therefore, this patent uses the periodogram method to estimate the deformation parameters. The periodogram method searches for the solution that maximizes the objective function in a given solution space. That is,

[0136]

[0137]

[0138] Δv (x,y) Indicates the relative rate of change between two points; Δε (x,y) It represents the relative elevation error between two points; γ represents the time domain coherence coefficient. is the model residual in the k-th scene differential interferogram.

[0139] The calculation process in the present invention is basically implemented in IDL. The general process of implementing the periodogram method is given below.

[0140] Define the solution space. In order to ensure that the solution space contains the optimal solution, the solution space can be set as large as possible. In the experimental process of the present invention, given Δv (x,y) The range is [-50,50] mm / yr, Δε (x,y) The range of is [-100,100]m. And the given solution range is divided into 10×10 grids. That is, Δv (x,y) The step length Δv sw is 5mm, Δε(x,y) The step length Δε sw is 10m.

[0141] Obtain the optimal solution on the grid. Substitute the 100 solutions provided on the 10×10 grid into equation (5) and calculate the time domain coherence coefficient γ corresponding to each solution. The solution that maximizes the time domain coherence coefficient is the optimal solution, denoted as (Δv n (x,y) , Δε n (x,y) ).

[0142] Iterative search. Redefine the solution space with the above optimal solution as the center. The new Δv (x,y) The range is [Δv n (x,y) -Δv sw , Δv n (x,y) +Δv sw ]mm / yr, Δε (x,y) The range is [Δε n (x,y) -Δε sw , Δε n (x,y) +Δε sw ]m. The number of grids remains 10×10. Calculate the optimal solution (Δv n+1 (x,y) , Δε n+1 (x,y) ).

[0143] Repeat the above two steps until |Δv n+1 (x,y) -Δv n (x,y) |<0.001, and |Δε n+1 (x,y) -Δε n (x,y) |<0.001. At this time (Δv n+1 (x,y) , Δε n+1 (x,y) ) is the final solution result.

[0144] During the calculation process, the solution will converge in geometric series. Each iteration will reduce the solution range to 1 / 10 of the original, and only three or four iterations are needed to reach the expected convergence condition. However, this search method has its own defects. The periodogram method is not the optimal solution method. It depends on the continuity of the given solution space. That is, if the given convergence condition is that the difference between two solutions is less than 0.001, then the accuracy of the solution space search can only reach 0.0001. Although it is not the optimal solution in theory, it is accurate enough in practical applications.

[0145] Example 2

[0146] This method is applied near the mining area in Taiyuan, Shanxi. From the data source, 37 scenes of COSMO-SkyMed HIMAGE SAR image data that completely cover the Taiyuan area are used. The time span is from June 2013 to June 2016, the central longitude is 112.5653666° east longitude, the central latitude is 37.8582637° north latitude, the imaging mode is Stripmap, the ground resolution is 3m x 3m, the polarization mode is HH, the incident angle is 24.940-28.360°, right-looking / ascending orbit, and the width is 40km×47km, which can cover the urban area of ​​Taiyuan and the surrounding areas very well. The data coverage of the monitoring work area is as follows: Figure 6 shown.

[0147] Taking into account the time baseline, space baseline and Doppler baseline, the image of 2014-8-9 was selected as the public master image, and other SAR images were registered. The registered images were processed with 3×3 multi-view to obtain their multi-view intensity map and average intensity map, and 36 interferometric pairs were generated at the same time.

[0148] Through the method proposed in this invention, the quantitative information of surface subsidence changes in Taiyuan City between 2013 and 2016 is finally obtained, such as Figure 7 As shown, different colors represent the corresponding sedimentation rates.

[0149] Import the identified tower information to form a transmission tower settlement rate diagram in the area, such as Figure 8 As shown in the figure, the area marked with a tower is where the tower is located.

[0150] Through this method, the ground subsidence and geological changes in the transmission line mining area can be accurately and timely reflected, and the horizontal and vertical displacements of the power grid tower base in the transmission line mining area can be quantitatively monitored, providing data basis for accurate early warning, timely prevention and control, and post-disaster emergency response to geological disasters in the transmission line mining area, effectively enhancing the ability of the power grid system to cope with geological disasters in the mining area, which is of great significance to improving the safe and stable operation level of the power grid.

[0151] Example 3

[0152] Based on the same inventive concept, another object of the present invention is to provide a displacement monitoring system for power grid towers in mining areas, comprising: a collection and selection module, a selection and determination module and a deformation data determination module;

[0153] The following is a further description of the above three modules:

[0154] An acquisition and selection module is used to select ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain of the mining area;

[0155] A determination module is selected to determine the distributed scatterer point among the distributed scatterer candidate points based on the overall correlation coefficient method;

[0156] The deformation data determination module is used to calculate the surface deformation of the mining area based on the distributed scatterer points and monitor the displacement of the tower according to the surface deformation.

[0157] Preferably, the acquisition and selection module includes: a registration submodule and a preliminary selection submodule;

[0158] A registration submodule, used for performing image registration on the image data of the terrain of the mining area acquired in advance to obtain image registration data;

[0159] The preliminary selection submodule is used to select ground targets as candidate points of distributed scatterers based on image registration data.

[0160] Preferably, the registration submodule comprises: a first processing unit, a second processing unit, a third processing unit, a parameter determination unit and an image determination unit;

[0161] A first processing unit is used to select one image data as a main image and select the remaining image data as slave images based on the synthetic aperture radar image data of the mining area terrain acquired in advance;

[0162] A second processing unit, used for performing a coarse registration process on the slave image to determine an initial offset, a search window and coarse registration image data;

[0163] A third processing unit, configured to determine fine registration image data according to fine registration processing based on the initial offset, the search window, and the coarse registration image data;

[0164] A parameter determination unit, used for determining different unknown parameters according to polynomial fitting based on the precisely registered image data and the main image;

[0165] An image determination unit, for determining image registration data based on unknown parameters, polynomial fitting and precise registration image data;

[0166] The fine registration includes: refining the result of the rough registration to provide reliable observations for polynomial fitting.

[0167] Preferably, selecting a determination module includes: selecting a submodule;

[0168] The selection submodule is used to generate a coherence map based on the image registration data and select ground targets with stable scattering characteristics as candidate points of distributed scatterers.

[0169] Preferably, the deformation data determination module includes: a calculation submodule;

[0170] The calculation submodule is used to calculate the linear deformation velocity and elevation error according to the following formula:

[0171]

[0172] In the formula, is the differential phase between two points in the k-th scene differential interference pattern; λ is the sensor wavelength; Δv (x,y) Indicates the relative rate of change between two points; T k is the time baseline of the k-th scene differential interferogram; is the vertical spatial baseline of the k-th scene differential interferogram; R is the slant distance of the reference point; θ is the radar side viewing angle of the reference point; Δε (x,y) is the relative elevation error between two points; is the model residual in the k-th scene differential interferogram.

[0173] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0174] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0175] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0176] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0177] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for monitoring the displacement of power towers in mining areas, characterized in that: include: Select ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain of the mining area; Determining a distributed scatterer point among the distributed scatterer candidate points; Calculating the ground deformation amount of the mining area based on the distributed scatterer points, and monitoring the displacement of the tower according to the ground deformation amount; The method of selecting ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain of the mining area includes: Performing image registration on the image data of the terrain of the mining area acquired in advance to obtain image registration data; Selecting ground targets as candidate points of distributed scatterers based on the image registration data; The selecting a ground target as a distributed scatterer candidate point based on the image registration data includes: Generate a coherence map based on the image registration data and select ground targets with stable scattering characteristics as candidate points of distributed scatterers; The determining of a distributed scatterer point among the distributed scatterer candidate points comprises: Generate a differential interference phase map based on the coherence map and determine the atmospheric phase disturbance of each candidate point of the distributed scatterer based on the high-pass characteristics in the time domain and the low-pass characteristics in the space domain; Removing the atmospheric phase disturbance from the differential interferometry phase image using a Kriging interpolation method; According to the distributed scatterer candidate points from which the atmospheric phase is removed, points with overall correlation coefficients greater than a threshold of 0.6 are retained as distributed scatterer points based on an overall correlation coefficient method.

2. The method for monitoring displacement of power towers in mining areas according to claim 1, characterized in that: The step of performing image registration on the image data of the pre-acquired terrain of the mining area to obtain image registration data comprises: Based on the synthetic aperture radar image data of the terrain of the mining area acquired in advance, one image data is selected as the main image, and the remaining image data are selected as the slave images; Performing coarse registration processing on the slave image to determine an initial offset, a search window, and coarse registration image data; Determining fine registration image data according to fine registration processing based on the initial offset, the search window, and the coarse registration image data; Determine different unknown parameters based on the precisely registered image data and the main image according to polynomial fitting; Determining image registration data based on the unknown parameters, the polynomial fitting and the precise registration image data; Wherein, the fine registration includes: refining the result of the rough registration to provide a reliable observation value for the polynomial fitting.

3. The method for monitoring the displacement of power towers in mining areas according to claim 2, characterized in that: The polynomial fit is as follows: s'=a1s+b1l+c1sl+d1 l'=a2s+b2l+c2sl+d2 Where s is the horizontal coordinate value of the pixel in the main image, l is the vertical coordinate value of the main image, s' is the horizontal coordinate value of the pixel in the slave image, l' is the vertical coordinate value of the pixel in the slave image, and a1, b1, c1, d1, a2, b2, c2, and d2 are different unknown parameters.

4. The method for monitoring the displacement of power towers in mining areas according to claim 1, characterized in that: The calculating of the surface deformation amount of the mining area based on the distributed scatterer points comprises: Constructing a set of equations for each pair of adjacent distributed scatterer points based on the distributed scatterer points; The solution of the equation group is obtained by the periodogram method and the linear deformation velocity and elevation error at each distributed scatterer point are obtained by using the phase unwrapping algorithm.

5. The method for monitoring the displacement of power towers in mining areas according to claim 4, characterized in that: The linear deformation velocity and elevation error are calculated as follows: In the formula, is the differential phase between two points in the k-th scene differential interference pattern; λ is the sensor wavelength; △v (x,y) Indicates the relative rate of change between two points; T k is the time baseline of the k-th scene differential interferogram; is the vertical spatial baseline of the k-th scene differential interferogram; R is the slant distance of the reference point; θ is the radar side viewing angle of the reference point; △ε (x,y) is the relative elevation error between two points; is the model residual in the k-th scene differential interferogram.

6. A displacement monitoring system for power towers in mining areas, characterized in that: include: Acquisition and selection module, selection and determination module and deformation data determination module; The acquisition and selection module is used to select ground targets as candidate points of distributed scatterers based on pre-acquired image data of the terrain of the mining area; The selection and determination module is used to determine the distributed scatterer points among the distributed scatterer candidate points based on the overall correlation coefficient method; The deformation data determination module is used to calculate the surface deformation amount of the mining area based on the distributed scatterer points, and monitor the displacement of the tower according to the surface deformation amount; The acquisition and selection module includes: a registration submodule and a preliminary selection submodule; The registration submodule is used to perform image registration on the image data of the terrain of the mining area acquired in advance to obtain image registration data; The preliminary selection submodule is used to select ground targets as candidate points of distributed scatterers based on the image registration data; The selection and determination module includes: a selection submodule; The selection submodule is used to generate a coherence map based on the image registration data and select ground targets with stable scattering characteristics as candidate points of distributed scatterers; The selection and determination module is specifically used for: Generate a differential interference phase map based on the coherence map and determine the atmospheric phase disturbance of each candidate point of the distributed scatterer based on the high-pass characteristics in the time domain and the low-pass characteristics in the space domain; Removing the atmospheric phase disturbance from the differential interferometry phase image using a Kriging interpolation method; According to the distributed scatterer candidate points from which the atmospheric phase is removed, points with overall correlation coefficients greater than a threshold of 0.6 are retained as distributed scatterer points based on an overall correlation coefficient method.

7. The displacement monitoring system for power grid towers in mining areas according to claim 6, characterized in that: The registration submodule includes: a first processing unit, a second processing unit, a third processing unit, a parameter determination unit and an image determination unit; The first processing unit is used to select one image data as a main image and select the remaining image data as slave images based on the synthetic aperture radar image data of the mining area terrain acquired in advance; The second processing unit is used to perform coarse registration processing on the slave image to determine an initial offset, a search window and coarse registration image data; The third processing unit is used to determine the fine registration image data according to the fine registration processing based on the initial offset, the search window and the coarse registration image data; The parameter determination unit is used to determine different unknown parameters according to polynomial fitting based on the fine registration image data and the main image; The image determination unit is used to determine the image registration data based on the unknown parameters, polynomial fitting and precise registration image data; Wherein, the fine registration includes: refining the result of the rough registration to provide a reliable observation value for the polynomial fitting.

8. The displacement monitoring system for power towers in mining areas according to claim 6, characterized in that: The deformation data determination module is specifically used for: The calculating of the surface deformation amount of the mining area based on the distributed scatterer points comprises: Constructing a set of equations for each pair of adjacent distributed scatterer points based on the distributed scatterer points; The solution of the equation group is obtained by the periodogram method and the linear deformation velocity and elevation error at each distributed scatterer point are obtained by using the phase unwrapping algorithm.

9. The displacement monitoring system for power towers in mining areas according to claim 8, characterized in that: The deformation data determination module includes: a calculation submodule; The calculation submodule is used to calculate the linear deformation velocity and elevation error according to the following formula: In the formula, is the differential phase between two points in the k-th scene differential interference pattern; λ is the sensor wavelength; △v (x,y) Indicates the relative rate of change between two points; T k is the time baseline of the k-th scene differential interferogram; is the vertical spatial baseline of the k-th scene differential interferogram; R is the slant distance of the reference point; θ is the radar side viewing angle of the reference point; △ε (x,y) is the relative elevation error between two points; is the model residual in the k-th scene differential interferogram.

Images