A method and device for determining deformation of a dump of an open-pit mine based on time-series InSAR technology

Abstract

The application provides a method and device for determining the deformation of an open-pit mine dump based on time-series InSAR technology, which aims to effectively fuse continuous multi-period observations, eliminate the interference of surface elevation on SAR interference phase, and improve the determination accuracy of the deformation of the open-pit mine dump. The method comprises the following steps: obtaining SAR satellite images and a digital surface model of a target area in a preset period; constructing a plurality of multi-master image differential interference queues based on the SAR satellite images; calculating differential interference phases based on the plurality of multi-master image differential interference queues and the corresponding digital surface model, and obtaining unwrapped phases after atmospheric filtering processing by combining a singular value decomposition method; and calculating the deformation of the target area based on a sequential adjustment method, wherein after constructing an error estimation matrix containing elevation errors and unwrapped phases, the sequential adjustment method is used to obtain a velocity matrix corresponding to the time-series phase by using the error estimation matrix, and the deformation of the target area is calculated based on the velocity matrix.

Description

Technical Field

[0001] This application relates to the field of remote sensing image technology, and in particular to a method and apparatus for determining the shape variables of open-pit mine spoil heaps based on temporal InSAR technology. Background Technology

[0002] Open-pit mine spoil heaps are formed by the accumulation of large amounts of mining waste. The structure of the heap is loose and is affected by changes in load and rainwater infiltration, making it prone to slow deformation. If the deformation exceeds the safety threshold, it will cause disasters such as landslides and collapses, which will not only cause equipment damage and casualties, but also interrupt the mine's production process. Therefore, accurately obtaining the deformation of the spoil heap is a key link in assessing the stability of the heap, providing early warning of disasters, and ensuring the safe operation of the mine.

[0003] Currently, the industry commonly uses single-period, multi-temporal SAR image interferometry to monitor spoil heap deformation. This method extracts interferometric phase information and inverts deformation by comparing SAR images of the same area at different times. However, this method calculates based on images from a single period and does not consider the interference of surface elevation differences caused by mining activities on phase calculation. This results in elevation errors being mixed into the phase signal, making it impossible to accurately distinguish between elevation changes and actual deformation, thus causing errors in deformation calculation and failing to meet the actual needs of refined spoil heap monitoring.

[0004] Therefore, there is an urgent need for a new method to determine the shape variables of open-pit mine spoil heaps, so as to ensure the elimination of surface elevation interference, improve the accuracy of determining the shape variables of open-pit mine spoil heaps based on time-series InSAR technology, and provide technical support for safe mine operation. Summary of the Invention

[0005] This application provides a method and apparatus for determining the shape variables of open-pit mine spoil heaps based on time-series InSAR technology. The purpose is to effectively fuse continuous multi-period observations, eliminate the interference of surface elevation on SAR interferometric phase, and improve the determination accuracy of open-pit mine spoil heap shape variables.

[0006] To address the aforementioned technical problems, this application provides the following technical solutions:

[0007] The first aspect of this application provides a method for determining the shape variables of open-pit mine spoil heaps based on time-series InSAR technology, including:

[0008] Acquire SAR satellite imagery and digital surface model data of a target area within a preset period, wherein the digital surface model data and the SAR satellite imagery are in the same geometric space and have the same resolution;

[0009] Multiple multi-master image differential interferometry queues are constructed based on the SAR satellite imagery. Any multi-master image differential interferometry queue in the multiple multi-master image differential interferometry queues has SAR satellite imagery with overlapping observation dates with the adjacent multi-master image differential interferometry queues.

[0010] The differential interferometric phase is calculated based on the multiple multi-master image differential interferometric queues and their corresponding digital surface model data, and the unwrapped phase after atmospheric filtering is obtained by combining the differential interferometric phase with the singular value decomposition method.

[0011] Based on the sequential adjustment method, the deformation of the target region is calculated. After constructing an error estimation matrix containing elevation error terms and unwrapped phases, the sequential adjustment method uses the error estimation matrix to obtain the velocity matrix corresponding to the time phase, and calculates the deformation of the target region based on the velocity matrix.

[0012] The second aspect of this application provides a device for determining the shape variables of open-pit mine spoil heaps based on temporal InSAR technology, comprising:

[0013] The acquisition unit is used to acquire SAR satellite images and digital surface model data of a target area within a preset period, wherein the digital surface model data and the SAR satellite images are in the same geometric space and have the same resolution.

[0014] The construction unit is used to construct multiple multi-master image differential interferometry queues based on the SAR satellite images in the acquisition unit, wherein any multi-master image differential interferometry queue in the multiple multi-master image differential interferometry queues has SAR satellite images with overlapping observation dates with the adjacent multi-master image differential interferometry queues;

[0015] The calculation unit is used to calculate the differential interferometric phase based on the multiple multi-master image differential interferometric queues in the construction unit and the digital surface model data in the corresponding acquisition unit, and to obtain the unwrapped phase after atmospheric filtering by combining the differential interferometric phase with the singular value decomposition method.

[0016] The calculation unit is used to calculate the deformation of the target region based on the sequential adjustment method. After constructing an error estimation matrix containing elevation error terms and unwrapped phases, the sequential adjustment method uses the error estimation matrix to obtain the velocity matrix corresponding to the time phase, and calculates the deformation of the target region based on the velocity matrix.

[0017] A third aspect of this application provides a storage medium comprising a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the aforementioned method for determining the deformation of open-pit mine spoil heaps based on temporal InSAR technology.

[0018] A fourth aspect of this application provides an electronic device, the device including at least one processor, at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the above-described method for determining the shape variables of open-pit mine spoil heaps based on time-series InSAR technology.

[0019] Compared to existing technologies, the core of the method for determining the deformation of open-pit mine spoil heaps based on temporal InSAR technology in this application lies in accurately determining the deformation of open-pit mine spoil heaps using a sequential adjustment method. The sequential adjustment method ensures the accuracy of the deformation in three ways: First, the method constructs an error estimation matrix containing an elevation error term, incorporating elevation error as an independent parameter into the calculation. This accurately isolates the interference of elevation error on the temporal phase in digital surface model data, preventing elevation error from being mixed into the deformation data. Second, the sequential adjustment relies on the date overlap characteristics of multiple multi-master image differential interferometric cohorts, utilizing the overlapping SAR of adjacent cohorts. Imagery enables data integration, consolidating continuous temporal information when solving the velocity matrix, reducing errors caused by data fragmentation. Simultaneously, iterative optimization of elevation error estimates through multiple multi-master image differential interferometric queues reduces the accumulation of random errors. Furthermore, utilizing date overlap features, the error matrix includes constraint terms for the overlapping portion, achieving the integration of results from previous observation periods with the current calculation period to obtain continuous temporal information. Since the overlapping portion is part of data from previous observation periods, this significantly reduces computational load and improves efficiency when integrating continuous temporal information. Thirdly, the unwrapped phase obtained through singular value decomposition reduces phase noise interference, providing high-quality input data for sequential adjustment and ensuring the accuracy of the excavation field deformation variables calculated by the sequential adjustment method, thus providing reliable data support for stability assessment and disaster early warning. Attached Figure Description

[0020] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, with the same or corresponding reference numerals denoteing the same or corresponding parts, wherein:

[0021] Figure 1 A flowchart is shown for a method to determine the shape variables of an open-pit mine spoil heap based on time-series InSAR technology;

[0022] Figure 2 A schematic diagram of the arrangement of a multi-master image differential interferometry queue constructed based on the acquisition time of SAR satellite images using digital surface models is shown.

[0023] Figure 3A flowchart is shown for another method for determining the shape variables of open-pit mine spoil heaps based on time-series InSAR technology;

[0024] Figure 4 A structural diagram of another device for determining the deformation of open-pit mine spoil heaps based on temporal InSAR technology is shown.

[0025] Figure 5 A schematic diagram of another device for determining the deformation of open-pit mine spoil heaps based on temporal InSAR technology is shown. Detailed Implementation

[0026] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0027] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains.

[0028] Open-pit mine spoil heaps are formed by the accumulation of large amounts of mining waste. The heap structure is loose and susceptible to slow deformation due to load changes and rainwater infiltration. If the deformation exceeds a safety threshold, it can trigger landslides and collapses, causing equipment damage, casualties, and disruption to mining operations. Therefore, accurately acquiring spoil heap deformation data is crucial for assessing spoil heap stability, providing early warnings of disasters, and ensuring safe mine operations. Currently, the industry commonly uses single-phase, single-period SAR image interferometry to monitor spoil heap deformation. This method compares SAR images of the same area at different times, extracts interferometric phase information, and inverts the deformation data. However, this method calculates based on the influence of a single period and does not consider the interference of surface elevation differences caused by mining activities on phase calculations. This results in elevation errors being mixed into the phase signal, making it impossible to accurately distinguish between elevation changes and actual deformation, thus causing errors in deformation calculations and failing to meet the actual needs of refined spoil heap monitoring.

[0029] Based on this, the applicant proposed a time-series InSAR measurement scheme that integrates multiple high-resolution DSM data with improved sequential adjustment techniques. Through collaborative data preprocessing, construction of multiple multi-master image differential interferometric queues, multi-strategy phase optimization, and sequential adjustment fusion, it accurately separates elevation changes from actual deformation, achieving long-term, high-precision deformation monitoring of open-pit mine spoil heaps. Figure 1 As shown, the details are as follows:

[0030] Step 101: Obtain SAR satellite imagery and digital surface model data of the target area within a preset period. The digital surface model data and the SAR satellite imagery are in the same geometric space and have the same resolution.

[0031] In this step, the preset period is determined based on the mining frequency and the deformation pattern of the spoil heap, and is not further limited here. The target area covers the open-pit mine spoil heap and its surrounding buffer zone. The resolution of the SAR satellite imagery is not limited in this step. To ensure that the Digital Surface Model (DSM) and SAR satellite imagery are in the same geometric space and maintain high-resolution matching, a rigorous processing procedure is required. First, high-resolution raw DSM data (such as the Beijing-3 satellite DSM, with an elevation accuracy better than 5m) must be acquired. After checking for geographic offsets, it is registered to the first-phase DSM geometric space using a cross-correlation algorithm. Simultaneously, after radiometric calibration, multi-look processing, and geocoding of the SAR imagery, considering the location, imaging quality, and meteorological conditions of the remote sensing imagery throughout the observation period, one image is selected as the super master image, and the other images are registered with the super master image. Next, based on the lookup table generated by geocoding, the DSM product is resampled to the geometric space and resolution of the SAR satellite data. The DSM processed in this way not only has high resolution characteristics, but can also achieve spatial alignment with SAR data at the pixel level, thus meeting the requirement that the two are in the same geometric space and have the same resolution, laying a precise foundation for subsequent analysis applications based on multi-source data.

[0032] Among them, SAR satellite imagery is acquired by synthetic aperture radar satellites, unaffected by clouds, rain, or day / night cycles, and can extract information on ground deformation. DSM data is generated by digital surface models, containing elevation information of surface undulations, buildings, and vegetation. Radiometric calibration converts the grayscale values ​​of SAR images into backscattering coefficients, eliminating sensor errors. Geocoding establishes a mapping relationship between the SAR image radar coordinate system and the geographic coordinate system, achieving a correspondence between pixels and geographic coordinates.

[0033] Step 102: Using the adjustment principle, construct multiple multi-master image differential interferometry queues based on the SAR satellite images.

[0034] In this step, any of the multiple multi-master image differential interferometric queues has SAR satellite images with overlapping observation dates with adjacent multi-master image differential interferometric queues. The multi-master image differential interferometric queue is an interferometric image set constructed based on SAR satellite images in open-pit mine spoil heap deformation measurement, according to the adjustment principle. Each queue contains SAR satellite images within a specific observation period (including images used only by this queue and images overlapping with adjacent queues). Differential interferometric pairs are generated by setting a time baseline threshold (T≤50d) and a spatial baseline threshold (|B⊥|≤200m). Its core function is to provide continuous and reliable image data support for subsequent differential interferometric phase calculation, temporal phase solution, and deformation estimation, adapting to the long-term dynamic monitoring needs of mines. The master image refers to the SAR satellite image selected as the "reference reference" during the construction of the interferometric queue and image registration process. All other images (referred to as "slave images") must be spatially and phase aligned with this reference image. Its core value lies in reducing geometric deviations and phase noise between images by fixing a benchmark, thus providing a unified reference standard for subsequent differential interferometry calculations. The multi-master image differential interferometry queue is a satellite image queue without a limited benchmark reference, and it constructs an interferometry queue for all images that meet the temporal baseline threshold and spatial baseline threshold.

[0035] Specifically, the method of constructing multiple multi-master image differential interferometric queues is as follows: Figure 2 As shown, observations are conducted within a preset period. The observation time (observation time of the digital surface model data) for each of the multiple multi-master image differential interferometric queues is determined based on the date of acquisition of the digital surface model data. One set of multi-master image differential interferometric queues comprises differential interferometric pairs formed by all SAR satellite images from one set of multi-master image differential interferometric queues and SAR satellite images shared by two sets of multi-master image differential interferometric queues. The two sets of multi-master image differential interferometric queues are the next multi-master image differential interferometric queues adjacent to the first set. The timestamp of the shared SAR satellite images is the midpoint of the observation period of the first set of multi-master image differential interferometric queues. Specifically, a set of multi-master image differential interferometric queues can be the first set of multi-master image differential interferometric queues or an intermediate set; no limitation is made here.

[0036] Specifically, a) the first group of multi-principal image differential interferometric queues should include all images in the first interferometric queue, totaling N. 1,1 Scene. b) The image N contained in both the preceding and following interferometric queues. 1,2 The first observation period consists of N1 images, forming M1 differential interferometric pairs.

[0037] When the observation period exceeds two, any interferometric queue (denoted as i, i) from the second group to the (N-1)th group... [2, N-1], where N is greater than 2) should simultaneously include: a) all images used only in the i-th queue, totaling N. i,i Scene image. b) The last N of the previous observation period i-1,i c) The images included together with the following observation cohort total N. i,i+1 Scene. The i-th interference queue contains a total of N images. i Scenery, together forming an interference pair M i indivual.

[0038] The final round of observations, i.e., the Nth interferometric cohort, should include: (1) all images from the Nth observation period, with the time obtained from the Nth period DSM. From then until the end of the observation, a total of N n,n Scene; (2) the last observation period of the previous observation period The Nth interferometric cohort contains a total of [number] images. Scenery, forming an interference pair Notably, the i-th interferometric cohort contains the number of image periods from the previous observation period. , When generating each interferometric queue, both the temporal baseline threshold and the spatial baseline threshold are set simultaneously. This construction method divides observation time into DSM dates, adapting to dynamic elevation changes in spoil heaps and reducing the interference of terrain on subsequent phase calculations. Adjacent queues contain more than 1 / 3 overlapping images, with the overlap starting in the middle of the observation period of the preceding queue, ensuring continuous connection of multi-cycle data and avoiding gaps in deformation monitoring. At the same time, by controlling temporal and spatial baseline thresholds, the influence of temporal decorrelation and orbital errors is reduced. Interferometric pairs generated by queues balance efficiency and quality, laying a solid data foundation for subsequent high-precision phase calculations and deformation inversion.

[0039] Step 103: Calculate the differential interferometric phase based on the multiple multi-master image differential interferometric queues and their corresponding digital surface model data, and use the differential interferometric phase combined with the singular value decomposition method to obtain the unwrapped phase after atmospheric filtering.

[0040] Using the differential interferometric pairs from multiple multi-master image differential interferometric queues obtained in step 102, and based on the DSM data of the corresponding time period, differential interferometric phases are obtained by removing flat terrain phases and topographic phases. The differential interferometric phases are then filtered to reduce noise, and phase unwrapping is performed to obtain absolute phase values ​​(relative to the reference point). The unwrapped differential interferometric phases from each queue are converted into temporal phases using singular value decomposition, and principal components with high temporal correlation and low spatial correlation are separated. This allows for the extraction of unwrapped phases reflecting the deformation of the target area. This method effectively reduces noise interference and accurately extracts unwrapped phases, providing a reliable data foundation for subsequent deformation calculations.

[0041] Step 104: Calculate the deformation of the target region based on the sequential adjustment method.

[0042] The sequential adjustment method constructs an error estimation matrix containing elevation error terms and unwrapped phases, then uses the error estimation matrix to obtain the velocity matrix corresponding to the time phase, and calculates the deformation of the target region based on the velocity matrix. The velocity matrix corresponding to the time phase obtained by using the error estimation matrix can be obtained by performing optimal estimation using the error estimation matrix.

[0043] In this step, when calculating the deformation of the target area based on the sequential adjustment method, an error estimation matrix containing elevation error terms is first constructed according to the number of observation periods. The first period matrix includes the phase change rate estimation term of the independent image of the current period, the rate estimation term of the image overlapping with the next period, and the elevation error terms corresponding to the two types of images. The intermediate periods add an additional term overlapping with the adjacent preceding and following periods. The last period only retains the term overlapping with the previous period and the independent term of the current period, and all are substituted into the unwrapped interferometric phase and unit weight matrix of the corresponding period. Then, the least squares method is used to solve the problem. The initial velocity and elevation error are directly obtained in the first period. The intermediate and last periods are combined with the adjustment results of adjacent periods (such as the velocity and elevation error cofactor matrix of the overlapping period) and iteratively solved with the current observation data to obtain the velocity matrix of the complete temporal phase. Then, the cumulative phase change of each period is calculated by combining the observation time interval of each period with the velocity matrix and converted into line-of-sight deformation, thus completing the calculation of the deformation of the target area.

[0044] For consecutive multi-observation periods, based on the differential interferometric phase of each observation period, a sequential adjustment algorithm is used to solve for the complete time-series phase and estimated elevation correction values ​​covering all dates. The error estimation matrices for each stage of the sequential adjustment algorithm model are as follows:

[0045] For the first observation period, the temporal phase estimation error matrix based on the indirect adjustment principle for the first set of multi-master image differential interferometric cohort deformation estimation is as follows:

[0046]

[0047] Based on the overall estimation of interferometric deformation using indirect adjustment, for the last observation in a multi-round observation period (i.e., the nth observation period) Based on the principle of indirect adjustment, the above time-series phase estimation error matrix is:

[0048]

[0049] If more than two monitoring sessions are conducted, for any of the 2 to N-1 observation periods in between, m ( [2~N-1]), the time-series phase estimation error matrix based on the indirect adjustment principle is:

[0050] If more than two monitoring sessions are conducted, for any of the 2 to N-1 observation periods in between, m ( [2~N-1]), the time-series phase estimation error matrix based on the indirect adjustment principle is:

[0051]

[0052] Where i represents any observation period, and V is the error estimation matrix. Let be the differential unwrapping interferometric phase matrix of the multi-principal images for the i-th observation period. This is a general estimate of the phase change rate for all times in the i-th observation period that coincide with the (i-1)-th observation period. This represents the elevation error estimate corresponding to the differential interferometric phase of all SAR satellite images included in this observation period. This is a general estimate of the phase change rate that occurs only in the m-th observation period. This refers to the elevation error estimates corresponding to the differential interferometric phase of all SAR satellite imagery containing only images from the current observation period. This is a general estimate of the phase change rate for all times in the i-th observation period that coincide with the (i+1)-th observation period. The elevation error estimate for all differential interferometric phases containing this SAR satellite image is: for The estimated matrix from the first adjustment calculation, Let be the differential unwrapping interferometric phase estimation matrix for the i-th observation period. for The coefficient at the i-th observation, for The coefficient at the i-th observation, for The coefficient at the i-th observation, This represents the time interval between the observation date of period j and the observation date of period i. The superscript (i) indicates the coefficient at the time of the observation for dates that coincide with the dates of periods j and i. , , Elevation error , , The phase conversion coefficient matrix has i = 1 for the first observation period, i = n for the last observation period, and i = m for any intermediate observation period.

[0053]

[0054] in, express and Differential Interferometric Phase Composed of Two Images Sensitivity to changes in elevation:

[0055]

[0056] For carrier wavelength, for and Vertical baselines of the two image phases. R is the distance from the target to the radar. The incident angle of a pixel.

[0057] Among them, the difference between any two observation periods of DSM data hour, The average elevation accuracy of the acquired DSM products. , If the elevation values ​​are measured before the start of any two observation periods i and j, then the elevation error for each observation period is defined as a uniform value and estimated.

[0058] Furthermore, this application also provides a more specific method for determining the shape variables of open-pit mine spoil heaps based on time-series InSAR technology, such as... Figure 3 As shown, the details are as follows:

[0059] Step 301: Obtain SAR satellite images and DSM data of the target area within a preset period, and construct multiple multi-master image differential interferometry queues based on the SAR satellite images.

[0060] The method involves constructing multiple multi-master image differential interferometric queues based on the SAR satellite imagery. These queues include: a first multi-master image differential interferometric queue containing all SAR satellite imagery from the first queue and differential interferometric pairs formed by SAR satellite imagery shared by two other multi-master image differential interferometric queues; the second queue being the next multi-master image differential interferometric queue adjacent to the first queue; and the shared SAR satellite imagery being the timestamp at which the second queue began acquiring SAR satellite imagery being the midpoint of the observation period for the first queue. By including all its own imagery and the shared imagery from the two queues within the first queue, and ensuring the shared imagery corresponds to the midpoint of the observation period for the first queue, the method ensures continuous data connection between adjacent queues to avoid monitoring gaps, adapts to dynamic elevation changes in the spoil heap, reduces terrain interference with subsequent phase calculations, provides reliable data support for high-precision phase calculation and deformation inversion, and helps improve measurement accuracy.

[0061] Step 302: Calculate the differential interferometric phase based on the multiple multi-master image differential interferometric queues and their corresponding DSM data, and use the differential interferometric phase combined with the singular value decomposition method to obtain the unwrapped phase after atmospheric filtering.

[0062] In this step, the differential interferometric phase is calculated based on the multiple multi-master image differential interferometric queues and the DSM data, including: using differential interferograms to perform differential interferometric processing on each interferometric pair in the multiple multi-master image differential interferometric queues, calculating the coherence coefficient, which is used as an index to measure the phase stability of two SAR images at corresponding pixels; filtering the differential interferometric phase using the coherence coefficient as a weight matrix to obtain a new differential interferometric phase. Furthermore, after filtering the differential interferometric phase using the coherence coefficient as a weight matrix to obtain a new differential interferometric phase, the method further includes: using the correlation model between phase and baseline based on the DSM data to calculate the baseline deviation and the corresponding flat and terrain phases for the baseline correction; removing the flat and terrain phases from the new differential interferometric phase to generate a corrected differential interferometric phase; and re-unwrapping the corrected differential interferometric phase to obtain the target differential interferometric phase, i.e., the unwrapped phase. Therefore, by performing differential interferometric processing on each interferometric pair in multiple multi-master image differential interferometric queues, and combining it with DSM data, the differential interferometric phase is first optimized by filtering with the coherence coefficient as the weight matrix to highlight stable phase information and reduce noise. Then, the baseline deviation and flat and topographic phases are calculated and removed using the phase-baseline correlation model to eliminate track deviation interference. Finally, the target differential interferometric phase is obtained by re-phase unwrapping, which lays a high-precision data foundation for subsequent singular value decomposition to solve the time-series phase, effectively improving the reliability of phase data and helping to accurately invert the deformation of open-pit mine spoil heaps.

[0063] It is worth noting that the process of obtaining the unwrapped phase after filtering using differential interferometric phase combined with singular value decomposition includes: obtaining the autocorrelation length of the target area; when the target area is multiple open-pit mine spoil heaps and the autocorrelation length is less than a preset length (the preset length being the minimum of the length and width of the interferometric image), a first filtering strategy is adopted, which includes performing low-frequency filtering of the spatial dimension on the differential interferometric phase of the SAR satellite image; when the target area is a single open-pit mine, or when the target area is multiple open-pit mine spoil heaps and the autocorrelation length is greater than the preset length, a second filtering strategy is adopted, which includes performing time-dimensional filtering and independent spatial-dimensional low-frequency filtering on the differential interferometric phase of the SAR satellite image.

[0064] Step 303: Calculate the deformation of the target region based on the sequential adjustment method.

[0065] The method for calculating the deformation of the target region based on the sequential adjustment method includes: constructing an error estimation matrix containing an elevation error term and unwrapped phase characteristics using the target differential interferometric phase based on the indirect adjustment principle; solving the error estimation matrix using the least squares criterion to obtain the estimated values ​​of the temporal phase change rate and elevation error for each observation period within a preset period; obtaining a velocity matrix based on the estimated values ​​of the temporal phase change rate and elevation error, and converting the velocity matrix into the cumulative deformation of the target region in combination with the observation time.

[0066] Furthermore, the preset period includes at least two observation periods, and the construction of the error estimation matrix, which includes an elevation error term and unwrapping phase characteristics, includes:

[0067] For any intermediate observation period, the error estimation matrix is:

[0068]

[0069] Based on the above discussion, this application also provides specific examples, as follows:

[0070] First, the data used was DSM data produced by the stereo mapping mode of the Beijing-3 satellite. Geographic deviation was checked on the collected DSM data from two observation periods. Using the DSM from the first observation period as a reference, a cross-correlation algorithm was used to register the DSM from the second observation period. The images were captured in June and October 2023, with a resolution of 1m and relative elevation errors of 2.3m and 2.1m, respectively. This embodiment uses SLC data from the open-source Sentinel-1 Level-1A satellite. Radiometric calibration, multi-look, and geocoding were performed on the collected SAR satellite image dataset. An intermediate period image was selected as the super master image for registration of the other images. The multi-look distance look-ahead number was set to 4, and the azimuth look-ahead number was set to 1. The spatial resolution of the SAR satellite data after multi-look was better than 15m. The pixel resolution after geocoding was 10m. Based on the lookup table generated during the geocoding process, the DSM was resampled to the spatial coordinates and resolution of the SAR data. The basic parameters of the Sentinel-1 satellite imagery data include the satellite name Sentinel-1A, its operation in the C-band, its carrier frequency of 5.405 GHz, its polarization mode of VV mode, its imaging mode of interferometric wide swath mode, and its resolution of 2.33 × 13.97 m (range × azimuth).

[0071] Secondly, based on the acquisition time of the DSM during the two observation periods, two sets of multi-master image differential interferometric sequences were designed, generating a "time-vertical baseline" connection map for all differential interferometric pairs. The first round of observations included 9 images from August to November, and the second and final round included 8 images from October to January of the following year. The two sets also included 3 images from the same date.

[0072] Next, differential interferometry and coherence coefficient estimation are performed on the preprocessed images. The interferometric coherence coefficients are used as the weight matrix, and Goldstein filtering is applied to the obtained initial differential interferometric phase dataset to improve the interferometric phase quality. A combination of visual inspection and DSM (Discrete Structural Modeling) is used, selecting a point near the outer edge of the open-pit spoil heap (any point on the outer edge is acceptable) as the unwrapping reference point. After setting the reference points, the minimum cost flow method is used for phase unwrapping. After obtaining the initial phase, based on the correlation between the phase and the baseline, the corresponding DSM is used to estimate the residual baseline values ​​for both sets of interferometric phases. The residual terrain and flatland phases are simulated based on the baseline residuals, and then differiated with the existing differential interferometric phases. After re-filtering, phase unwrapping is performed again.

[0073] For the obtained unwrapped phase, the temporal phase ϕ is calculated using singular value decomposition. The mining area has a small coverage area, with an autocorrelation length of approximately 1.2 km, exceeding the monitoring area. Therefore, strategy two is used for two-step filtering: First, linear least squares fitting is performed on the phase to obtain the estimated phase deformation rate v and residual σ. Then, areas with deformation values ​​less than 10 mm are selected, and a box filter is used to filter the residuals with a spatial filtering window of 15×15. The Springs method is then used to interpolate the filtering results. After obtaining the noise estimate of the temporal phase, it is differentially analyzed according to a multi-master image differential interferometric queue and subtracted from the unwrapped phase to obtain the final interferometric phase used to estimate the deformation.

[0074] Finally, the temporal deformation of the surface line of sight is estimated based on the above sequential adjustment algorithm. Specifically, the first set of interferometric deformation estimates is based on indirect adjustment.

[0075]

[0076] Among them, the phase of the first group of multi-principal image differential decoupling interference is ,forward The first estimated value of the scene's temporal phase change rate is The elevation error estimates corresponding to all differential interferometric images containing this image set are: .back The first velocity estimate of the scene image is The elevation error estimates corresponding to all differential interferometric analyses containing only this image are: . This is the correlation matrix of the interference phase. The estimation matrix for the phase of the differential unwrapping interference is expressed as:

[0077]

[0078]

[0079] in, express and Differential Interferometric Phase Composed of Two Images Sensitivity to changes in elevation:

[0080] Solved using the least squares method, satisfying... To obtain the optimal estimate, that is:

[0081]

[0082] Therefore, the solution estimated in the first step is:

[0083]

[0084] in, Let be the cofactor matrix of the estimated values ​​I and J. For the cofactor matrix:

[0085]

[0086] According to the elevation accuracy of DSM products, for elevation differences In this situation, it is believed that , To simplify, then:

[0087]

[0088] Variance estimated in the first attempt:

[0089]

[0090] When only the first observation period is available, the deformation of the open-pit mine spoil heap is calculated as follows:

[0091]

[0092] Where, d LOS λ represents the deformation of the open-pit mine spoil heap, λ is the carrier wavelength, which is an inherent parameter of the satellite and needs to be determined according to the SAR satellite model used, and ϕ is the timing phase.

[0093] The variance estimated in the first step is an accuracy assessment index for the first stage of sequential adjustment (i.e., the first set of interferometric cohorts, such as the nine image observations from August to November in the example). Its core function is to quantify the error level and reliability of single-cycle observation data after adjustment. Logically, it compares the measured differential unwrapped interferometric phase of the first set of interferometric cohorts with the phase estimate obtained from the first adjustment, calculates the sum of squares of their deviations, and normalizes it using the first set of interferometric pairs. The resulting variance directly reflects the correction effect of the adjustment model on the original phase error within a single cycle. That is, the smaller the variance, the smaller the deviation between the phase estimate and the measured value after adjustment, and the higher the accuracy of the phase data within a single cycle, which can serve as reliable basic data for subsequent multi-cycle fusion. Conversely, if the variance is too large, it indicates a problem with the preprocessing of the first set of images (such as registration and filtering) or the parameter settings of the adjustment model, requiring backtracking and optimization to avoid low-precision data affecting the overall monitoring results. Meanwhile, the variance result can also provide a basis for weight allocation when fusing multiple periods of data. For example, the first set of data with small variance and high precision can be given higher weight when fusing with subsequent period data to ensure the accuracy of the fusion result.

[0094] The two sets of overall estimates of interferometric deformation based on indirect adjustment are as follows:

[0095] Based on the principle of indirect adjustment, the overall estimation error of the time series phase between the two time periods is:

[0096]

[0097] Among them, the phase of the second group of multi-principal image differential decoupling interference is ,back The estimated velocity of the scene image set is The elevation error estimates corresponding to all differential interferometric images containing this image set are: . The estimation matrix for the phase of the differential unwrapped interferometric interference of the second set of multi-principal images:

[0098]

[0099] If remember and The correction value in the second adjustment is , ,but

[0100]

[0101] , This is the result of the first adjustment.

[0102] Considering the elevation errors of both sets of interferometric sequences simultaneously, and introducing the first indirect result, the conditional function for estimating the overall error is:

[0103]

[0104] Based on the least squares criterion, satisfying The optimal solution is, i.e.

[0105]

[0106] At this point, the overall estimated value is:

[0107]

[0108] and The solution for the second adjustment is:

[0109]

[0110] For elevation difference In this situation, it is believed that , The simplified result is:

[0111]

[0112] The simplified solution after the second adjustment is:

[0113]

[0114] The variance of the second estimate:

[0115]

[0116] The variance estimated in the second step is an overall accuracy assessment index for the sequential adjustment multi-period fusion stage (e.g., after fusing the first group of data from August to November and the second group from October to January of the following year in the example). Its core function is to verify the reliability of the multi-period data fusion results and the effectiveness of the improved sequential adjustment model. Its calculation logic is to comprehensively consider the "deviation between the measured phase and the phase estimate after adjustment" of all fused periods (e.g., the first group and the second group), calculate the total sum of squares of deviations, and normalize the total number M1+M2 by all period interferences. The final variance value reflects the overall error level of the multi-period data after cross-period error propagation and collaborative correction. On the one hand, by comparing the second estimated variance with the first estimated variance, if the second estimated variance is smaller than the first estimated variance, it can be proven that the sequential adjustment model of this application (including elevation error term and multi-period overlapping data association) can effectively reduce the cumulative error of multi-period observations, and the model design meets the dynamic monitoring needs of open-pit mine spoil heaps. On the other hand, the variance result directly provides a basis for the credibility labeling of the final deformation result. For example, when the variance is small, the accuracy range of the deformation (such as ±5mm) can be clearly labeled, providing accurate precision reference for mine safety production supervision (such as landslide risk assessment) and spoil heap reclamation decision-making, avoiding decision-making bias caused by the uncertainty of the result.

[0117] First, based on the sequential adjustment calculation, the temporal velocity matrix is ​​obtained. Then, based on the observation time, the cumulative deformation of each phase is calculated. In addition, the annual average deformation rate can be calculated. Secondly, the phase and line-of-sight deformation conversion is performed. The deformation of the open-pit mine spoil heap is calculated as follows: The formula for the deformation of the open-pit mine spoil heap is as follows:

[0118]

[0119] Where, d LOS The deformation of the open-pit mine spoil heap is represented by λ, where λ is the carrier wavelength, an inherent parameter of the satellite, which needs to be determined based on the type of SAR satellite used. It is the cumulative phase deformation of the phase difference between each period and the first observation period, i.e., all time-series phases.

[0120] Furthermore, as a response to the above Figure 1 or Figure 3The implementation of the method embodiment shown in this invention provides a device for determining the shape variables of open-pit mine spoil heaps based on temporal InSAR technology. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this embodiment will not repeat the details of the foregoing method embodiment, but it should be understood that the device in this embodiment can implement all the contents of the foregoing method embodiment. For example... Figure 4 As shown, it includes:

[0121] Acquisition unit 41 is used to acquire SAR satellite images and digital surface model data of the target area within a preset period, wherein the digital surface model data and the SAR satellite images are in the same geometric space and have the same resolution;

[0122] Construction unit 42 is used to construct multiple multi-master image differential interferometry queues based on the SAR satellite images in the acquisition unit 41, wherein any multi-master image differential interferometry queue in the multiple multi-master image differential interferometry queues has SAR satellite images with overlapping observation dates with adjacent multi-master image differential interferometry queues;

[0123] The calculation unit 43 is used to calculate the differential interference phase based on the multiple multi-master image differential interferometry queues in the construction unit 42 and the digital surface model data in the corresponding acquisition unit 41, and to obtain the unwrapped phase after atmospheric filtering by combining the differential interference phase with the singular value decomposition method.

[0124] The calculation unit 43 is used to calculate the deformation of the target region based on the sequential adjustment method. After constructing an error estimation matrix containing elevation error terms and unwrapping phase, the sequential adjustment method uses the error estimation matrix to obtain the velocity matrix corresponding to the time phase, and calculates the deformation of the target region based on the velocity matrix.

[0125] Furthermore, such as Figure 5 As shown, the calculation unit 43 includes:

[0126] Module 431 is used to perform differential interferometric processing on each interference pair in the plurality of multi-master image differential interferometric queues to obtain differential interferometric phase and differential interferogram.

[0127] The obtained module 431 is used to calculate the coherence coefficient using the differential interferogram. The coherence coefficient is used as an index to measure the phase stability of two SAR images at corresponding pixels.

[0128] The obtaining module 431 is used to filter the differential interference phase using the coherence coefficients as a weight matrix to obtain a new differential interference phase.

[0129] Furthermore, such as Figure 5As shown, after filtering the differential interference phase using the coherence coefficients as a weight matrix to obtain a new differential interference phase, the device further includes a calculation module 432, which includes:

[0130] Based on the digital surface model data, the correlation model between phase and baseline is used to calculate the baseline deviation and the flat and topographic phases corresponding to the baseline correction.

[0131] Remove the flat terrain and topographic phases from the new differential interferometric phase to generate the corrected differential interferometric phase;

[0132] The corrected differential interference phase is re-unwrapped to obtain the target differential interference phase.

[0133] Furthermore, such as Figure 5 As shown, the calculation module 432 includes the following: After atmospheric filtering, the unwrapped phase is obtained by using differential interferometric phase decomposition combined with singular value decomposition.

[0134] Obtain the autocorrelation length of the target region;

[0135] When the target area is multiple open-pit mine spoil heaps, and the autocorrelation length is less than the preset length, where the preset length is the minimum of the length and width of the interferometric image, a first filtering strategy is adopted. The first filtering strategy includes performing low-frequency filtering of the spatial dimension on the differential interferometric phase of the SAR satellite image.

[0136] When the target area is a single open-pit mine, or when the target area is multiple open-pit mine spoil heaps, and the autocorrelation length is greater than the preset length, a second filtering strategy is adopted. The second filtering strategy includes sequentially performing time-dimension filtering and independent spatial-dimension low-frequency filtering on the differential interferometric phase of the SAR satellite image.

[0137] Furthermore, such as Figure 5 As shown, the building unit 42 includes:

[0138] A set of multi-master image differential interferometric queues includes all SAR satellite images from the first set of multi-master image differential interferometric queues and the differential interferometric pairs formed by the SAR satellite images jointly contained in the second set of multi-master image differential interferometric queues. The second set of multi-master image differential interferometric queues is the next multi-master image differential interferometric queue adjacent to the first set of multi-master image differential interferometric queues. The SAR satellite images jointly contained are the timestamps at which the second set of multi-master image differential interferometric queues began acquiring SAR satellite images, which are the intermediate timestamps of the observation period of the first set of multi-master image differential interferometric queues.

[0139] Furthermore, such as Figure 5 As shown, the computing unit 43 includes:

[0140] Module 433 is used to construct an error estimation matrix containing elevation error terms and unwrapped phase characteristics based on the principle of indirect adjustment and the target differential interferometric phase.

[0141] The parameter determination module 434 is used to solve the error estimation matrix of the construction module 433 using the least squares criterion, so as to obtain the estimated value of the temporal phase change rate and the estimated value of the elevation error for each observation period within the preset period;

[0142] The deformation calculation module 435 is used to obtain a velocity matrix based on the estimated value of the temporal phase change rate and the estimated value of the elevation error of the determination parameter module 434, and to convert the velocity matrix into the cumulative deformation of the target area in combination with the observation time.

[0143] Furthermore, such as Figure 5 As shown, the preset period includes at least two observation periods, and the construction module 433 includes: for any intermediate observation period, the error estimation matrix is:

[0144]

[0145] Furthermore, embodiments of the present invention also provide a readable storage medium for storing a computer program, wherein the computer program, when running, controls the device where the storage medium is located to perform the above-described actions. Figure 1-3 The method for determining the deformation of open-pit mine spoil heaps based on time-series InSAR technology as described in any one of the following.

[0146] Furthermore, embodiments of the present invention also provide an electronic device, the electronic device including a storage medium; and one or more processors, the storage medium being coupled to the processors, the processors being configured to execute program instructions stored in the storage medium; the program instructions, when executed, perform as described above. Figure 1-3 The method for determining the deformation of open-pit mine spoil heaps based on time-series InSAR technology as described in any one of the following.

[0147] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0148] It is understood that the relevant features in the above methods and apparatus can be referenced interchangeably. Furthermore, the terms "first," "second," etc., in the above embodiments are used to distinguish between embodiments and do not represent the superiority or inferiority of any particular embodiment.

[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0150] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention. Additionally, the memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory may include at least one memory chip.

[0151] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0153] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0154] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0155] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0156] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0157] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0158] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for determining the shape variables of open-pit mine spoil heaps based on time-series InSAR technology, characterized in that, include: Acquire SAR satellite imagery and digital surface model data of a target area within a preset period, wherein the digital surface model data and the SAR satellite imagery are in the same geometric space and have the same resolution; Multiple multi-master image differential interferometry queues are constructed based on the SAR satellite imagery. Any multi-master image differential interferometry queue in the multiple multi-master image differential interferometry queues has SAR satellite imagery with overlapping observation dates with the adjacent multi-master image differential interferometry queues. The differential interferometric phase is calculated based on the multiple multi-master image differential interferometric queues and their corresponding digital surface model data, and the unwrapped phase after atmospheric filtering is obtained by combining the differential interferometric phase with the singular value decomposition method. The deformation of the target region is calculated based on the sequential adjustment method. After constructing an error estimation matrix containing elevation error terms and unwrapped phases, the sequential adjustment method uses the error estimation matrix to obtain the velocity matrix corresponding to the time phase, and calculates the deformation of the target region based on the velocity matrix.

2. The method according to claim 1, characterized in that, The calculation of differential interferometric phase based on the multiple multi-master image differential interferometric queues and the digital surface model data includes: Differential interferometry is performed on each interference pair in the multiple multi-master image differential interferometry queues to obtain differential interferometry phase and differential interferogram; The coherence coefficient is calculated using differential interferograms, and the coherence coefficient is used as an index to measure the phase stability of two SAR images at corresponding pixels; The coherence coefficients are used as a weight matrix to filter the differential interference phase, resulting in a new differential interference phase.

3. The method according to claim 2, characterized in that, After filtering the differential interferometric phase using the coherence coefficients as a weight matrix to obtain a new differential interferometric phase, the method further includes: Based on the digital surface model data, the correlation model between phase and baseline is used to calculate the baseline deviation and the corresponding flat and topographic phases for the baseline correction. Remove the flat land and terrain phases from the new differential interferometric phase to generate the corrected differential interferometric phase; The corrected differential interference phase is re-unwrapped to obtain the target differential interference phase.

4. The method according to claim 3, characterized in that, The unwrapped phase, after atmospheric filtering, is obtained by using differential interferometric phase decomposition combined with singular value decomposition, including: Obtain the autocorrelation length of the target region; When the target area is multiple open-pit mine spoil heaps, and the autocorrelation length is less than the preset length, where the preset length is the minimum of the length and width of the interferometric image, a first filtering strategy is adopted. The first filtering strategy includes performing low-frequency filtering of the spatial dimension on the differential interferometric phase of the SAR satellite image. When the target area is a single open-pit mine, or when the target area is multiple open-pit mine spoil heaps, and the autocorrelation length is greater than the preset length, a second filtering strategy is adopted. The second filtering strategy includes sequentially performing time-dimension filtering and independent spatial-dimension low-frequency filtering on the differential interferometric phase of the SAR satellite image.

5. The method according to claim 1, characterized in that, Multiple multi-master image differential interferometric queues are constructed based on the SAR satellite imagery, including: A set of multiple multi-master image differential interferometric queues contains all the SAR satellite images of the set of multiple multi-master image differential interferometric queues and the differential interferometric pairs formed by the SAR satellite images jointly contained in the two sets of multiple multi-master image differential interferometric queues. The two sets of multiple multi-master image differential interferometric queues are the next multiple multi-master image differential interferometric queues adjacent to the set of multiple multi-master image differential interferometric queues. The SAR satellite images jointly contained are the timestamps at which the two sets of multiple multi-master image differential interferometric queues begin acquiring SAR satellite images. The timestamps are the middle timestamps of the observation period of the set of multiple multi-master image differential interferometric queues.

6. The method according to claim 4, characterized in that, The calculation of the deformation of the target region based on the sequential adjustment method includes: Based on the principle of indirect adjustment, an error estimation matrix containing elevation error terms and unwrapped phase characteristics is constructed using the target differential interferometric phase. The error estimation matrix is ​​solved using the least squares criterion to obtain the estimated values ​​of the temporal phase change rate and the elevation error for each observation period within the preset period; Based on the estimated values ​​of the temporal phase change rate and the estimated values ​​of the elevation error, a velocity matrix is ​​obtained, and the velocity matrix is ​​converted into the cumulative deformation of the target area by combining the observation time.

7. The method according to claim 6, characterized in that, The preset period includes at least two observation periods, and the construction of an error estimation matrix containing an elevation error term and unwrapped phase characteristics includes: For any intermediate observation period, the error estimation matrix is: Where V is the error estimation matrix, Let m be the differential unwrapping interferometric phase matrix of the multi-principal images in the m-th observation period. This is a general estimate of the phase change rate for all times in the m-th observation period that coincide with the m-1th observation period. This represents the elevation error estimate corresponding to the differential interferometric phase of all SAR satellite images included in this observation period. This is a general estimate of the phase change rate that occurs only in the m-th observation period. This refers to the elevation error estimates corresponding to the differential interferometric phase of all SAR satellite images that only contain images from the current observation period. This is a general estimate of the phase change rate for all times in the m-th observation period that coincide with the (m+1)-th observation period. The elevation error estimate for all differential interferometric phases containing this SAR satellite image is: Let m be the differential unwrapping interferometric phase estimation matrix for the m-th observation period. for The coefficient at the m-th observation, for The coefficient at the m-th observation, for The coefficient at the m-th observation, superscript ( () represents the coefficient at the m-th observation for dates that coincide with the dates before and after it. , , Elevation error , , The phase conversion coefficient matrix, This is the correlation matrix of the interference phase.

8. A device for determining the shape variables of an open-pit mine spoil heap based on time-series InSAR technology, characterized in that, include: The acquisition unit is used to acquire SAR satellite images and digital surface model data of a target area within a preset period, wherein the digital surface model data and the SAR satellite images are in the same geometric space and have the same resolution. The construction unit is used to construct multiple multi-master image differential interferometry queues based on the SAR satellite images in the acquisition unit, wherein any multi-master image differential interferometry queue in the multiple multi-master image differential interferometry queues has SAR satellite images with overlapping observation dates with adjacent multi-master image differential interferometry queues; The calculation unit is used to calculate the differential interferometric phase based on the multiple multi-master image differential interferometric queues in the construction unit and the digital surface model data in the corresponding acquisition unit, and to obtain the unwrapped phase after atmospheric filtering by combining the differential interferometric phase with the singular value decomposition method. The calculation unit is used to calculate the deformation of the target region based on the sequential adjustment method. After constructing an error estimation matrix containing elevation error terms and unwrapped phases, the sequential adjustment method uses the error estimation matrix to obtain the velocity matrix corresponding to the time phase, and calculates the deformation of the target region based on the velocity matrix.

9. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to perform the method for determining the deformation of open-pit mine spoil heaps based on temporal InSAR technology as described in any one of claims 1-7.

10. An electronic device, characterized in that, The device includes at least one processor, at least one memory connected to the processor, and a bus; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the method for determining the shape variables of open-pit mine spoil heaps based on time-series InSAR technology as described in any one of claims 1-7.

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