Power equipment landslide disaster monitoring method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a power equipment landslide disaster monitoring method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring historical optical satellite images and laser radar data of a to-be-monitored area where power equipment is located; determining a landslide hidden danger area in the to-be-monitored area according to the historical optical satellite image and the laser radar data; obtaining synthetic aperture radar data of the to-be-monitored area, and obtaining deformation information of the landslide hidden danger area according to the synthetic aperture radar data by using a synthetic aperture radar interferometry technology; and obtaining a landslide disaster monitoring result of the to-be-monitored area according to the deformation information. By adopting the method, the landslide disaster condition of the to-be-monitored area can be accurately monitored.

Description

Technical Field

[0001] The present application relates to the field of power safety technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for monitoring landslide disasters in power equipment. Background Art

[0002] With the development of smart grid and green grid technology, the demand for the construction of high-voltage and longer-distance transmission lines is increasing. More and more transmission lines must pass through mountainous areas prone to geological disasters, and the geological safety issues of transmission lines in mountainous areas are becoming increasingly prominent. Large-scale geological disaster risk source areas are often located at high altitudes, with difficult transportation and few people. They are highly concealed. It is usually difficult to conduct early identification and deformation monitoring of large-scale high-altitude geological disaster risks by traditional ground investigation and inspection methods. Summary of the invention

[0003] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for monitoring landslide disasters in power equipment in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for monitoring landslide disasters of power equipment, comprising:

[0005] Obtain historical optical satellite imagery and lidar data of the area to be monitored where the power equipment is located;

[0006] Determine a landslide potential area in the area to be monitored based on the historical optical satellite images and the laser radar data;

[0007] Acquire synthetic aperture radar data of the area to be monitored, and obtain deformation information of the landslide potential area according to the synthetic aperture radar data using synthetic aperture radar interferometry technology;

[0008] The landslide disaster monitoring result of the area to be monitored is obtained according to the deformation information.

[0009] In one embodiment, the synthetic aperture radar data includes synthetic aperture radar images at multiple monitoring time points within a monitoring period; the use of synthetic aperture radar interferometry technology to obtain the deformation information of the landslide hazard area based on the synthetic aperture radar data includes: using superposition synthetic aperture radar interferometry technology to obtain the average deformation information of the landslide hazard area within the monitoring period based on the synthetic aperture radar data; using small baseline set synthetic aperture radar interferometry technology to obtain the time series deformation information of the landslide hazard area within the monitoring period based on the synthetic aperture radar data; obtaining the deformation information based on the average deformation information and the time series deformation information.

[0010] In one of the embodiments, the stacked synthetic aperture radar interferometry measurement technology is used to obtain the average deformation information of the landslide potential area during the monitoring period based on the synthetic aperture radar data, including: obtaining multiple image pairs based on each of the synthetic aperture radar images; performing differential interference processing on each of the image pairs to obtain a differential interference map; performing phase unwrapping processing on each of the differential interference maps to obtain an unwrapped interference map; performing weighted averaging processing on each of the unwrapped interference maps based on a time baseline of each of the unwrapped interference maps to obtain an average deformation rate of the landslide potential area during the monitoring period; and obtaining the average deformation information based on the average deformation rate.

[0011] In one of the embodiments, the small baseline set synthetic aperture radar interferometry measurement technology is used to obtain the time-series deformation information of the landslide hazard area within the monitoring period according to the synthetic aperture radar data, including: obtaining multiple small baseline image pairs according to each of the synthetic aperture radar images; performing differential interference processing on each of the small baseline image pairs to obtain a differential interference map; performing phase unwrapping processing on the differential interference map to obtain an unwrapped interference map; obtaining the time-series cumulative deformation amount information of the landslide hazard area according to the time-series relationship between each of the unwrapped interference maps; obtaining the time-series deformation rate information of the landslide hazard area according to the time-series cumulative deformation amount information; obtaining the time-series deformation information according to the time-series cumulative deformation amount information and the time-series deformation rate information.

[0012] In one of the embodiments, after obtaining the time-series cumulative deformation information of the landslide hazard area according to the time-series relationship between each of the disentangled interference graphs, it also includes: selecting multiple sample points in the landslide hazard area; obtaining the cumulative deformation of each of the sample points at multiple statistical time points according to the time-series cumulative deformation information; statistically analyzing the changing trend of the cumulative deformation of each of the sample points over time; obtaining the landslide deformation stage information corresponding to the landslide hazard area according to the changing trend of each of the sample points; obtaining the deformation information according to the average deformation information and the time-series deformation information, including: obtaining the deformation information according to the average deformation information, the time-series deformation information, and the landslide deformation stage information.

[0013] In one of the embodiments, the selecting of multiple sample points in the landslide hazard area includes: selecting multiple first sample points on the slope profile line of the landslide hazard area; selecting multiple second sample points in a sub-sampling area of ​​the landslide hazard area that includes the equipment location of the power equipment; the sub-sampling area is smaller than the landslide hazard area; the obtaining of the landslide deformation stage information corresponding to the landslide hazard area according to the change trend of each of the sample points includes: obtaining the first landslide deformation stage information corresponding to the landslide hazard area according to the first change trend of each of the first sample points; obtaining the second landslide deformation stage information corresponding to the sub-sampling area according to the second change trend of each of the second sample points; obtaining the landslide deformation stage information according to the first landslide deformation stage information and the second landslide deformation stage information.

[0014] In a second aspect, the present application also provides a landslide disaster monitoring device for electric power equipment, comprising:

[0015] The first data acquisition module is used to acquire historical optical satellite images and laser radar data of the area to be monitored where the power equipment is located;

[0016] A hidden danger area identification module, used for determining a landslide hidden danger area in the area to be monitored based on the historical optical satellite image and the laser radar data;

[0017] A second data acquisition module is used to acquire synthetic aperture radar data of the area to be monitored, and obtain deformation information of the landslide potential area according to the synthetic aperture radar data using synthetic aperture radar interferometry technology;

[0018] The monitoring result acquisition module is used to obtain the landslide disaster monitoring result of the area to be monitored according to the deformation information.

[0019] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0020] Obtain historical optical satellite imagery and lidar data of the area to be monitored where the power equipment is located;

[0021] Determine a landslide potential area in the area to be monitored based on the historical optical satellite images and the laser radar data;

[0022] Acquire synthetic aperture radar data of the area to be monitored, and obtain deformation information of the landslide potential area according to the synthetic aperture radar data using synthetic aperture radar interferometry technology;

[0023] The landslide disaster monitoring result of the area to be monitored is obtained according to the deformation information.

[0024] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0025] Obtain historical optical satellite imagery and lidar data of the area to be monitored where the power equipment is located;

[0026] Determine a landslide potential area in the area to be monitored based on the historical optical satellite images and the laser radar data;

[0027] Acquire synthetic aperture radar data of the area to be monitored, and obtain deformation information of the landslide potential area according to the synthetic aperture radar data using synthetic aperture radar interferometry technology;

[0028] The landslide disaster monitoring result of the area to be monitored is obtained according to the deformation information.

[0029] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0030] Obtain historical optical satellite imagery and lidar data of the area to be monitored where the power equipment is located;

[0031] Determine a landslide potential area in the area to be monitored based on the historical optical satellite images and the laser radar data;

[0032] Acquire synthetic aperture radar data of the area to be monitored, and obtain deformation information of the landslide potential area according to the synthetic aperture radar data using synthetic aperture radar interferometry technology;

[0033] The landslide disaster monitoring result of the area to be monitored is obtained according to the deformation information.

[0034] The above-mentioned method, device, computer device, computer-readable storage medium and computer program product for monitoring landslide disasters of power equipment first obtain historical optical satellite images and laser radar data of the area to be monitored where the power equipment is located, then determine the landslide hazard area in the area to be monitored based on the historical optical satellite images and laser radar data, then obtain synthetic aperture radar data of the area to be monitored, and use synthetic aperture radar interferometry technology to obtain deformation information of the landslide hazard area based on the synthetic aperture radar data, and then obtain the landslide disaster monitoring results of the area to be monitored based on the deformation information. This scheme, by first using optical satellite images and laser radar data to detect the landslide situation in the area to be monitored, can timely discover possible landslide hazards in the area to be monitored and accurately divide the landslide hazard area. By obtaining synthetic aperture radar data of the area to be monitored and processing it using synthetic aperture radar interferometry technology, high-precision deformation information of the landslide hazard area over time can be obtained, so that accurate monitoring of the landslide disaster situation in the area where the power equipment is located can be achieved, which is conducive to ensuring the safety of the power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A schematic diagram of a flow chart of a method for monitoring landslide disasters of power equipment in one embodiment;

[0037] Figure 2 This is a schematic diagram of laser radar data of an area to be monitored in one embodiment;

[0038] Figure 3 is a schematic diagram of an optical satellite image of an area to be monitored in one embodiment;

[0039] Figure 4 A schematic diagram of a process for obtaining average deformation information of a landslide potential area within a monitoring period in one embodiment;

[0040] Figure 5 is a graph of average deformation rate of a landslide potential area in one embodiment;

[0041] Figure 6 A schematic diagram of a process for obtaining time-series deformation information of a landslide potential area within a monitoring period in one embodiment;

[0042] Figure 7is a deformation rate diagram of the line of sight direction of a landslide potential area in one embodiment;

[0043] Figure 8 is a diagram of average deformation rate of a section corresponding to a slope section line 1-1' in one embodiment;

[0044] Fig. 9 A schematic diagram of a process for obtaining landslide deformation stage information in one embodiment;

[0045] Fig.10 is a schematic diagram of the position of the second sample point in one embodiment;

[0046] Fig.11 is a schematic diagram of a cumulative deformation curve of a first sample point in an embodiment;

[0047] Fig.12 is a schematic diagram of a cumulative deformation curve of a second sample point in an embodiment;

[0048] Fig.13 A structural block diagram of a landslide disaster monitoring device for power equipment in one embodiment;

[0049] Fig.14 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] In one embodiment, Figure 1 As shown, a method for monitoring landslide disasters of power equipment is provided. This embodiment takes the method applied to a server as an example. It can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0052] Step S101, obtaining historical optical satellite images and lidar data of the area to be monitored where the power equipment is located.

[0053] Specifically, the power equipment may be equipment such as a transmission tower installed on a slope, and the area to be monitored may be an area including the location of the power equipment and the slope on which it is located. For the area to be monitored, the corresponding historical optical satellite images and lidar data may be obtained respectively. Exemplarily, the historical optical satellite images and lidar data of the area to be monitored may be pre-collected and stored in a specific storage location, and in this step, the server may obtain the historical optical satellite images and lidar (Light Detection and Ranging, LiDAR) data from the specific storage location.

[0054] The historical optical satellite images corresponding to the area to be monitored may include optical satellite images at multiple historical time points, which may be acquired using optical remote sensing technology. The laser radar data corresponding to the area to be monitored may be acquired using an airborne laser radar.

[0055] Step S102, determining landslide potential areas in the area to be monitored based on historical optical satellite images and lidar data.

[0056] Among them, the lidar data collected by the airborne lidar can eliminate the influence of vegetation and obtain real ground information. Therefore, based on the lidar data, it is possible to identify ancient landslides that may exist in mountainous areas covered with vegetation, or hidden dangers that have been active but have not slid as a whole, or cracked mountains formed by strong effects such as earthquakes, which are prone to landslides.

[0057] Among them, since surface deformation will cause changes in spectral characteristics, the color changes of optical remote sensing can be used to effectively identify surface deformation, thereby delineating potential landslide hazards. In this step, historical optical satellite images can be used to interpret the geological deformation of the monitored area based on the differences between optical satellite images at multiple historical time points, thereby identifying landslide hazards.

[0058] Based on this, combined with the analysis results of historical optical satellite images and lidar data, it is possible to determine whether there is a landslide hazard in the area to be monitored, and if it is determined that there is a landslide hazard, determine the boundary of the landslide hazard area.

[0059] For example, taking the power equipment as tower A, the laser radar data of the area to be monitored where it is located can be as follows: Figure 2 As shown in Figure 2, the optical satellite image of the area to be monitored can be Figure 3As shown. Among them, both the LiDAR data and the optical satellite images show that there is a landslide structure on the slope where the tower A is located. The landslide is pear-shaped as a whole, with the bedrock outcrop and the groove as the boundary on the left and right sides respectively. The boundaries are obvious, and the slope is narrow at the top and wide at the bottom. The front edge accumulation is loose and bulging, squeezing the river channel. It can be inferred that it is the residual accumulation of the underlying bedrock covered by the landslide in the early stage of the slope. Based on this, combined with the LiDAR data and optical satellite images, it can be determined that the slope where the tower A is located is a large ancient landslide accumulation body, and the following is obtained. Figure 3 The landslide boundary is shown. According to the landslide boundary, the area within the landslide boundary can be used as the landslide hazard area of ​​the area to be monitored where the tower A is located.

[0060] Step S103, obtaining synthetic aperture radar data of the area to be monitored, and using synthetic aperture radar interferometry technology to obtain deformation information of the landslide potential area based on the synthetic aperture radar data.

[0061] Among them, the Synthetic Aperture Radar (SAR) technology can use the synthetic aperture principle to achieve high-resolution microwave imaging of the surface. In this step, according to the preset monitoring period, synthetic aperture radar images collected at multiple monitoring time points can be obtained. Exemplarily, these synthetic aperture radar images can include ascending orbit data and descending orbit data.

[0062] Among them, Interferometric synthetic aperture radar (InSAR) technology is a quantitative microwave remote sensing technology developed on the basis of the fusion of synthetic aperture radar remote sensing imaging and electromagnetic wave interference. It can use the phase data in two or more synthetic aperture radar images covering the same area for interference processing and analysis to achieve three-dimensional reconstruction of the surface and detection of surface changes.

[0063] In this step, by acquiring synthetic aperture radar data of the area to be monitored and analyzing the synthetic aperture radar data using synthetic aperture radar interferometry technology, deformation information of the landslide hazard area in the area to be monitored during the monitoring period can be obtained. Exemplarily, the deformation information may include cumulative deformation amount, deformation rate at multiple time points, average deformation rate, etc., and may also include information such as landslide deformation stage derived from data such as deformation amount and deformation rate.

[0064] Step S104: obtaining the landslide hazard monitoring result of the area to be monitored according to the deformation information.

[0065] Among them, according to the deformation information of the landslide hazard area during the monitoring period, the development of the landslide in the landslide hazard area and the current deformation stage can be determined, thereby monitoring the landslide disaster situation in the area where the power equipment is located and obtaining the landslide disaster monitoring result. Exemplarily, the landslide disaster monitoring result may include part or all of the deformation information of the landslide hazard area, and may also include the prediction results of future landslide conditions obtained based on the deformation information, or the evaluation results of the damage risk faced by the power equipment, etc.

[0066] In the above-mentioned method for monitoring landslide disasters of power equipment, by first using optical satellite images and laser radar data to detect the landslide situation in the monitored area, it is possible to timely discover the potential landslide hazards in the monitored area and accurately divide the landslide hazard area. By acquiring synthetic aperture radar data of the monitored area and processing it using synthetic aperture radar interferometry technology, it is possible to obtain high-precision deformation information of the landslide hazard area over time, thereby realizing accurate monitoring of the landslide disaster situation in the area where the power equipment is located, which is conducive to ensuring the safety of the power equipment.

[0067] In an exemplary embodiment, the synthetic aperture radar data may include synthetic aperture radar images of multiple monitoring time points within a monitoring period, and using synthetic aperture radar interferometry measurement technology to obtain deformation information of the landslide hazard area based on the synthetic aperture radar data may include: using superposition synthetic aperture radar interferometry measurement technology to obtain average deformation information of the landslide hazard area within the monitoring period based on the synthetic aperture radar data; using small baseline set synthetic aperture radar interferometry measurement technology to obtain time series deformation information of the landslide hazard area within the monitoring period based on the synthetic aperture radar data; obtaining deformation information based on the average deformation information and the time series deformation information.

[0068] The synthetic aperture radar data of the area to be monitored may include synthetic aperture radar images at multiple monitoring time points within a preset monitoring period.

[0069] In this embodiment, based on synthetic aperture radar data, the stacking synthetic aperture radar interferometry (Stacking-InSAR) technology can be used to perform phase stacking processing on the unwrapped phases extracted from multiple synthetic aperture radar images to estimate the average deformation rate of the landslide hazard area during the monitoring period, so that the average deformation information of the landslide hazard area during the monitoring period can be obtained according to the average deformation rate. Exemplarily, the average deformation rate estimation processing can be performed by weighted stacking.

[0070] At the same time, in this embodiment, the small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) technology can also be used to calculate the unwrapped phase using a shorter time and space baseline in multiple synthetic aperture radar images, and the time series relationship of multiple unwrapped phases is combined to calculate the cumulative deformation and deformation rate of the landslide potential area at multiple time points during the monitoring period, thereby obtaining the time series deformation information of the landslide potential area.

[0071] Among them, according to the average deformation information and time series deformation information of the landslide potential area, deformation information that can reflect the overall deformation situation and deformation development situation of the landslide potential area during the monitoring period can be obtained.

[0072] In this embodiment, by analyzing the synthetic aperture radar data using the superposition synthetic aperture radar interferometry measurement technology, a large-scale and long-term deformation monitoring of the landslide potential area can be carried out; and by analyzing the synthetic aperture radar data using the small baseline set synthetic aperture radar interferometry measurement technology, the landslide potential area can be finely monitored for deformation. Combining the results of the two measurement methods, a comprehensive result that can reflect the long-term deformation trend and short-term deformation situation of the landslide potential area can be obtained, thereby improving the quality and interpretability of the deformation information, which is conducive to obtaining more accurate landslide disaster monitoring results in the future.

[0073] In an exemplary embodiment, Figure 4 As shown in the figure, using the superposition synthetic aperture radar interferometry technology, the average deformation information of the landslide potential area during the monitoring period is obtained according to the synthetic aperture radar data, which may include:

[0074] Step S401, obtaining a plurality of image pairs according to each synthetic aperture radar image.

[0075] According to the monitoring time points and acquisition positions corresponding to each synthetic aperture radar image in the synthetic aperture radar data, a temporal and spatial baseline distribution map can be generated. According to a preset time interval threshold, every two synthetic aperture radar images whose time interval between corresponding monitoring time points is less than the time interval threshold can be selected as an image pair, thereby obtaining multiple image pairs.

[0076] Optionally, before selecting an image pair from the synthetic aperture radar images, each synthetic aperture radar image may be preprocessed. Among them, one synthetic aperture radar image may be selected as a main image from multiple synthetic aperture radar images, and the other images may be uniformly registered to the main image radar coordinate system. Optionally, precise orbit data may be added to assist registration during the image registration process.

[0077] Step S402: Perform differential interference processing on each image pair to obtain a differential interference pattern.

[0078] Among them, for each image pair selected previously, the two synthetic aperture radar images contained therein can be subjected to differential interference processing according to the sequence of corresponding monitoring time points to obtain a corresponding differential interference map.

[0079] For example, in the differential interferometry processing, the image pair can be pre-filtered, and the interferometric phase map can be generated by pixel-by-pixel conjugate multiplication. At the same time, the flat ground phase can be calculated using the spatial baseline parameters and the earth ellipsoid parameters, and the terrain phase can be calculated using the digital elevation model (DEM) registered with the main image. Then, the flat ground phase and terrain phase can be removed from the interferometric phase map to obtain the differential interferogram.

[0080] Step S403: performing phase unwrapping processing on each differential interference pattern to obtain an unwrapped interference pattern.

[0081] Among them, for the multiple differential interference patterns obtained in the above process, each differential interference pattern can be subjected to phase unwrapping processing to obtain a corresponding unwrapped interference pattern.

[0082] Exemplarily, in this step, the differential interferogram may be first filtered for noise reduction, and then phase unwrapping may be performed on it. Optionally, a two-dimensional phase unwrapping method such as a branch cutting method or a minimum cost flow method may be used to phase unwrap the differential interferogram. Optionally, before phase unwrapping, the coherence coefficient of the image pair may be calculated pixel by pixel and a coherence coefficient map may be generated, and then the coherence coefficient map may be applied to the phase unwrapping of the differential interferogram to improve the accuracy of phase unwrapping.

[0083] Step S404: performing weighted average processing on each disentangled interference graph according to the time baseline of each disentangled interference graph, and obtaining the average deformation rate of the landslide potential area during the monitoring period.

[0084] Among them, the time baseline of each unwrapped interferogram can be the time interval between the monitoring time points of the two synthetic aperture radar images in its corresponding image pair, and the unwrapped interferogram corresponding to each image pair can reflect the surface deformation variable of the landslide potential area between the corresponding two monitoring time points.

[0085] In this step, each disentangled interference graph may be subjected to weighted average processing according to its time baseline to obtain an average deformation rate of the landslide hazard area. Exemplarily, in the weighted average processing, a disentangled interference graph with a longer time baseline may correspond to a larger weight.

[0086] Step S405, obtaining average deformation information according to the average deformation rate.

[0087] Among them, according to the average deformation rate of the landslide potential area, the average deformation information of the landslide potential area can be obtained.

[0088] Optionally, the average deformation information of the landslide hazard area may include an average deformation rate map of the landslide hazard area. Exemplarily, an initial deformation rate map of the landslide hazard area can be generated according to the average deformation rate of the landslide hazard area. Then, the initial deformation rate map can be converted to the geographic coordinate system using a conversion lookup table between the digital elevation model coordinate system and the image coordinate system. Further, by comprehensively utilizing information such as the digital elevation model, the coherence coefficient map, and the overlapping shadow map, invalid observation information such as water areas, shadows, and overlapping can be eliminated from the initial deformation rate map. Then, according to the average deformation rate corresponding to each pixel in the processed initial deformation rate map, the average deformation rate map of the landslide hazard area is displayed according to different numerical graded colors, thereby obtaining the average deformation rate map of the landslide hazard area.

[0089] For example, taking the area to be monitored where tower A is located as an example, the average deformation rate diagram of the landslide hazard area can be as follows: Figure 5 As shown. Among them, Figure 5 Part (a) is the average deformation rate diagram obtained using synthetic aperture radar down-orbit data. Figure 5 Part (b) shows the average deformation rate obtained using synthetic aperture radar orbit-raising data. Figure 5 It can be seen that the landslide potential area in the area to be monitored where the tower A is located is in a continuous deformation state during the monitoring period.

[0090] In this embodiment, by using the superposition synthetic aperture radar interferometry measurement technology to measure the average deformation rate of the landslide potential area during the monitoring period, and performing weighted superposition of the disentangled interference pattern according to the time baseline, the long-term deformation trend of the landslide potential area can be better monitored and more accurate monitoring results can be obtained.

[0091] In an exemplary embodiment, Figure 6 As shown in the figure, using the small baseline set synthetic aperture radar interferometry technology, the time series deformation information of the landslide potential area during the monitoring period is obtained according to the synthetic aperture radar data, which may include:

[0092] Step S601: obtaining a plurality of small baseline image pairs according to each synthetic aperture radar image.

[0093] Wherein, according to the monitoring time points and acquisition positions corresponding to each synthetic aperture radar image in the synthetic aperture radar data, a time and space baseline distribution map can be generated. Wherein, according to the pre-set time interval threshold and space interval threshold, two synthetic aperture radar images whose time interval between corresponding monitoring time points is less than the time interval threshold and whose position interval between corresponding acquisition positions is less than the space interval threshold can be selected as a small baseline image pair, thereby obtaining a plurality of small baseline image pairs. Wherein, before selecting the small baseline image pair, each synthetic aperture radar image can also be preprocessed. The specific preprocessing method is similar to the preprocessing method in the aforementioned embodiment, and will not be repeated here.

[0094] For example, Figure 5 As shown, for the area to be monitored where the tower A is located, the orbit-dropping data can obtain better detection results, so the orbit-dropping data can be used in this step to perform small baseline synthetic aperture radar interferometry.

[0095] Step S602: Perform differential interference processing on each small baseline image pair to obtain a differential interference pattern.

[0096] Among them, for each small baseline image pair selected previously, the two synthetic aperture radar images contained therein can be subjected to differential interference processing according to the sequence of corresponding monitoring time points to obtain a corresponding differential interference map.

[0097] Optionally, during the differential interferometry processing, the synthetic aperture radar image in the small baseline image pair can be pre-filtered, and the interferometric phase map can be generated by pixel-by-pixel conjugate multiplication. Then, the coherence coefficient of the small baseline image pair can be calculated, and the coherent point target can be extracted based on the coherence coefficient. Then, the flat ground phase can be calculated using the spatial baseline parameters and the earth ellipsoid parameters, and the terrain phase can be calculated using the digital elevation model (DEM) registered with the main image, and the flat ground phase and terrain phase can be removed from the interferometric phase map to obtain a differential interferogram.

[0098] Step S603: performing phase unwrapping processing on each differential interference pattern to obtain an unwrapped interference pattern.

[0099] In this step, each differential interferogram obtained previously can be filtered and denoised, and then phase unwrapped. Optionally, a two-dimensional phase unwrapping method such as a branch cutting method or a minimum cost flow method can be used to phase unwrap the differential interferogram to obtain a corresponding unwrapped interferogram.

[0100] Step S604: obtaining the time-series cumulative deformation information of the landslide potential area according to the time-series relationship between the disentangled interference graphs.

[0101] Based on the above process, a plurality of disentangled interference graphs are obtained, and linear deformation and nonlinear deformation calculation can be performed, and time-series cumulative deformation amount information can be obtained according to the calculated linear deformation components and nonlinear deformation components.

[0102] For example, in the calculation of linear deformation, the previously selected coherent points can be used to estimate the deformation parameters between adjacent coherent points, and the residual elevation error in the disentangled interference map can be calculated and removed from the disentangled interference map. Then, based on the surface deformation corresponding to each disentangled interference map and the time series relationship between each disentangled interference map, it can be assumed that the surface deformation changes linearly, and the relationship between the deformation and time can be linearly fitted to obtain the cumulative linear deformation time series of the landslide potential area during the monitoring period.

[0103] Exemplarily, in the calculation of nonlinear deformation, the residual phase after deducting the linear deformation from the unwrapped interference pattern can be low-pass filtered to remove high-frequency noise. Then, the residual phase can be globally analyzed using the singular value decomposition (SVD) method to extract the components containing the nonlinear deformation variables. Then, by performing atmospheric correction on the singular value decomposition results, the influence of the atmospheric phase can be eliminated. Then, based on the time series of the corrected residual phase, the cumulative nonlinear deformation time series of the landslide hazard area during the monitoring period can be further calculated.

[0104] Then, according to the cumulative linear deformation time series and the cumulative nonlinear deformation time series, the cumulative deformation time series can be obtained, thereby obtaining the time series cumulative deformation information of the landslide potential area.

[0105] Step S605, obtaining the time-series deformation rate information of the landslide potential area according to the time-series accumulated deformation amount information.

[0106] Among them, according to the time series cumulative deformation information of the landslide potential area, the deformation rate time series of the landslide potential area in the monitoring period can be further calculated, so as to obtain the time series deformation rate information of the landslide potential area.

[0107] Step S606, obtaining the time series deformation information according to the time series accumulated deformation amount information and the time series deformation rate information.

[0108] Among them, the time-series deformation information of the landslide hazard area can be obtained according to the time-series cumulative deformation amount information and the time-series deformation rate information of the landslide hazard area. Optionally, the time-series deformation information may include the time-series cumulative deformation amount information and the time-series deformation rate information, and may also include derivative information obtained by processing these information, for example, it may include a line-of-sight (LOS) direction deformation rate diagram, a profile deformation rate diagram, etc. of the landslide hazard area.

[0109] For example, taking the monitored area where tower A is located as an example, the line of sight deformation rate diagram of the landslide hazard area can be as follows: Figure 7 As shown in , it is the result obtained by processing the down-orbit data of synthetic aperture radar using small baseline set synthetic aperture radar interferometry technology. A negative deformation rate value indicates that the deformation point moves away from the satellite, and a positive deformation rate value indicates that the deformation point moves toward the satellite. Optionally, as Figure 7 As shown in FIG. 1 , according to the deformation rate value of each deformation point in the landslide hazard area, the landslide hazard area can be further divided into a strong deformation area.

[0110] For example, based on Figure 7 The deformation rate diagram in the line of sight direction shown in the figure can be used to determine the slope profile line 1-1' of the landslide body, and then the following can be obtained: Figure 8 The average deformation rate diagram of the profile corresponding to the slope profile line 1-1' is shown in FIG. Figure 8 It can be seen that the deformation rate of the landslide increases from the rear edge downward, and shows signs of relaxation and a small degree of reversal in local areas of the slope, and then suddenly increases within the strong deformation area.

[0111] In this embodiment, by using the small baseline set of synthetic aperture radar interferometry technology, the time series cumulative deformation of the landslide hazard area is measured, and the influence of geometric decorrelation and DEM error on the deformation measurement results can be reduced by using a shorter spatial baseline, and the singular value decomposition method is used when solving the deformation rate to connect the data sets of the larger spatial baseline to increase the time sampling frequency of the deformation measurement and provide a high spatial density deformation measurement, so as to achieve a high-precision analysis of the deformation situation in the landslide hazard area. Moreover, in this embodiment, by obtaining the time series cumulative deformation and time series deformation rate of the landslide hazard area, the landslide development process can be analyzed more finely and more valuable information can be obtained.

[0112] In an exemplary embodiment, after obtaining the time-series cumulative deformation information of the landslide hazard area according to the time-series relationship between each disentangled interference graph, it can also include: selecting multiple sample points in the landslide hazard area; obtaining the cumulative deformation of each sample point at multiple statistical time points according to the time-series cumulative deformation information; statistically analyzing the changing trend of the cumulative deformation of each sample point over time; obtaining the landslide deformation stage information corresponding to the landslide hazard area according to the changing trend of each sample point; obtaining the deformation information according to the average deformation information and the time-series deformation information, which can include: obtaining the deformation information according to the average deformation information, the time-series deformation information, and the landslide deformation stage information.

[0113] Specifically, after obtaining the time series cumulative deformation information of the landslide potential area, multiple sample points can be selected in the landslide potential area, and the landslide deformation stage of the landslide potential area can be analyzed according to the cumulative deformation change trend of each sample point.

[0114] Among them, typical deformation points in the landslide hazard area can be selected as sample points. Then, the cumulative deformation time series of each sample point can be obtained according to the time series cumulative deformation information of the landslide hazard area. Then, the variation trend of the cumulative deformation of each sample point over time can be analyzed to obtain the landslide deformation stage information corresponding to the landslide hazard area. Exemplarily, the historical deformation curve of the sample point can be drawn to intuitively present the variation trend of the cumulative deformation over time, so as to determine the landslide deformation stage corresponding to the landslide hazard area. Exemplarily, the landslide deformation stage can include creep deformation stage, uniform deformation stage, accelerated deformation stage, etc.

[0115] Among them, the landslide deformation stage information obtained according to the analysis can be combined with the average deformation information and the time series deformation information to obtain deformation information that can comprehensively reflect the landslide deformation state of the landslide hazard area.

[0116] In this embodiment, by further analyzing the cumulative deformation trends of multiple sample points in the landslide hazard area based on the time-series cumulative deformation information, the landslide deformation stage in the landslide hazard area can be identified, which is conducive to evaluating possible disaster damage and thus ensuring the safety of power equipment.

[0117] In an exemplary embodiment, Fig. 9 As shown, multiple sample points are selected in the landslide hazard area, which may include:

[0118] Step S901: Select a plurality of first sample points on a slope profile line in a landslide hazard area.

[0119] Step S902: selecting a plurality of second sample points in a sub-sampling area including the equipment location of the power equipment in the landslide hazard area.

[0120] In order to further analyze the deformation characteristics of the landslide hazard area in the monitored area where the power equipment is located, different sample points can be selected in the landslide hazard area for deformation analysis. In step S901, multiple first sample points can be selected from the slope profile line of the landslide hazard area. For example, Figure 7 and Figure 8 As shown, four first sample points P1, P2, P3, and P4 can be selected on the slope profile line 1-1'.

[0121] In step S902, a plurality of second sample points may be selected in a sub-sampling area of ​​the landslide hazard area. The sub-sampling area may be a smaller area divided in the landslide hazard area and including the equipment location of the power equipment. For example, Fig.10 As shown, four second sample points P5, P6, P7, and P8 can be selected near the equipment position of the tower A.

[0122] Optionally, the first sample point and the second sample point selected in this embodiment may be typical deformation points in the landslide hazard area.

[0123] Among them, according to the change trend of each sample point, the landslide deformation stage information corresponding to the landslide potential area is obtained, which may include:

[0124] Step S903: obtaining first landslide deformation stage information corresponding to the landslide hazard area according to the first change trend of each first sample point.

[0125] Among them, according to the time series cumulative deformation information of the landslide hazard area, the cumulative deformation time series of each first sample point can be extracted. Then, according to the cumulative deformation time series of each first sample point, the following can be drawn: Fig.11 The cumulative deformation curve is shown in Figure 2. Fig.11 As shown in the figure, the cumulative deformation of points P1, P2, P3, and P4 increases in sequence. From the cumulative deformation curves of the four sampling points, it can be seen that the landslide hazard area is basically in the uniform deformation stage during the monitoring period. Therefore, the first landslide deformation stage information of the landslide hazard area can be obtained.

[0126] Step S904: obtaining the second landslide deformation stage information corresponding to the sub-sampling area according to the second change trend of each second sample point.

[0127] Among them, according to the time series cumulative deformation information of the landslide hazard area, the cumulative deformation time series of each second sample point can be extracted. Then, according to the cumulative deformation time series of each second sample point, the following can be drawn: Fig.12 The cumulative deformation curve is shown in Figure 2. Fig.12As shown in the figure, the cumulative deformation of point P7 among the four second sampling points is the largest. From the cumulative deformation curves of the four second sampling points, it can be seen that the sub-sampling area is basically in the creep deformation stage during the monitoring period. Therefore, the second landslide deformation stage information of the sub-sampling area where the power equipment is located can be obtained.

[0128] Step S905, obtaining the landslide deformation stage information according to the first landslide deformation stage information and the second landslide deformation stage information.

[0129] Among them, according to the first landslide deformation stage information and the second landslide deformation stage information, the landslide deformation stage information that can comprehensively reflect the overall deformation stage of the landslide potential area and the landslide deformation stage information that can reflect the deformation stage of the small area where the power equipment is located can be obtained.

[0130] In this embodiment, by selecting multiple first sample points on the slope profile line and multiple second sample points in the sub-sampling area, the overall deformation stage of the landslide hazard area and the landslide deformation stage information that can reflect the deformation stage of the small area where the power equipment is located can be obtained according to the deformation change trend of the first sample points and the second sample points, which is conducive to a more accurate assessment of the safety risks that the power equipment may face in the future.

[0131] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0132] Based on the same inventive concept, the embodiment of the present application also provides a power equipment landslide disaster monitoring device for implementing the power equipment landslide disaster monitoring method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more power equipment landslide disaster monitoring device embodiments provided below can refer to the limitations of the power equipment landslide disaster monitoring method above, and will not be repeated here.

[0133] In an exemplary embodiment, Fig.13 As shown, a landslide disaster monitoring device 1300 for electric power equipment is provided, comprising:

[0134] The first data acquisition module 1301 is used to acquire historical optical satellite images and laser radar data of the area to be monitored where the power equipment is located.

[0135] The hazard area identification module 1302 is used to determine the landslide hazard area in the area to be monitored based on historical optical satellite images and lidar data.

[0136] The second data acquisition module 1303 is used to acquire synthetic aperture radar data of the area to be monitored, and obtain deformation information of the landslide potential area according to the synthetic aperture radar data using synthetic aperture radar interferometry technology.

[0137] The monitoring result acquisition module 1304 is used to obtain the landslide disaster monitoring result of the area to be monitored according to the deformation information.

[0138] In an exemplary embodiment, the synthetic aperture radar data includes synthetic aperture radar images at multiple monitoring time points within a monitoring period; the second data acquisition module 1303 is used to: use the superposition synthetic aperture radar interferometry measurement technology to obtain the average deformation information of the landslide hazard area within the monitoring period based on the synthetic aperture radar data; use the small baseline set synthetic aperture radar interferometry measurement technology to obtain the time series deformation information of the landslide hazard area within the monitoring period based on the synthetic aperture radar data; obtain deformation information based on the average deformation information and the time series deformation information.

[0139] In an exemplary embodiment, the second data acquisition module 1303 is used to: obtain multiple image pairs based on each synthetic aperture radar image; perform differential interference processing on each image pair to obtain a differential interference map; perform phase unwrapping processing on each differential interference map to obtain an unwrapped interference map; perform weighted averaging processing on each unwrapped interference map based on the time baseline of each unwrapped interference map to obtain the average deformation rate of the landslide hazard area during the monitoring period; and obtain average deformation information based on the average deformation rate.

[0140] In an exemplary embodiment, the second data acquisition module 1303 is used to: obtain multiple small baseline image pairs based on each synthetic aperture radar image; perform differential interference processing on each small baseline image pair to obtain a differential interference map; perform phase unwrapping processing on the differential interference map to obtain an unwrapped interference map; obtain the time-series cumulative deformation information of the landslide hazard area based on the time-series cumulative deformation information; obtain the time-series deformation rate information of the landslide hazard area based on the time-series cumulative deformation information; obtain the time-series deformation information based on the time-series cumulative deformation information and the time-series deformation rate information.

[0141] In an exemplary embodiment, the second data acquisition module 1303 is used to: select multiple sample points in the landslide hazard area; obtain the cumulative deformation of each sample point at multiple statistical time points based on the time series cumulative deformation information; statistically analyze the changing trend of the cumulative deformation of each sample point over time; obtain the landslide deformation stage information corresponding to the landslide hazard area based on the changing trend of each sample point; obtain the deformation information based on the average deformation information, the time series deformation information, and the landslide deformation stage information.

[0142] In an exemplary embodiment, the second data acquisition module 1303 is used to: select multiple first sample points on the slope profile line of the landslide hazard area; select multiple second sample points in a sub-sampling area that includes the equipment location of the power equipment in the landslide hazard area; the sub-sampling area is smaller than the landslide hazard area; according to the first change trend of each first sample point, obtain the first landslide deformation stage information corresponding to the landslide hazard area; according to the second change trend of each second sample point, obtain the second landslide deformation stage information corresponding to the sub-sampling area; according to the first landslide deformation stage information and the second landslide deformation stage information, obtain the landslide deformation stage information.

[0143] Each module in the above-mentioned landslide disaster monitoring device for electric power equipment can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0144] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.14 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store historical optical satellite images, lidar data, synthetic aperture radar data and other data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for monitoring landslide disasters of power equipment is implemented.

[0145] Those skilled in the art will understand that Fig.14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0148] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0151] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0152] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for monitoring landslide disasters of power equipment, characterized in that: The method comprises: Obtain historical optical satellite imagery and lidar data of the area to be monitored where the power equipment is located; Determine a landslide potential area in the area to be monitored based on the historical optical satellite images and the laser radar data; Acquire synthetic aperture radar data of the area to be monitored, and obtain deformation information of the landslide potential area according to the synthetic aperture radar data using synthetic aperture radar interferometry technology; The landslide disaster monitoring result of the area to be monitored is obtained according to the deformation information.

2. The method according to claim 1, characterized in that The synthetic aperture radar data includes synthetic aperture radar images at multiple monitoring time points within a monitoring period; The method of obtaining deformation information of the landslide hazard area according to the synthetic aperture radar data using synthetic aperture radar interferometry technology includes: Using the superposition synthetic aperture radar interferometry technology, the average deformation information of the landslide potential area within the monitoring period is obtained according to the synthetic aperture radar data; Using a small baseline set synthetic aperture radar interferometry technique, the temporal deformation information of the landslide potential area within the monitoring period is obtained according to the synthetic aperture radar data; The deformation information is obtained according to the average deformation information and the time series deformation information.

3. The method according to claim 2, characterized in that The method of using the superposition synthetic aperture radar interferometry technology to obtain the average deformation information of the landslide potential area during the monitoring period according to the synthetic aperture radar data includes: According to each of the synthetic aperture radar images, a plurality of image pairs are obtained; Performing differential interference processing on each of the image pairs to obtain a differential interference pattern; Performing phase unwrapping processing on each of the differential interference patterns to obtain an unwrapped interference pattern; According to the time baseline of each disentangled interference graph, weighted average processing is performed on each disentangled interference graph to obtain the average deformation rate of the landslide potential area during the monitoring period; The average deformation information is obtained according to the average deformation rate.

4. The method according to claim 2, characterized in that: The method of using the small baseline set synthetic aperture radar interferometry technology to obtain the time series deformation information of the landslide potential area within the monitoring period according to the synthetic aperture radar data includes: According to each of the synthetic aperture radar images, a plurality of small baseline image pairs are obtained; Performing differential interference processing on each of the small baseline image pairs to obtain a differential interference pattern; Performing phase unwrapping processing on the differential interference pattern to obtain an unwrapped interference pattern; According to the time series relationship between the disentangled interference graphs, the time series cumulative deformation information of the landslide potential area is obtained; According to the time-series accumulated deformation amount information, obtaining the time-series deformation rate information of the landslide potential area; The time series deformation information is obtained according to the time series accumulated deformation amount information and the time series deformation rate information.

5. The method according to claim 4, characterized in that After obtaining the time-series cumulative deformation information of the landslide potential area according to the time-series relationship between the disentangled interference graphs, the method further includes: Selecting a plurality of sample points in the landslide hazard area; According to the time series cumulative deformation amount information, the cumulative deformation amount of each sample point at multiple statistical time points is obtained; Counting the variation trend of the accumulated deformation amount of each sample point over time; According to the change trend of each of the sample points, the landslide deformation stage information corresponding to the landslide potential area is obtained; The obtaining the deformation information according to the average deformation information and the time series deformation information includes: The deformation information is obtained according to the average deformation information, the time series deformation information, and the landslide deformation stage information.

6. The method according to claim 5, characterized in that The selecting of a plurality of sample points in the landslide hazard area comprises: Selecting a plurality of first sample points on the slope profile line of the landslide hazard area; Selecting a plurality of second sample points in a sub-sampling area including the equipment location of the power equipment in the landslide hazard area; the sub-sampling area is smaller than the landslide hazard area; The obtaining of the landslide deformation stage information corresponding to the landslide hazard area according to the change trend of each of the sample points includes: According to the first change trend of each of the first sample points, first landslide deformation stage information corresponding to the landslide potential area is obtained; According to the second change trend of each of the second sample points, obtaining the second landslide deformation stage information corresponding to the sub-sampling area; The landslide deformation stage information is obtained according to the first landslide deformation stage information and the second landslide deformation stage information.

7. A landslide disaster monitoring device for power equipment, characterized in that: The device comprises: The first data acquisition module is used to acquire historical optical satellite images and laser radar data of the area to be monitored where the power equipment is located; A hidden danger area identification module, used for determining a landslide hidden danger area in the area to be monitored based on the historical optical satellite image and the laser radar data; A second data acquisition module is used to acquire synthetic aperture radar data of the area to be monitored, and obtain deformation information of the landslide potential area according to the synthetic aperture radar data using synthetic aperture radar interferometry technology; The monitoring result acquisition module is used to obtain the landslide disaster monitoring result of the area to be monitored according to the deformation information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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