A deformation detection method and device for a power transmission channel geological sensitive area

Abstract

The application discloses a deformation detection method and device for a power transmission channel geological sensitive area. The method comprises the following steps: calculating radar image sets in the power transmission channel geological sensitive area by using a small baseline set interferometric measurement technology, determining first deformation data information of the power transmission channel geological sensitive area, performing difference calculation on front and back time phase point cloud data, determining second deformation data information of the power transmission channel geological sensitive area, determining absolute deformation data information of a tower in the power transmission channel geological sensitive area by using a Beidou differential positioning technology, and determining ground subsidence information of the power transmission channel geological sensitive area according to the first deformation data information, the second deformation data information and the absolute deformation data information of the tower.

Description

Technical Field

[0001] This invention relates to the field of power transmission channel deformation detection technology, and in particular to a method and apparatus for detecting deformation in geologically sensitive areas of power transmission channels. Background Technology

[0002] Surface deformation detection is a crucial aspect of power transmission corridor inspection. Considering the characteristics of power transmission corridors, surface deformation typically falls into two categories: continuous deformation and abrupt changes. Continuous deformation has been extensively studied, with traditional methods based on leveling. This method is time-consuming and labor-intensive, with a small single-detection area, but it offers high accuracy and is suitable for fixed-point inspections of structures such as foundation pits and bridges. Alternatively, time-series InSAR technology is a newer detection technique for surface deformation. Abrupt changes are primarily caused by natural disasters such as earthquakes, subsidence, and debris flows, or other uncontrollable external forces. Currently, detection methods commonly use manual measurement, InSAR, or multi-temporal lidar.

[0003] Transmission lines are a crucial component of the power grid, ensuring the transmission and distribution of electrical energy. With the construction and development of ultra-high-voltage and extra-high-voltage (UHV) power transmission lines in my country, transmission corridors often traverse mountainous areas, riverbeds, and coal mining subsidence zones—regions prone to geological disasters. Affected by these geological disasters, transmission line towers may experience foundation and facing cracking, ground settlement, tower tilting, and even collapse. From a deformation monitoring perspective, the displacement of tower foundations under geological disasters can generally be categorized into three main types: settlement, tilting, and slippage. In some cases, combined deformation may occur, affecting multiple tower foundations.

[0004] Deformation, tower movement, and warping in geologically sensitive areas can cause tower structural failure or even damage, seriously affecting the safe and stable operation of transmission lines and posing a serious threat to the safety of the power grid. Therefore, it is imperative to conduct inspections of geologically sensitive areas along transmission corridors. Summary of the Invention

[0005] The embodiments of this disclosure provide a method and apparatus for detecting deformation in geologically sensitive areas of power transmission channels.

[0006] According to one aspect of the present disclosure, a deformation detection method for a geologically sensitive area of ​​a power transmission channel is provided, comprising: calculating a radar image set within the geologically sensitive area of ​​the power transmission channel using small baseline set interferometry to determine first deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the first deformation data information is wide-area planar deformation information within the geologically sensitive area of ​​the power transmission channel; calculating the difference between consecutive temporal point cloud data to determine second deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the second deformation data information is strip-shaped deformation information of the power transmission channel within the geologically sensitive area of ​​the power transmission channel; determining absolute deformation data information of the towers within the geologically sensitive area of ​​the power transmission channel using BeiDou differential positioning technology, wherein the absolute deformation data information of the towers is point-like deformation information of the power transmission towers within the geologically sensitive area of ​​the power transmission channel; and determining ground subsidence information of the geologically sensitive area of ​​the power transmission channel based on the first deformation data information, the second deformation data information, and the absolute deformation data information of the towers.

[0007] According to another aspect of the present disclosure, a storage medium is also provided, the storage medium including a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0008] According to another aspect of the present disclosure, an apparatus is also provided for deformation detection in a geologically sensitive area of ​​a power transmission channel, comprising: a first determining module, configured to calculate a radar image set within the geologically sensitive area of ​​the power transmission channel using small baseline set interferometry to determine first deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the first deformation data information is wide-area planar deformation information within the geologically sensitive area of ​​the power transmission channel; a second determining module, configured to perform difference calculation on point cloud data of preceding and following time phases to determine second deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the second deformation data information is strip-shaped deformation information of the power transmission channel within the geologically sensitive area of ​​the power transmission channel; a third determining module, configured to determine absolute deformation data information of towers within the geologically sensitive area of ​​the power transmission channel using BeiDou differential positioning technology, wherein the absolute deformation data information of towers is point-like deformation information of power transmission towers within the geologically sensitive area of ​​the power transmission channel; and a fourth determining module, configured to determine ground subsidence information of the geologically sensitive area of ​​the power transmission channel based on the first deformation data information, the second deformation data information, and the absolute deformation data information of towers.

[0009] In this embodiment, the deformation detection technology for geologically sensitive areas of power transmission channels, which integrates SAR, laser scanning data, and BeiDou positioning data, utilizes high-precision point monitoring data acquired through multi-temporal laser scanning measurements and GNSS (BeiDou positioning) to verify the effectiveness of large-scale areal monitoring information obtained by InSAR technology. Then, data fusion calculations are performed at overlapping points. Finally, the fused data is used to correct the monitoring values ​​at all points, obtaining high-precision areal settlement information over a large area. Furthermore, the fused results not only ensure the accuracy and reliability of the leveling data but also possess the high-resolution advantage of InSAR measurements, effectively depicting the detailed information of deformation in geologically sensitive areas of power transmission channels. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of this disclosure and form part of this invention, illustrate exemplary embodiments of the present disclosure and are used to explain the disclosure, but do not constitute an undue limitation thereof. In the drawings:

[0011] Figure 1 This is a hardware structure block diagram of a computing device for implementing the method described in Embodiment 1 of this disclosure;

[0012] Figure 2 This is a flowchart illustrating the deformation detection method for geologically sensitive areas of power transmission channels according to Embodiment 1 of this disclosure;

[0013] Figure 3 This is a schematic diagram of the multi-source data fusion process for ground deformation monitoring according to Embodiment 1 of this disclosure;

[0014] Figure 4a It is based on the intensity of each scattering point of the distributed target pixel and the 100th pixel phase distribution according to Embodiment 1 of this disclosure;

[0015] Figure 4b It is based on the intensity of each scattering point of the coherent pixel and the phase distribution of the 100th pixel according to Embodiment 1 of this disclosure;

[0016] Figure 5 This is another schematic flowchart of the deformation detection method for geologically sensitive areas of power transmission channels based on point cloud data according to Embodiment 1 of this disclosure;

[0017] Figure 6a A schematic diagram illustrating the closest distance principle between points in the ICP algorithm as described in Embodiment 1 of this disclosure;

[0018] Figure 6b A schematic diagram illustrating the point-to-surface proximity principle of the ICP algorithm according to Embodiment 1 of this disclosure;

[0019] Figure 7A schematic diagram of the slope entropy-based thinning algorithm according to Embodiment 1 of this disclosure;

[0020] Figure 8 A schematic diagram of terrain key points and entropy thresholds as described in Embodiment 1 of this disclosure;

[0021] Figure 9 This is a schematic diagram of the structure of the Beidou tower monitoring equipment according to Embodiment 1 of this disclosure;

[0022] Figure 10 This is a schematic diagram of the BeiDou differential positioning technology according to Embodiment 1 of this disclosure;

[0023] Figure 11 This is a schematic diagram of the deformation detection method device for geologically sensitive areas of power transmission channels according to Embodiment 2 of this disclosure. Detailed Implementation

[0024] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Example 1

[0027] According to this embodiment, a deformation detection method embodiment for geologically sensitive areas of power transmission channels is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] The method embodiments provided in this example can be executed on mobile terminals, computer terminals, servers, or similar computing devices. Figure 1 A hardware block diagram of a computing device for implementing a deformation detection method in geologically sensitive areas of power transmission channels is shown. Figure 1 As shown, a computing device may include one or more processors (processors may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), memory for storing data, and transmission devices for communication functions. In addition, it may also include: a display, input / output interfaces (I / O interfaces), a universal serial bus (USB) port (which may be included as one of the ports in the I / O interface), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, a computing device may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0029] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element in a computing device. As involved in the embodiments of this disclosure, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0030] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the deformation detection method for geologically sensitive areas of power transmission channels in this embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the deformation detection method for geologically sensitive areas of power transmission channels described above. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to computing devices via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0031] The transmission device is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the computing device's communications provider. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0032] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows users to interact with the user interface of the computing device.

[0033] It should be noted here that, in some optional embodiments, the above... Figure 1 The computing device shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computing devices.

[0034] Under the aforementioned operating environment, according to the first aspect of this embodiment, a deformation detection method for geologically sensitive areas of power transmission channels is provided. Figure 2 A flowchart illustrating the method is shown below. (Refer to...) Figure 2 As shown, the method includes:

[0035] S202: Using small baseline set interferometry, the radar image set in the geologically sensitive area of ​​the power transmission channel is calculated to determine the first deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the first deformation data information is the wide-area surface deformation information within the geologically sensitive area of ​​the power transmission channel.

[0036] S204: Calculate the difference between the point cloud data of the preceding and following time phases to determine the second deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the second deformation data information is the strip-shaped deformation information of the power transmission channel within the geologically sensitive area of ​​the power transmission channel.

[0037] S206: Using BeiDou differential positioning technology, determine the absolute deformation data of towers in the geologically sensitive area of ​​the absolute deformation data transmission channel, wherein the absolute deformation data of the towers is the point deformation information of the transmission towers in the geologically sensitive area of ​​the absolute deformation data transmission channel;

[0038] S208: Based on the absolute deformation data information, the first absolute deformation data information, the second absolute deformation data information, and the absolute deformation data information of the tower, determine the ground subsidence information of the geologically sensitive area of ​​the power transmission channel.

[0039] As described in the background section, deformation, tower movement, and warping in geologically sensitive areas can cause tower structural failure or even damage, seriously affecting the safe and stable operation of transmission lines and posing a serious threat to the safety of the power grid. Therefore, it is imperative to conduct inspections of geologically sensitive areas along transmission corridors.

[0040] In view of this, refer to Figure 2 and Figure 3 As shown, this application presents a deformation detection technology for geologically sensitive areas of power transmission channels that integrates synthetic aperture radar (SAR), laser scanning data, and BeiDou positioning data. The fusion of SAR, laser scanning, and BeiDou positioning data in deformation detection of "wide-area, area-based, and point-based" objects is reflected in four aspects: early identification, hazard investigation, monitoring and early warning, and disaster emergency response. This follows a hierarchical, progressive, coarse-to-fine, and space-ground collaborative working mode.

[0041] This technology for detecting deformation in geologically sensitive areas of power transmission channels, which integrates SAR, laser scanning data, and BeiDou positioning data, utilizes high-precision point monitoring data acquired through multi-temporal laser scanning measurements and GNSS (BeiDou positioning) to verify the effectiveness of large-scale areal monitoring information obtained by InSAR technology. Data fusion calculations are then performed at overlapping points. Finally, the fused data is used to correct the monitoring values ​​at all points, obtaining high-precision areal settlement information over a large area. Furthermore, the fused results not only ensure the accuracy and reliability of leveling data but also possess the high-resolution advantage of InSAR measurements, effectively characterizing the detailed information of deformation in geologically sensitive areas of power transmission channels.

[0042] In addition, specifically, (1) the deformation monitoring points obtained by time-series InSAR processing have three-dimensional position coordinates, deformation rate, and deformation evolution history information. Deformation refers to the deformation in the radar line-of-sight (LOS) direction. Positive values ​​represent movement toward the radar, and negative values ​​represent movement away from the radar. For ease of expression, these are respectively described as uplift and subsidence.

[0043] (2) Using Beidou terminal equipment installed on or under ultra-high voltage transmission towers, high-precision point monitoring data obtained by long-term GNSS measurements are used to verify the effectiveness of large-area monitoring information obtained by InSAR technology.

[0044] (3) Using the results of point cloud deformation detection from multiple phases of airborne lidar, select more than 10 pairs of points with the same name to verify the effectiveness of the large-area monitoring information obtained by InSAR technology.

[0045] (4) The data of the Beidou differential monitoring points, airborne radar point cloud points and InSAR monitoring overlapping points in the area are fused and calculated. The fused data is used to correct the monitoring values ​​of all points and form a deformation rate rendering map of the area. The ground subsidence can be predicted and analyzed based on the fused ground subsidence rate.

[0046] (5) The fusion results should not only ensure the accuracy and reliability of the leveling data, but also have the advantages of high resolution of InSAR measurement, which can well depict the detailed information of deformation in the geologically sensitive area of ​​the power transmission channel.

[0047] Optionally, the operation of determining the ground settlement information of the geologically sensitive area of ​​the power transmission channel based on the absolute deformation data information, the first absolute deformation data information, the second absolute deformation data information, and the absolute deformation data information of the tower includes:

[0048] The nearest neighbor weighted average algorithm is used to extract corresponding points from the first absolute deformation data, the second absolute deformation data, and the absolute deformation data of the tower to determine the overlapping point data.

[0049] The data of overlapping points of absolute deformation data are fused using a pre-set data fusion algorithm to determine the fused data value of the overlapping points;

[0050] The fused data values ​​of absolute deformation data information at the points where absolute deformation data information overlaps are corrected using a pre-set monitoring value correction algorithm, and the corrected data values ​​of the points where absolute deformation data information overlaps are determined.

[0051] Based on the first deformation data and the corrected values ​​of the absolute deformation data, the ground subsidence information of the geologically sensitive area of ​​the power transmission channel is determined.

[0052] Specifically, refer to Figure 3 As shown, (1) point monitoring data verifies the accuracy of InSAR measurements.

[0053] Neighbor Interpolation: InSAR subsidence rate values ​​are extracted based on the coordinates of laser point clouds and GNSS points. Since InSAR observations acquire subsidence values ​​of related ground targets, while airborne laser point clouds and GNSS measurements acquire subsidence values ​​at specific points on the ground, these are not the same location. Therefore, it is necessary to extract the InSAR monitoring results for the corresponding locations of airborne laser point clouds and GNSS points according to certain criteria.

[0054] Corresponding points are extracted using a neighboring point weighted average method. Neighboring points within a certain distance ρ from the leveling point are selected, and their weighted average is used as the InSAR result corresponding to that laser point cloud or GNSS point. If no InSAR result exists within distance ρ, these points are not included in the analysis.

[0055] Accuracy assessment: Using laser point cloud and GPS observation results as the most probable values ​​of ground subsidence, the standard deviation is selected as the indicator for measuring accuracy.

[0056] (2) Algorithm for fusing data of overlapping points

[0057] If n monitoring methods are used to monitor ground subsidence, the subsidence value obtained by each monitoring method is y. i The settlement values ​​can then be represented by a matrix equation.

[0058] Y = KX + ε

[0059] In the formula, For unknown parameter vectors; ε represents the monitoring coefficient of the monitoring data; ε represents the noise of the monitoring data.

[0060] The error equation for estimating X using the least squares method is as follows:

[0061] V = KX′-Y

[0062] The valuation should meet the following requirements.

[0063]

[0064] Taking the first partial derivative of the estimate and setting it to zero, we can solve the equation to obtain...

[0065] X′=(K T PK) -1 K T PY

[0066] Due to the second derivative Valuation X′=(K) T PK) -1 K T PY has a minimum value.

[0067] The valuation error is:

[0068] ΔX′=XX′=(K T PK) -1 K T P

[0069] (3) Monitoring value correction algorithm

[0070] Setting position M(X) i Y i The estimated ground subsidence after fusion is Z′. i The observed settlement amount is Z. i Then the settlement correction at that point is

[0071] Z = Z′ i -Z i

[0072] The correction number for any point is

[0073]

[0074] F(x, y, x) i y i )=[(xx i ) 2 +(yy i ) 2 +δ 2 ] k

[0075] When k = 1 / 2, equation (9) is a positive hyperboloid function; when k = -1 / 2, it is a negative hyperboloid function. Let Q... ij =F(x, y, x) i y i ),but The error equation is v = Qa - t. According to the least squares principle, we can get a = Q. T Q -1 Q T From this, the multifaceted function equation can be obtained, and then the point correction can be calculated to obtain the estimated point settlement Z′. i .

[0076] Optionally, the operation of calculating the radar image set within the geologically sensitive area of ​​the transmission channel using small baseline set interferometry to determine the first deformation data information of the geologically sensitive area of ​​the transmission channel includes:

[0077] After registering a predetermined number N radar images within the geologically sensitive area of ​​the power transmission channel, they are resampled onto the same main image to determine the image set, where the dimension of the image set is N.

[0078] The coherence between distributed target points and permanent scattering points in each radar image in the image set is determined by calculating the pixels of each radar image in the image set.

[0079] The maximum likelihood estimation algorithm is used to calculate the temporal coherence dimension of each radar image in the image set, and the temporal coherence dimension image is added to the image set to determine the temporal coherence image set, where the dimension of the temporal coherence image set is N+1.

[0080] Based on the small baseline set interferometry technique, radar images of the temporal coherence image set are calculated to determine the first deformation data information of the geologically sensitive area of ​​the power transmission channel.

[0081] Specifically, firstly, a predetermined number N radar images are registered and then resampled onto the same main image to determine an image set, where the dimension of the image set is N. The geologically sensitive area of ​​the power transmission channel can be a user-specified region of the power transmission channel, i.e., the area image to be studied.

[0082] Specifically, given N SAR images that have been registered and resampled onto the same master image geometry, and denoted as d, the image set can be expressed as:

[0083] d(P)=[d1(P), d2(P),...,d N (P)] T

[0084] Where P represents a pixel, T represents the transpose, and d i (P) The complex data corresponding to pixel P in the i-th SAR image.

[0085] Furthermore, the coherence between distributed target points and permanent scattering points in each radar image in the image set is determined by calculating the pixels of each radar image in the image set.

[0086] Distributed Scatterers (DS) refer to ground features in which all scatterers within a resolution cell have roughly the same backscattering coefficient, and are mostly bare land, sparse vegetation, etc.

[0087] refer to Figure 4a and Figure 4b As shown, DS points are common in suburban areas. Although they only maintain certain coherence characteristics in some interferograms, extracting the interferometric geometric information of these targets is very important for expanding the application scope of time series SAR data. Figure 4a and Figure 4bCiting Hooper's 2006 simulation results, the difference between distributed pixels and coherent pixels is shown. If, within a pixel, the backscattering intensity of one scatterer (permanent scatterer PS) is significantly greater than that of other scatterers in the pixel, and the dominant scatterer is relatively stable, this pixel is called a coherent pixel. Coherent pixels have relatively stable backscattering intensity and phase, such as... Figure 4b As shown, in the 100 simulations, the pixel phase remained relatively constant. The ground features within the distributed pixels were relatively homogeneous, with roughly equal backscattering intensities from each scatterer, and no single strong scatterer was observed. The phase exhibited a random distribution among the scatterers, such as... Figure 4a As shown, distributed target interferometric SAR technology was employed, taking into account the statistical characteristics of the backscattering coefficients at both the PS and DS points.

[0088] Furthermore, the maximum likelihood estimation algorithm is used to calculate the temporal coherence dimension of each radar image in the image set, and the temporal coherence dimension image is added to the image set to determine the temporal coherence image set, where the dimension of the temporal coherence image set is N+1. Thus, temporal coherence facilitates the analysis of deformation information in the geologically sensitive areas of the power transmission channel over time.

[0089] Finally, based on the small baseline set interferometry technique, radar images from the temporal coherence image set are calculated to determine the first deformation data information of the geologically sensitive area of ​​the power transmission channel. Deformation information is obtained by solving the data and removing various residual errors using the small baseline set interferometry technique.

[0090] Therefore, this application provides a deformation detection method for geologically sensitive areas of power transmission channels based on synthetic aperture radar (SAR). Due to the highly variable geological environment of power transmission channels, and considering deformation monitoring and full extraction of image information, distributed target interferometric SAR (DPI) technology is employed. This achieves large-scale, long-term, and high-precision monitoring of geological hazards in power transmission channels, overcoming the problem of scarce data extraction points in suburban areas. It is suitable for multi-scale change monitoring of various complex geological conditions, facilitating the assessment of hazard influencing factors. While retaining the basic workflow of PS-InSAR technology, DPI technology jointly considers the statistical characteristics of backscattering coefficients of PS and DS points, ultimately achieving surface deformation monitoring of PS and DS points. This solves the technical problem in existing technologies where traditional time-series INSAR technology discards a large number of poorly coherent interferometric pairs, resulting in insufficient extraction of SAR image information.

[0091] Optionally, the operation of calculating the coherence between distributed target points and permanent scattering points in each radar image of the image set includes: sorting the amplitude values ​​of the pixels in each radar image of the image set and determining the cumulative distribution function of the probability density function of the pixels; based on the cumulative distribution function, checking whether the first pixel and the second pixel of each radar image in the image set are homogeneous pixels using a non-parametric test method, wherein the first pixel and the second pixel are any two pixels of the radar image; if the first pixel and the second pixel are homogeneous pixels, determining the homogeneous region of the first pixel; based on the homogeneous region, determining the complex coherence matrix between the distributed target points and permanent scattering points; and based on the complex coherence matrix, determining the interferogram of the complex elements of the complex coherence matrix, the interferogram being used to describe the coherence between the distributed target points and permanent scattering points.

[0092] Specifically, the amplitude values ​​(x = |d|) of pixel P in each image are sorted, and the cumulative distribution function CDF of its probability density function PDF is obtained, and its unbiased estimator F N (X) can be expressed as:

[0093]

[0094] x i It is the i-th element in the amplitude sequence. After defining the cumulative distribution function (CDF), in order to test whether two pixels p and q are statistically homogeneous pixels (SHP) (i.e., their amplitude data have the same PDF), non-parametric test methods can be used, including the Kullback-Leibler distance method, the Kolmogorov-Smirnov test (KS), and the Anderson-Darling (AD) test.

[0095] Based on the homogeneous region Ω, a complex coherence matrix C is defined for the DS (distributed target) points and PS (permanent scattering) points. The phase value of the complex elements on the off-diagonal of the matrix is ​​the corresponding spatially filtered interferogram, and the absolute value is the coherence of the interferogram, as shown in the following formula:

[0096]

[0097] Where C(P) is the complex coherence matrix to be solved, and y represents the vector matrix formed by each pixel. H Let |Ω| represent the conjugate transpose of y, |Ω| represent the absolute value of the homogeneous region, y(p) represent the projection of the polarization scattering vector, p represents the pixel point of the homogeneous region, and E is another expression of the complex coherence matrix.

[0098] Thus, by adopting the above method, distributed target interferometric SAR technology is used to jointly consider the statistical characteristics of the backscattering coefficients of PS and DS points.

[0099] Optionally, the operation of checking whether the first and second pixels of each radar image in the image set are homogeneous pixels by means of a nonparametric test method includes: checking whether the two pixels of each radar image in the image set are homogeneous pixels by means of a nonparametric two-tailed test method based on the cumulative distribution function.

[0100] KS test statistical parameter D N Defined as:

[0101]

[0102] Of these methods, the parametric two-tailed A / D test is the most reliable. Statistical parameters Defined as:

[0103]

[0104] Let be the empirical cumulative distribution function CDF for points p and q, i = 1, 2, ..., N.

[0105] D N or If a certain threshold is met, then p and q are considered homogeneous pixels. For pixel p, the connected homogeneous region Ω within the estimation window can be obtained.

[0106] Optionally, the operation of calculating the temporal coherence dimension image by using the maximum likelihood estimation algorithm for each radar image in the image set includes: calculating the true phase of each radar image in the image set by using the maximum likelihood estimation method; replacing the target phase of the distributed target point in each radar image with the true phase to determine the evaluation index temporal coherence; and determining the temporal coherence dimension image based on the evaluation index temporal coherence.

[0107] Specifically, maximum likelihood estimation is used to solve for the true phase corresponding to each SAR image, and this phase is then used to replace the DS target phase. Assuming the phase value of the first SAR image is 0, according to the maximum likelihood estimation criterion, the optimal estimate for the other N-1 phase values ​​is λ = [0, θ2, ... θ]. N ] T It can be obtained from the following formula:

[0108]

[0109] Where Λ=exp(iλ) is an N-dimensional vector, This is the estimate of the complex coherence matrix. This is the Hadamard product. The above equation can be solved using the iterative algorithm BFGS (Broyden-Fletcher-Goldfarb-Shanno). The optimized phase is evaluated using the temporal coherence γ metric. PTA The expression is:

[0110]

[0111] In this way, the temporal coherence of each radar image in the image set can be obtained, which facilitates the calculation of the first deformation data information of the sensitive area of ​​the power transmission channel in the time dimension.

[0112] Optionally, the operation of calculating the first deformation data information of the geologically sensitive area of ​​the power transmission channel based on the small baseline set interferometry technique for radar images of the temporally coherent image set includes: determining multiple interferometric pairs based on the spatiotemporal baseline threshold and the temporally coherent image set; calculating the phase change rate between two adjacent radar images in the temporally coherent image set; determining the deformation velocity based on the interferometric pairs and the phase change rate; and integrating the deformation velocity of the geologically sensitive area of ​​the power transmission channel in each time interval in the time domain to determine the first deformation data information of the geologically sensitive area of ​​the power transmission channel.

[0113] Specifically, Baseline Set Assemblies (SBAS) are used to address low-resolution, large-scale deformation. The basic idea of ​​the SBAS algorithm is to divide the acquired SAR images into several sets, with smaller baseline distances within each set and larger baseline distances between sets. Then, the Singular Value Decomposition (SVD) method is used to combine the data from multiple small baseline sets for time-series inversion of surface deformation. Compared to PS-InSAR technology, SBAS uses multiple master images and selects small baseline interferometric pairs, enabling the detection of partially coherent targets that maintain coherence only within certain time periods. The number of these targets is far greater than the number of PS points, making it more suitable for suburban areas with fewer stable scatterers.

[0114] First, assume that there are N+1 SAR images of the same area, with corresponding time series (t0,...,t...). N Based on the spatiotemporal baseline threshold, M interference pairs are obtained, and M satisfies the following condition formula:

[0115]

[0116] Assuming the i-th interferogram, the acquisition times for the primary and secondary images are t and t, respectively. B and t B (t B >t B The differential interference phase at pixel x can be expressed as:

[0117]

[0118] In the formula d(t) B ,x) and d(t) A d(t0,x) represents the first deformation data in the radar line-of-sight (LOS) relative to the reference time t0, where d(t0,x) ≡ 0. This equation is a simplified model that ignores atmospheric and terrain errors in the phase. It is the differential phase after registration and correct unwrapping. Taking a certain highly coherent point as an example, the equation is established as follows:

[0119] Assume the i-th interferometric pair, i = 1, ..., M, and the time corresponding to the main image is t. IEi The auxiliary image is t ISi , and t IEi ≥t ISi The differential interference phase can be expressed as:

[0120] δφ i =φ(t) IEi )-φ(t ISi ), i = 1, ..., M

[0121] Converting the above expression into matrix form, we obtain the formula:

[0122] δφ=Aφ

[0123] A(M×N) is a coefficient matrix, with rows corresponding to interferograms and columns corresponding to SAR images. Equation 3-20 contains M equations and N unknowns. When M ≥ N and the rank of A (Rank(A)) = N, the unknowns can be solved using the least squares method, as follows:

[0124] φ=(A T A) -1 A T δφ

[0125] In actual solving, Rank(A) < N often occurs, which is related to the choice of interferometric pair. Suppose there are L distinct baseline sets in the interferometric pair, then Rank(A) = N - L + 1. In this case, Singular Value Decomposition (SVD) can be used for solving. However, some cumulative deformation jumps and discontinuities may occur during the solution process. A solution to this problem is to introduce the phase change rate between adjacent images:

[0126]

[0127] Substituting the above equation, we get:

[0128]

[0129] The new matrix equation can be expressed as:

[0130] δφ=Bv

[0131] Here, B is a matrix of the same dimension as A. Performing SVD decomposition on matrix B yields the minimum norm solution for vector v. Based on the deformation rate in each time interval, integrating the rate over the time domain yields the first deformation data for each time period. SBAS technology supports various nonlinear deformation models: v = M·P, where M is the coefficient matrix and P represents the unknown deformation parameters.

[0132] In addition, PS-InSAR technology can be used to calculate radar images from temporally coherent image sets to determine the first deformation data information of geologically sensitive areas of power transmission channels.

[0133] Optionally, the operation of calculating the difference between the point cloud data of the preceding and following time phases to determine the second deformation data information of the geologically sensitive area of ​​the power transmission channel includes:

[0134] The preceding and following time phase point cloud data are set to the detection point cloud data and the target point cloud data. The preceding and following time phase point cloud data includes the preceding time phase point cloud data and the following time phase point cloud data.

[0135] The point cloud data of the preceding and following time phases are classified to generate ground point data, and the ground point data is thinned based on slope entropy to determine the ground key point data.

[0136] The point cloud data from the preceding and following time phases are used to construct a network based on the thinned ground key point data to determine the network model.

[0137] Based on the network model, the difference between the point cloud data of the preceding and following time phases is calculated to determine the second deformation data information of the geologically sensitive area of ​​the power transmission channel.

[0138] Specifically, refer to Figure 5As shown, firstly, the preceding and following temporal point cloud data are assigned to the detection point cloud data and the target point cloud data. Following the forward detection principle, the preceding and following temporal point cloud data are sequentially assigned to the detection point cloud and the target point cloud, using the preceding temporal point cloud data as a baseline. Further, the preceding and following temporal point cloud data are classified to generate ground point data, and the ground point data is thinned based on slope entropy to determine key ground point data. Further, the preceding and following temporal point cloud data are networked based on the thinned key ground point data to determine the network model. The preceding and following temporal point cloud data undergo a TIN data structure design, and a data network is constructed based on the thinned ground points. Finally, the difference between the preceding and following temporal point cloud data is calculated according to the network model to determine the second deformation data information of the geologically sensitive area of ​​the transmission channel. Based on the improved ICP algorithm and processed by the slope entropy thinning operator, the final three-dimensional surface change detection strategy is formed, improving detection efficiency.

[0139] Therefore, the surface deformation change detection and analysis research proposed in this application, based on point set matching of the improved ICP algorithm and surface thinning calculation based on slope entropy, performs three-dimensional surface detection, which improves detection efficiency. This also solves the technical problem of low detection efficiency in existing technologies that are currently widely used for surface deformation detection based on manual measurement, INSAR, or multi-temporal lidar.

[0140] Optionally, it also includes: registering point cloud data of different time phases using a pre-set improved ICP point set matching algorithm to determine the point cloud data of the preceding and following time phases.

[0141] Specifically, the Iterative Closest Point (ICP) algorithm is a precise and efficient 2D and 3D shape registration algorithm. It can quickly and efficiently handle six-degree-of-freedom registration problems, and the matching result is independent of the shape representation. The algorithm is robust and reliable. Therefore, the ICP algorithm is currently widely used in complex laser point cloud stitching and remote sensing image (hyperspectral image, SAR image) registration, and performs well.

[0142] Currently, this algorithm has been studied and improved by many scholars both domestically and internationally, and has been widely applied in scientific research and production. For ease of explanation, we refer to the ICP algorithm proposed by PJBesl and NDMcKay as the basic ICP algorithm. This registration method can be used to describe point sets, folded lines, implicit curves, parametric curves, and various surfaces.

[0143] The basic ICP algorithm mainly includes corresponding point search and pose calculation, and usually adopts the principle of shortest distance between points (see...). Figure 6a (Left) and the principle of closest point-to-plane distance (see Figure 6bThe purpose is to find the optimal spatial matching relationship between different point sets and to solve the spatial translation and rotation relationships between point sets. Figure 6a and Figure 6b This is a 3D spatial illustration of point cloud registration at different time phases using the ICP algorithm. This improves the matching accuracy of point cloud data.

[0144] Optionally, the registration of point cloud data from different time phases using a pre-defined improved ICP point set matching algorithm to determine the point cloud data from previous and subsequent time phases includes: determining a first point set and a second point set for the point cloud data from different time phases, and matching the first point set and the second point set using nearest neighbor search to determine the corresponding point set; performing spatial parameter transformation on the corresponding point set using an iterative algorithm until the corresponding point set converges to determine the point cloud data from previous and subsequent time phases. Optionally, it also includes: removing error data from the corresponding point set based on a robust estimation method.

[0145] Specifically, for a more intuitive mathematical description, this application uses M and P to represent two different mathematical point sets. Here, P represents the set of points to be registered, and M represents the set of reference data points. The basic ICP algorithm calculation flow can then be expressed as:

[0146] (1) Nearest neighbor search. Randomly select any point i from the point set P, denoted by p(i). Traverse the point set M and search for the nearest point m(i). In this way, (pi, mi) successfully constructs the first set of corresponding points;

[0147] (2) The spatial transformation parameters are solved based on an iterative algorithm, where we use (R, T) to represent their transformation relationship. To calculate the transformation relationship as accurately as possible, we usually use an iterative calculation method.

[0148] (3) According to the precision threshold specified by the user, when each point in the point set P1 is converted to P2 through the iterative change relationship, if the precision meets the threshold condition, the iteration stops. This can be expressed by the following formula.

[0149]

[0150] Among them, E d (r, T) is the objective function, R r Let T be the spatial rotation parameter and T be the spatial translation parameter.

[0151] (4) Repeat the iteration on the point set P until the end.

[0152] In the process of lidar point cloud matching, the core of matching two temporal point sets using the ICP matching algorithm is the accurate estimation of three-dimensional similarity changes using the least squares method. However, during data processing, although some errors are eliminated through data preprocessing, some errors inevitably remain. Least squares-based calculations do not possess the characteristic of optimal estimation, ultimately leading to non-convergence or erroneous calculation results. To enhance resistance to gross errors during adjustment calculations, a robust estimation-based elimination method is employed to mitigate the impact of matching point errors caused by gross errors.

[0153] Since the M-estimation is theoretically a type of maximum likelihood estimation, we can express it using the following formula:

[0154]

[0155] Here we assume:

[0156]

[0157] Substituting this into the equation simplifies it to:

[0158] ∑w i v i a i =0

[0159] Thus, the M-estimation can be transformed into a least-squares estimate based on a weighted iterative approach. The Huber weight function can be modified as follows:

[0160]

[0161] Optionally, the operation of thinning the ground point data based on slope entropy to determine the ground key point data includes: calculating the slope of the triangular slope surface of the ground point data, and calculating multiple slope entropies of multiple local terrains in the ground point data according to the slope; sorting the multiple slope entropies, and removing the ground points corresponding to the slope entropies according to a pre-set precision threshold to determine the ground key point data.

[0162] Specifically, "slope entropy" is a quantitative indicator expressing the slope characteristics of urban surfaces and building areas, used to measure the complexity of regional terrain. Here, slope entropy is defined as follows:

[0163]

[0164]

[0165] In the formula, i represents the nth slope surface, Si represents the slope value of the i-th slope surface, and n is the number of slope surfaces actually used in the calculation. H(S) is the mathematical expression of slope entropy.

[0166] In the process of terrain representation, the characteristics of slope entropy include:

[0167] (1) Slope entropy is a mathematical expression of the real surface conditions. Its value is based on the real surface model and does not require any assumptions. The calculation result is related to the slope surface constructed by the laser point cloud. When the actual point cloud density is high, there are many slope surfaces constructed, and the calculation complexity is high. In order to improve the calculation efficiency, point cloud thinning is required on the basis of satisfying the terrain expression accuracy.

[0168] (2) The result of the surface slope entropy calculation is a non-negative numerical expression. The value directly determines the complexity of the surface, that is, the complexity of the terrain. When the slope entropy value is larger, it indicates that the surface texture features are rich, but the surface changes are small and the terrain undulations are relatively gentle; when the slope entropy value is smaller, it indicates that the surface texture is simple and the terrain changes drastically.

[0169] When constructing ground models using LiDAR point cloud data, ground points that have been filtered and classified are typically used. In densely vegetated areas, the number of ground points penetrated is limited, resulting in a smaller data volume and significant differences in slope values ​​across the TIN model surface. However, on flat or bare ground, LiDAR points are almost always ground points, leading to a massive data volume and a very large TIN model surface, although the slope values ​​tend to be uniform. In such cases, point cloud thinning is necessary to improve retrieval efficiency.

[0170] When thinning airborne LiDAR point clouds, two factors need to be considered in order to balance terrain representation and thinning scale:

[0171] (1) Preserve terrain features and extract key areas and key points based on ground point classification results;

[0172] (2) The point cloud thinning scale should be consistent with the DEM representation accuracy, while ensuring that the key point locations are reasonably distributed (uniformly distributed).

[0173] refer to Figure 7 As shown, the specific steps of the slope entropy-based thinning algorithm include:

[0174] (1) Classify the loaded airborne LiDAR point cloud data according to the multi-level filtering classification method based on feature attributes to generate ground points;

[0175] (2) Following the principle of network construction, a TIN model under constraints is constructed based on ground points;

[0176] Read the first vertex in the triangulation and find adjacent triangles based on spatial properties;

[0177] (3) According to the grid division, read the data of the first triangular network in the first grid, and find the adjacent triangles of the first triangle based on spatial attributes and topological relationships;

[0178] (4) Calculate the slope of all triangular faces adjacent to the central apex in sequence;

[0179] (5) Based on the block file, calculate the slope entropy of the local terrain in the block;

[0180] (6) Traverse all grid data blocks and calculate the slope entropy within the experimental range;

[0181] (7) Set an entropy threshold and effectively select or discard data points based on the threshold. The entropy threshold is the elevation difference between the triangular mesh model constructed from key points and the triangular mesh model constructed from terrain points. For example... Figure 8 As shown, 0.15m is the entropy threshold set by the user; only data points exceeding this range are retained.

[0182] Optionally, the method further includes: preprocessing the preceding and following time-phase point cloud data sequentially based on the preceding time-phase point cloud data. Optionally, the operation of preprocessing the preceding and following time-phase point cloud data sequentially based on the preceding time-phase point cloud data includes: using the preceding time-phase point cloud data as a reference, sequentially performing flight strip adjustment calculation based on connecting lines and multi-level filtering processing based on feature attributes on the preceding and following time-phase point cloud data.

[0183] Specifically, refer to Figure 5 As shown, the quality of point cloud data can be improved by preprocessing the point cloud data.

[0184] Optionally, the operation of calculating the difference between the point cloud data of the preceding and following time phases based on the network model to determine the second deformation data information of the geologically sensitive area of ​​the transmission channel further includes: calculating the difference between the point cloud data of the preceding and following time phases based on the network model to determine the difference result; and refining the difference result based on the detection of the change area of ​​the point cloud data of the preceding and following time phases to determine the second deformation data information of the geologically sensitive area of ​​the transmission channel.

[0185] Specifically, refer to Figure 5 As shown, by refining the differential results, the deformation detection results are made more accurate, thus enabling better accuracy analysis and evaluation. This achieves the technical effect of accurately calculating the second deformation data information of the geologically sensitive area of ​​the power transmission channel.

[0186] In addition, refer to Figure 5 As shown, the deformation detection method for geologically sensitive areas of power transmission channels based on lidar scanning data, using an improved ICP algorithm and processed by a slope moisture thinning operator, forms the final three-dimensional surface change detection strategy. The specific steps include:

[0187] (1) Based on the forward detection principle, the point clouds of the preceding and following time phases are set to the detection point cloud and the target point cloud in sequence, with the point cloud of the preceding time phase as the reference. The point cloud data preprocessing performs strip adjustment calculation based on connecting lines and multi-level filtering processing based on feature attributes on the point cloud data of the preceding and following time phases in sequence.

[0188] (2) The point cloud data of the preceding and following time phases are classified into point clouds and thinned based on slope entropy;

[0189] (3) Design a TIN data structure for the point cloud data of the previous and next time phases, and construct a data network based on the thinned ground points;

[0190] (4) Perform difference calculation based on the network model in step three, and refine the calculation results.

[0191] (5) Analyze and evaluate the accuracy of change detection based on the calculation results.

[0192] Optionally, the operation of using BeiDou differential positioning technology to determine the absolute deformation data of towers in geologically sensitive areas of power transmission channels includes:

[0193] It receives two BeiDou satellite navigation timing data sets collected in real time at predetermined intervals from two pre-set BeiDou tower monitoring devices, where the BeiDou tower monitoring devices are used to monitor the towers;

[0194] The first and second time-series position information of the tower were obtained by calculating the two BeiDou satellite navigation time-series data using the Kalman filter model.

[0195] Based on the first temporal position information of the tower, the first deformation information of the tower is determined, and based on the second temporal position information of the tower, the second deformation information of the tower is determined.

[0196] Based on the first deformation information and the second deformation information, the absolute deformation data of the tower is determined.

[0197] Specifically, refer to Figure 9 The diagram shows the structure of the BeiDou pole monitoring equipment, which is referred to as the monitoring equipment. The monitoring equipment consists of a main control module, a BeiDou RTK module, an tilt measurement module, a wireless transmission module, a data storage module, a security encryption module, and a power management module. Through the peripheral management module, it can be expanded to connect external stress sensors, meteorological sensors, and other devices. It has functions such as RTK positioning, attitude angle measurement, internal data storage, and measurement data reporting. It can report the attitude change measurement information of the power pole to the monitoring center through a mobile communication network or local storage.

[0198] The software system can automatically collect data, automatically analyze deformation, automatically predict and warn, and automatically generate dynamic curves of single and cumulative measurement data, as well as dynamic curves of deformation rate changes. The system is designed with a B / S architecture, allowing users to query monitoring status via a webpage. Monitored data changes are displayed intuitively as curves. It includes modules for displacement vector analysis, historical data query, hierarchical user management, and hierarchical alarms. It can display monitoring structure diagrams and sensor layout diagrams, and the storage module is a database.

[0199] The BeiDou reference station receiver and BeiDou tower tilt deformation monitoring equipment collect data in real time at certain sampling intervals, performing calculations such as quality inspection, baseline processing, and network adjustment. Real-time location results of monitoring points are obtained through RTK real-time positioning; relative deformation results over time are obtained through epoch-level sliding processing; absolute deformation results are obtained through two deformation monitoring devices; tilt sensors and stress sensors collect monitoring data in real time and store it locally. When the 4G mobile communication network is available, real-time monitoring results, including real-time location results, relative deformation results, and absolute deformation results, are reported to the monitoring center via the 4G network. When the 4G network is unavailable, the data stored locally on the equipment is exported for use.

[0200] The main functions are as follows:

[0201] (1) Automated monitoring function

[0202] It can automatically collect, transmit, store, process and analyze monitoring data, and has the ability to conduct real-time monitoring under various climatic conditions.

[0203] (2) Alarm function

[0204] The alarm system is divided into three levels: Level 1, Level 2, and Level 3; it supports concurrent alarms from multiple locations and events. It automatically displays alarms and allows users to query detailed alarm information, including alarm type, alarm location, alarm level, alarm monitoring value, alarm occurrence time, alarm confirmation time, alarm recovery time, confirmer, and alarm status.

[0205] (3) Online analysis and early warning function

[0206] Based on relevant monitoring data and historical monitoring data, the operating status and changing trends of the towers are predicted and analyzed to provide early warning information.

[0207] (4) Access control function

[0208] It features the ability to add, delete, modify, and query user information; and allows for the setting and management of different levels of user permissions.

[0209] (5) Query and statistics functions

[0210] It has the function of querying and statistically analyzing historical alarm information based on multiple conditions such as alarm time, alarm location, alarm type, and alarm level.

[0211] It features the ability to query daily, monthly, and yearly statistical reports of monitoring data and provide monitoring data curves.

[0212] Further, refer to Figure 10 As shown, the BeiDou Navigation Satellite System is a crucial national space infrastructure. The BeiDou-3 system was officially launched on July 31, 2020, providing high-quality PNT (Power Utility Telemetry) services. The "White Paper on the BeiDou Standard System for the Power Industry" was issued in 2020, providing guidance for the application of the BeiDou system in the power sector.

[0213] Among them, reference Figure 10 As shown, RTK (Real-time kinematic) carrier phase differential technology is a method for processing the carrier phase observations of two measurement stations in real time. It sends the carrier phase collected by the reference station to the user receiver to calculate the coordinates by difference.

[0214] The predetermined interval can be half an hour, one hour, etc., and is not limited here; it is determined by the user according to their needs. Because BeiDou tower monitoring equipment may have errors, two BeiDou tower monitoring devices are used to determine the absolute deformation data of the tower. Alternatively, this application can also use one BeiDou tower monitoring device to detect tower deformation information, or it can use two or more BeiDou tower monitoring devices simultaneously.

[0215] Specifically, to address the deformation detection problem in geologically sensitive areas of power transmission channels based on BeiDou differential positioning technology, this application proposes a high-precision real-time differential positioning technology compatible with BeiDou-3. Based on real-time data collected from reference stations and monitoring stations, and through models such as double-difference observation, Kalman filter estimation, and ambiguity fixing, high-precision real-time differential positioning compatible with BeiDou-3 is achieved. This enables real-time monitoring of power transmission channel tower deformation information, and consequently, allows for real-time monitoring of power transmission channel deformation information through tower deformation data.

[0216] Furthermore, considering the characteristics of GNSS monitoring station displacement deformation and tower tilt deformation, adaptive verification quantities suitable for tower surface and tower body deformation are constructed. For example, the collected data mainly includes: BeiDou satellite navigation messages (i.e., BeiDou satellite navigation time-series data): These messages are broadcast by navigation satellites to users, describing the operational status parameters of the navigation satellites, including system time, ephemeris, almanac, satellite clock correction parameters, navigation satellite health status, and ionospheric delay model parameters. The navigation message parameters provide users with time information, which can be used to calculate the user's position coordinates and velocity. Tower tilt data: Acquired by the tilt measurement module, calculating the angle between the tower centerline and the vertical line (gravity line). This is used to determine whether the tower has tilted. Attitude angle information: Calculated jointly by the tilt measurement module and stress sensors, this is a three-dimensional concept, using three-axis (XYZ) tilt angles to describe the three-dimensional attitude of the transmission tower. Meteorological data: Temperature, wind speed, and other information acquired by micro-meteorological sensors. Adaptive verification of absolute deformation data of transmission line towers is performed using tower tilt data, attitude angle information, and meteorological data. For example, adaptive verification can integrate multi-source deformation monitoring information, combined with the overall and local structural physical and mechanical state of the transmission line tower, the working status of important parts, and the environmental conditions, to perform threshold assessment of the deformation state of the transmission line tower, and achieve baseline calculation, coordinate calculation, accuracy estimation, offset, and deformation calculation.

[0217] Optionally, it also includes:

[0218] Errors in BeiDou satellite navigation timing data are eliminated based on a pre-set double-difference observation equation.

[0219] Specifically, the double-difference observation equation is constructed as follows:

[0220] Assuming that the base station u and the monitoring station r simultaneously observe satellites i and j, the pseudorange and carrier phase double-difference observation equations in units of distance can be expressed as:

[0221]

[0222]

[0223] In the two equations above, Δ▽ is the double-difference operator, ρ is the geometric distance between the satellite and the receiver, φ is the carrier phase observation, P is the code pseudorange observation, N is the integer ambiguity, λ is the carrier wavelength, f is the frequency, I is the ionospheric delay, T is the tropospheric delay, ε is the pseudorange observation noise and the residual error after double-difference, and ξ is the phase observation noise and the residual error after double-difference.

[0224] For short baselines (generally considered to be within 10 km), it can be approximated that the double-difference ionospheric and tropospheric terms are completely eliminated. In this case, the double-difference observation equation can be further simplified to:

[0225]

[0226]

[0227] Assuming that u and r simultaneously observe mB+1 bd satellites at the same epoch, the linearized error equation at the s-th frequency is:

[0228]

[0229] in, These are carrier and pseudorange double-difference observations, respectively. For designing the matrix; X is the integer ambiguity parameter; X is the 3D baseline vector parameter; Λ s This is the ambiguity coefficient matrix.

[0230] Linearizing the double-difference observation equation for multiple frequencies at a single epoch yields:

[0231]

[0232] in, This refers to the double-difference integer ambiguity parameter; Λ=diag(Λ1,…Λ f I is the identity matrix; The product is the Dirichlet product; diag() represents a diagonal matrix.

[0233] Optionally, it also includes:

[0234] Preprocessing of BeiDou satellite navigation time series data.

[0235] Optionally, preprocessing operations for BeiDou satellite navigation time-series data include:

[0236] Baseline processing is performed on the BeiDou satellite navigation time series data;

[0237] Network adjustment processing was performed on the BeiDou satellite navigation time series data.

[0238] Specifically, baseline processing involves using observational data (location information data) collected synchronously by multiple GPS receivers in the field to determine the baseline vectors and their variance-covariance matrices between the receivers. The baseline calculation results are used not only for subsequent network adjustment but also to verify and evaluate the quality of the field observations. The baseline vectors provide the relative positional relationships between points and belong to the same reference frame as the satellite ephemeris used in the calculation. These baseline vectors determine the geometry and orientation of the GPS network. (GPS baseline calculation - die_job blog - CSDN blog - baseline calculation)

[0239] Network adjustment is the final stage of data processing. In this stage, the baseline vectors determined during baseline calculation are treated as observations. The posterior variance-covariance matrix of the baseline vectors is used to determine the weight matrix of the observations. Appropriate initial data is introduced, and the coordinates of each point in the network are determined through parameter estimation. Network adjustment can also identify gross errors in the observations and address them using appropriate methods. Furthermore, it can eliminate geometric inconsistencies caused by baseline vector errors and assess the accuracy of the observation results.

[0240] Optionally, the operation of calculating the first and second time-series position information of the tower by using a Kalman filter model on two BeiDou satellite navigation time-series data includes:

[0241] The floating-point solutions for the first and second time-series position information were obtained by calculating the two BeiDou satellite navigation time-series data using the Kalman filter model.

[0242] The ambiguity of the floating-point solutions of the first and second time-series position information is fixed to determine the first and second time-series position information.

[0243] Specifically, the Kalman filter model:

[0244] The Kalman filter formula for linear systems is used for estimation. The filter model can be expressed as:

[0245]

[0246] In the formula, x k Let z be an n-dimensional state vector. k Let w represent an m-dimensional observation vector and a random variable. k-1 ,v k Let represent the process noise vector and the measurement noise vector, respectively. They are assumed to be white noise with a mean of 0 and to follow a normal distribution:

[0247] p(w)~N(0,Q)

[0248] p(v)~N(0,R)

[0249] Where Q and R represent the variances of process noise and measurement noise, respectively.

[0250] A k H represents an n×n dimensional state transition matrix. k This represents an m×n dimensional measurement matrix.

[0251] The filtering process consists of the following 5 steps:

[0252] State prediction:

[0253]

[0254] Prediction variance:

[0255]

[0256] Filter gain:

[0257]

[0258] State filtering:

[0259]

[0260] Filter variance:

[0261]

[0262] Based on the linearized observation equation, the floating-point solution of the monitoring station is obtained in real time using Kalman filtering. Then, the ambiguity is fixed to obtain the real-time accurate coordinates of the monitoring station.

[0263] In addition, refer to Figure 1 As shown, according to a third aspect of this embodiment, a storage medium is provided. The storage medium includes a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0264] Therefore, according to this embodiment, the deformation detection technology for geologically sensitive areas of power transmission channels, which integrates SAR, laser scanning data, and BeiDou positioning data, utilizes high-precision point monitoring data acquired through multi-temporal laser scanning measurements and GNSS (BeiDou positioning) to verify the effectiveness of large-scale area monitoring information obtained by InSAR technology. Then, data fusion calculations are performed at overlapping points. Finally, the fused data is used to correct the monitoring values ​​at all points, obtaining high-precision area settlement information over a large area. Furthermore, the fused results not only ensure the accuracy and reliability of the leveling data but also possess the high-resolution advantage of InSAR measurements, effectively depicting the detailed information of deformation in geologically sensitive areas of power transmission channels.

[0265] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0266] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0267] Example 2

[0268] Figure 11 A deformation detection device 1100 for a geologically sensitive area of ​​a power transmission corridor according to this embodiment is shown. This device 1100 corresponds to the method described according to the first aspect of Embodiment 1. (See reference...) Figure 11 As shown, the device 1100 includes: a first determining module 1110, used to calculate radar image sets within the geologically sensitive area of ​​the power transmission channel using small baseline set interferometry technology to determine first deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the first deformation data information is wide-area planar deformation information within the geologically sensitive area of ​​the power transmission channel; a second determining module 1120, used to calculate the difference between consecutive temporal point cloud data to determine second deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the second deformation data information is strip-shaped deformation information of the power transmission channel within the geologically sensitive area of ​​the power transmission channel; a third determining module 1130, used to determine absolute deformation data information of the towers within the geologically sensitive area of ​​the power transmission channel using BeiDou differential positioning technology, wherein the absolute deformation data information of the towers is point-like deformation information of the power transmission towers within the geologically sensitive area of ​​the power transmission channel; and a fourth determining module 1140, used to determine ground subsidence information of the geologically sensitive area of ​​the power transmission channel based on the first deformation data information, the second deformation data information, and the absolute deformation data information of the towers.

[0269] Optionally, the fourth determining module 1140 includes: a first determining submodule, used to extract corresponding points from the first and second absolute deformation data of the tower using a nearest neighbor weighted average algorithm, and to determine the overlapping point data; a second determining submodule, used to fuse the overlapping point data of the tower absolute deformation data using a pre-set data fusion algorithm, and to determine the fused data value of the overlapping point; a third determining submodule, used to correct the fused data value of the tower absolute deformation data of the overlapping point using a pre-set monitoring value correction algorithm, and to determine the corrected data value of the overlapping point; and a fourth determining submodule, used to determine the ground subsidence information of the tower absolute deformation data in the geologically sensitive area of ​​the power transmission channel based on the first and corrected data values ​​of the tower absolute deformation data.

[0270] Optionally, the second determining module 1120 includes: a fifth determining submodule, used to register and resample a predetermined number N radar images within the geologically sensitive area of ​​the power transmission channel onto the same main image to determine an image set, wherein the dimension of the image set is N;

[0271] The sixth determination submodule 420 is used to calculate the pixels of each radar image in the image set and determine the coherence between distributed target points and permanent scattering points in each radar image in the image set.

[0272] The seventh determination submodule 430 is used to calculate the temporal coherence dimension image for each radar image in the image set using the maximum likelihood estimation algorithm, and add the temporal coherence dimension image to the image set to determine the temporal coherence image set, wherein the dimension of the temporal coherence image set is N+1.

[0273] The eighth determination submodule 440 is used to calculate the deformation of the geologically sensitive area of ​​the power transmission channel based on the radar image of the temporal coherence image set using the small baseline set interferometry technique.

[0274] Optionally, the sixth determining submodule 420 includes:

[0275] The first determining unit is used to sort the amplitude values ​​of pixels in each radar image in the image set and determine the cumulative distribution function of the probability density function of the pixels.

[0276] The test unit is used to test whether the first pixel and the second pixel of each radar image in the image set are homogeneous pixels according to the cumulative distribution function and through a non-parametric test method, wherein the first pixel and the second pixel are any two pixels of the radar image.

[0277] The second determining unit is used to determine the homogeneous region of the first pixel when the first pixel and the second pixel are homogeneous pixels.

[0278] The third determining unit is used to determine the complex coherence matrix between the distributed target point and the permanent scattering point based on the homogeneous region;

[0279] The fourth determining unit is used to determine the interferogram of the complex elements of the complex coherence matrix based on the complex coherence matrix. The interferogram is used to describe the coherence between the distributed target point and the permanent scattering point.

[0280] Optionally, the inspection subunit includes:

[0281] The inspection sub-unit is used to check whether two pixels in each radar image in the image set are homogeneous pixels based on the cumulative distribution function and the parametric two-tailed test method.

[0282] Optionally, the seventh determining submodule 430 includes:

[0283] The fifth determining unit is used to calculate the true phase of each radar image in the image set using the maximum likelihood estimation method.

[0284] The sixth determining unit is used to replace the target phase of the distributed target points in each radar image with the true phase to determine the evaluation index of temporal coherence.

[0285] Based on the evaluation index of temporal coherence, the temporal coherence dimension image is determined.

[0286] Optionally, the eighth determining submodule 440 includes:

[0287] The seventh determining unit is used to determine multiple interferometric pairs based on the spatiotemporal baseline threshold and the temporal coherence image set;

[0288] The first calculation unit is used to calculate the phase change rate between two adjacent radar images in the temporal coherence image set.

[0289] The eighth determining unit is used to determine the deformation rate based on the interference pair and the phase change rate;

[0290] The ninth determining unit is used to perform integral calculations of the deformation rate of the geologically sensitive area of ​​the transmission channel in each time interval in the time domain, and to determine the deformation of the geologically sensitive area of ​​the transmission channel.

[0291] Optionally, the third determining module 1130 includes: a setting submodule 710, used to set the preceding and following time-phase point cloud data to the detection point cloud data and the target point cloud data, wherein the preceding and following time-phase point cloud data includes the preceding time-phase point cloud data and the following time-phase point cloud data; a generating submodule 720, used to classify the preceding and following time-phase point cloud data, generate ground point data, and thin the ground point data based on slope entropy to determine the ground key point data; a ninth determining submodule 730, used to construct a network based on the thinned ground key point data of the preceding and following time-phase point cloud data to determine the network model; and a tenth determining submodule 740, used to calculate the difference between the preceding and following time-phase point cloud data according to the network model to determine the deformation of the geologically sensitive area of ​​the transmission channel.

[0292] Optionally, the third determining module 1130 further includes an eleventh determining submodule, used to determine the point cloud data of the preceding and following time phases by registering the point cloud data of different time phases through a pre-set improved ICP point set matching algorithm.

[0293] Optionally, the eleventh determining submodule includes: a tenth determining unit, used to determine the first point set and the second point set of point cloud data at different time phases, and to match the first point set and the second point set by nearest neighbor search to determine the corresponding point set; and an eleventh determining unit, used to perform spatial parameter transformation on the corresponding point set by an iterative algorithm until the corresponding point set converges to determine the point cloud data at different time phases.

[0294] Optionally, the third determining module 1130 further includes an error removal submodule, used to remove error data from the corresponding point set based on a robust estimation method.

[0295] Optionally, the ninth determining submodule includes: a second calculation unit, used to calculate the slope of the triangular slope surface of the ground point data, and calculate multiple slope entropies of multiple local terrains in the ground point data according to the slope; and a twelfth determining unit, used to sort the multiple slope entropies, and remove the ground points corresponding to the slope entropies according to a preset accuracy threshold, to determine the ground key point data.

[0296] Optionally, the third determining module 1130 further includes: a first preprocessing submodule, used to preprocess the preceding and following time phase point cloud data sequentially based on the preceding time phase point cloud data.

[0297] Optionally, the first preprocessing submodule includes: a preprocessing subunit, used to take the previous time phase point cloud data as a reference and sequentially perform flight strip adjustment calculation based on the connecting line and multi-level filtering processing based on feature attributes on the previous and next time phase point cloud data.

[0298] Optionally, the tenth determining submodule further includes: a thirteenth determining unit, used to perform difference calculation on the point cloud data of the preceding and following time phases based on the network model, and determine the difference results; and a fourteenth determining unit, used to perform refined calculation on the difference results based on the detection of the change area of ​​the point cloud data of the preceding and following time phases, and determine the deformation of the geologically sensitive area of ​​the power transmission channel.

[0299] Optionally, the fourth determining module 1140 includes: a receiving submodule 510, used to collect two BeiDou satellite navigation timing data in real time at predetermined intervals by two pre-set BeiDou tower monitoring devices, wherein the BeiDou tower monitoring devices are used to monitor towers;

[0300] The calculation submodule 520 is used to calculate the first and second time-series position information of the tower by using the Kalman filter model to calculate the two BeiDou satellite navigation time-series data.

[0301] The eleventh determining submodule 530 is used to determine the first deformation information of the tower based on the first time-series position information of the tower, and to determine the second deformation information of the tower based on the second time-series position information of the tower.

[0302] The twelfth determination submodule 540 is used to determine the absolute deformation information of the tower based on the first deformation information and the second deformation information.

[0303] Optionally, the fourth determining module 1140 further includes:

[0304] The error elimination submodule is used to eliminate errors in BeiDou satellite navigation timing data according to a pre-set double-difference observation equation.

[0305] Optionally, the fourth determining module 1140 further includes:

[0306] The second preprocessing submodule is used to preprocess the BeiDou satellite navigation timing data.

[0307] Optionally, the second preprocessing module includes:

[0308] The baseline processing unit is used to perform baseline processing on BeiDou satellite navigation time series data.

[0309] The network adjustment processing unit is used to perform network adjustment processing on BeiDou satellite navigation time series data.

[0310] Optionally, the computation submodule includes:

[0311] The third calculation unit is used to calculate the two BeiDou satellite navigation time series data through the Kalman filter model to obtain the floating-point solutions of the first time series position information and the second time series position information;

[0312] The fifteenth determining unit is used to fix the ambiguity of the floating-point solutions of the first time-series position information and the second time-series position information, and to determine the first time-series position information and the second time-series position information.

[0313] Therefore, according to this embodiment, the deformation detection technology for geologically sensitive areas of power transmission channels, which integrates SAR, laser scanning data, and BeiDou positioning data, utilizes high-precision point monitoring data acquired through multi-temporal laser scanning measurements and GNSS (BeiDou positioning) to verify the effectiveness of large-scale area monitoring information obtained by InSAR technology. Then, data fusion calculations are performed at overlapping points. Finally, the fused data is used to correct the monitoring values ​​at all points, obtaining high-precision area settlement information over a large area. Furthermore, the fused results not only ensure the accuracy and reliability of the leveling data but also possess the high-resolution advantage of InSAR measurements, effectively depicting the detailed information of deformation in geologically sensitive areas of power transmission channels.

[0314] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0315] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0316] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0317] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0318] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0319] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0320] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for detecting deformation in geologically sensitive areas of power transmission channels, characterized in that, include: Using small baseline set interferometry, radar image sets within the geologically sensitive area of ​​the power transmission channel are calculated to determine the first deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the first deformation data information is the wide-area surface deformation information within the geologically sensitive area of ​​the power transmission channel; The difference between the point cloud data of the preceding and following time phases is calculated to determine the second deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the second deformation data information is the strip-shaped deformation information of the power transmission channel within the geologically sensitive area of ​​the power transmission channel; Using BeiDou differential positioning technology, the absolute deformation data of the towers in the geologically sensitive area of ​​the power transmission channel is determined, wherein the absolute deformation data of the towers is the point deformation information of the power transmission towers in the geologically sensitive area of ​​the power transmission channel; Based on the first deformation data, the second deformation data, and the absolute deformation data of the tower, the ground subsidence information of the geologically sensitive area of ​​the power transmission channel is determined; The operation of determining the ground subsidence information of the geologically sensitive area of ​​the power transmission channel based on the first deformation data information, the second deformation data information, and the absolute deformation data information of the tower includes: The nearest neighbor weighted average algorithm is used to extract corresponding points from the second deformation data information and the absolute deformation data information of the tower on the first deformation data information to determine the overlapping point data. The overlapping point data is fused using a pre-set data fusion algorithm to determine the fused data value of the overlapping points; The fused data values ​​of the overlapping points are corrected using a pre-set monitoring value correction algorithm to determine the corrected data values ​​of the overlapping points. Based on the first deformation data information and the corrected data value, the ground subsidence information of the geologically sensitive area of ​​the power transmission channel is determined; The operation of calculating the difference between point cloud data from different time phases to determine the second deformation data information of the geologically sensitive area of ​​the power transmission channel includes: The preceding and following time phase point cloud data are set to the detection point cloud data and the target point cloud data, wherein the preceding and following time phase point cloud data includes the preceding time phase point cloud data and the following time phase point cloud data. The preceding and following time-phase point cloud data are classified to generate ground point data, and the ground point data is thinned based on slope entropy to determine the ground key point data. The point cloud data of the preceding and following time phases are used to construct a network based on the thinned ground key point data to determine the network model; Based on the network model, the difference between the preceding and following temporal point cloud data is calculated to determine the second deformation data information of the geologically sensitive area of ​​the power transmission channel. The operation of determining the absolute deformation data of towers in the geologically sensitive area of ​​the power transmission channel using BeiDou differential positioning technology includes: The system receives two BeiDou satellite navigation timing data sets collected in real time at predetermined intervals from two pre-set BeiDou tower monitoring devices, wherein the BeiDou tower monitoring devices are used to monitor towers. The first and second time-series position information of the tower are obtained by calculating the two BeiDou satellite navigation time-series data using a Kalman filter model. Based on the first temporal position information of the tower, the first deformation information of the tower is determined, and based on the second temporal position information of the tower, the second deformation information of the tower is determined. The absolute deformation data of the tower is determined based on the first deformation information and the second deformation information.

2. The method according to claim 1, characterized in that, The operation of calculating radar image sets within the geologically sensitive area of ​​the power transmission channel using small baseline set interferometry to determine the first deformation data information of the geologically sensitive area includes: After registering a predetermined number N radar images within the geologically sensitive area of ​​the power transmission channel, they are resampled onto the same main image to determine an image set, wherein the dimension of the image set is N. The coherence between distributed target points and permanent scattering points in each radar image in the image set is determined by calculating the pixels of each radar image in the image set. The maximum likelihood estimation algorithm is used to calculate the temporal coherence dimension image for each radar image in the image set, and the temporal coherence dimension image is added to the image set to determine the temporal coherence image set, wherein the dimension of the temporal coherence image set is N+1. Based on the small baseline set interferometry technique, the radar images of the time coherence image set are calculated to determine the first deformation data information of the geologically sensitive area of ​​the power transmission channel.

3. The method according to claim 2, characterized in that, The operation of calculating the coherence between distributed target points and permanent scattering points in each radar image of the image set includes: The amplitude values ​​of pixels in each radar image in the image set are sorted to determine the cumulative distribution function of the probability density function of the pixels. Based on the cumulative distribution function, a non-parametric test method is used to test whether the first pixel and the second pixel of each radar image in the image set are homogeneous pixels, wherein the first pixel and the second pixel are any two pixels of the radar image. If the first pixel and the second pixel are homogeneous pixels, determine the homogeneous region of the first pixel; Based on the homogeneous region, determine the complex coherence matrix between the distributed target point and the permanent scattering point; Based on the complex coherence matrix, an interferogram of the complex elements of the complex coherence matrix is ​​determined, and the interferogram is used to describe the coherence between the distributed target point and the permanent scattering point.

4. The method according to claim 3, characterized in that, The operation of verifying whether the first and second pixels of each radar image in the image set are homogeneous pixels using a non-parametric testing method includes: Based on the cumulative distribution function, the two pixels of each radar image in the image set are tested to see if they are homogeneous pixels using a parameterless two-tailed test.

5. The method according to claim 2, characterized in that, The operation of determining the temporal coherence dimension of each radar image in the image set using the maximum likelihood estimation algorithm includes: The maximum likelihood estimation method is used to calculate the true phase of each radar image in the image set. The true phase is used to replace the target phase of the distributed target point in each radar image to determine the evaluation index of temporal coherence. Based on the evaluation index of temporal coherence, the temporal coherence dimension image is determined.

6. The method according to claim 2, characterized in that, The operation of calculating the first deformation data information of the geologically sensitive area of ​​the power transmission channel based on the small baseline set interferometry technique includes: Based on the spatiotemporal baseline threshold and the aforementioned temporal coherence image set, multiple interference pairs are determined; Calculate the phase change rate between two adjacent radar images in the temporal coherence image set; The deformation rate is determined based on the interference pair and the phase change rate. The deformation rate of the geologically sensitive area of ​​the power transmission channel in each time interval is integrated in the time domain to determine the first deformation data information of the geologically sensitive area of ​​the power transmission channel.

7. The method according to claim 1, characterized in that, Also includes: The point cloud data of different time phases are determined by registering the point cloud data of different time phases using a pre-set improved ICP point set matching algorithm.

8. The method according to claim 7, characterized in that, The operation of registering point cloud data of different time phases using a pre-set improved ICP point set matching algorithm to determine the preceding and following time phase point cloud data includes: A first point set and a second point set are determined from point cloud data of different time phases, and the first point set and the second point set are matched by nearest neighbor search to determine the corresponding point set; The spatial parameters of the corresponding point set are transformed by an iterative algorithm until the corresponding point set converges, thereby determining the point cloud data of the preceding and following time phases.

9. The method according to claim 8, characterized in that, Also includes: Error data is removed from the corresponding point set based on a robust estimation method.

10. The method according to claim 1, characterized in that, The operation of thinning the ground point data based on slope entropy to determine the ground key point data includes: The slope of the triangular slope surface is calculated from the ground point data, and based on the slope, multiple slope entropies of multiple local terrain features in the ground point data are calculated. The multiple slope entropies are sorted, and the ground points corresponding to the slope entropies are removed according to a pre-set accuracy threshold to determine the ground key point data.

11. The method according to claim 1, further comprising: The point cloud data of the preceding and following time phases are preprocessed sequentially based on the preceding time phase point cloud data.

12. The method according to claim 11, characterized in that, The operation of preprocessing the preceding and following temporal point cloud data sequentially based on the preceding temporal point cloud data includes: Using the preceding time-phase point cloud data as a benchmark, the preceding and following time-phase point cloud data are sequentially subjected to flight strip adjustment calculations based on connecting lines and multi-level filtering processing based on feature attributes.

13. The method according to claim 1, characterized in that, The operation of calculating the difference between the preceding and following temporal point cloud data based on the network model to determine the second deformation data information of the geologically sensitive area of ​​the power transmission channel also includes: The difference between the preceding and following temporal point cloud data is calculated based on the network model to determine the difference results; Based on the detection of changes in the point cloud data before and after the time phases, the difference results are refined and calculated to determine the second deformation data information of the geologically sensitive area of ​​the power transmission channel.

14. The method according to claim 1, characterized in that, Also includes: Errors in the BeiDou satellite navigation timing data are eliminated based on a pre-set double-difference observation equation.

15. The method according to claim 14, characterized in that, Also includes: The BeiDou satellite navigation timing data is preprocessed.

16. The method according to claim 15, characterized in that, The preprocessing operations for the BeiDou satellite navigation time series data include: Baseline processing is performed on the aforementioned BeiDou satellite navigation timing data; The BeiDou satellite navigation time series data is subjected to network adjustment processing.

17. The method according to claim 1, characterized in that, The operation of calculating the first and second time-series position information of the tower by using a Kalman filter model on the two BeiDou satellite navigation time-series data includes: The floating-point solutions for the first and second time-series position information are obtained by calculating the two BeiDou satellite navigation time-series data using the Kalman filter model. The ambiguity of the floating-point solutions of the first and second time-series position information is fixed to determine the first and second time-series position information.

18. A computer-readable storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, the method described in any one of claims 1 to 17 is performed by a processor.

19. A deformation detection device for geologically sensitive areas of power transmission channels, used to implement the method of claim 1, characterized in that, include: The first determining module is used to calculate the radar image set in the geologically sensitive area of ​​the power transmission channel using small baseline set interferometry technology, and determine the first deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the first deformation data information is the wide-area surface deformation information in the geologically sensitive area of ​​the power transmission channel; The second determining module is used to calculate the difference between the point cloud data of the preceding and following time phases to determine the second deformation data information of the geologically sensitive area of ​​the power transmission channel, wherein the second deformation data information is the strip-shaped deformation information of the power transmission channel within the geologically sensitive area of ​​the power transmission channel; The third determining module is used to determine the absolute deformation data information of the towers in the geologically sensitive area of ​​the power transmission channel using Beidou differential positioning technology, wherein the absolute deformation data information of the towers is the point deformation information of the power transmission towers in the geologically sensitive area of ​​the power transmission channel; The fourth determining module is used to determine the ground subsidence information of the geologically sensitive area of ​​the power transmission channel based on the first deformation data information, the second deformation data information, and the absolute deformation data information of the tower.

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