Nonlinear deformation monitoring method, device and medium for mining area

Abstract

The application discloses a kind of mining area nonlinear deformation monitoring method, equipment and medium.The method comprises: based on Boltzmann time function, function model of the change of mining area deformation variable with time is established;Deformation variable is S-type nonlinear deformation variable, including maximum settlement, maximum settlement velocity occurrence time and settlement rate coefficient;Establish nonlinear deformation observation model associated with characteristic parameters and unwrapping phase;Synthetic aperture radar image data of target mining area is obtained, and synthetic aperture radar image data is registered;Based on the synthetic aperture radar image data after registration, construct small baseline set interference network, process small baseline set interference network to generate unwrapping phase diagram;Unwrapping phase data in unwrapping phase diagram is substituted into deformation model, and S-type nonlinear deformation variable of target mining area is obtained by solving.The application improves the precision of mining area nonlinear deformation monitoring.

Description

Technical Field

[0001] This invention belongs to the field of radar remote sensing mapping technology, and more specifically, relates to a method, equipment and medium for monitoring nonlinear deformation in mining areas. Background Technology

[0002] Underground mining operations remove underground ore bodies, causing the overlying rock strata to lose support and bend, fracture, or even collapse under gravity. This movement and destruction of the rock strata gradually propagates from bottom to top, eventually leading to continuous subsidence and deformation of the surface, forming a subsidence basin.

[0003] Synthetic Aperture Radar Interferometry (InSAR), particularly small baseline set technology, is a commonly used method for monitoring surface deformation. In conventional continuous monitoring operations, small baseline set technology is typically based on the assumption that the surface undergoes linear deformation within the time interval between two adjacent satellite images. It calculates the average subsidence velocity over adjacent time periods and then sums these values ​​to derive the total subsidence over the entire time series. However, surface subsidence in mining areas is influenced by underground mining and typically exhibits an "S"-shaped nonlinear dynamic evolution characteristic: "slow onset – accelerated development – ​​tendency to stabilize." This traditional approach, based on linear assumptions, struggles to accurately describe the complex nonlinear subsidence process in mining areas, easily leading to significant discrepancies between the generated monitoring results and the actual monitoring results. Summary of the Invention

[0004] The main objective of this invention is to provide a method, equipment, and medium for monitoring nonlinear deformation in mining areas, in order to overcome the shortcomings of existing technologies.

[0005] The first aspect of this invention provides a method for monitoring nonlinear deformation in a mining area, characterized by comprising: establishing a functional model of the deformation of the mining area over time based on a Boltzmann time function; the deformation being an S-shaped nonlinear deformation, including the maximum subsidence, the time of occurrence of the maximum subsidence velocity, and the subsidence rate coefficient; jointly establishing the functional model to characterize the mapping relationship between the unwrapped phase in the small baseline interferogram of the mining area and the deformation of the mining area, wherein the deformation model is a nonlinear deformation observation model; acquiring synthetic aperture radar (SAR) image data of the target mining area and registering the SAR image data; constructing a small baseline set interferometric network based on the registered SAR image data and processing the small baseline set interferometric network to generate an unwrapped phase map; and substituting the unwrapped phase data in the unwrapped phase map into the deformation model to solve for the S-shaped nonlinear deformation of the target mining area.

[0006] Preferably, the function model is: ; in, Let t be the cumulative subsidence of the mining area at time t. Indicates the maximum settlement. Indicates the time when the maximum settlement velocity occurs. This is the settling rate coefficient.

[0007] Preferably, the deformation model is:

[0008] ; in, Indicates the unwrapping phase of the i-th interferogram; , , respectively, represent the acquisition times of the main image and the auxiliary image corresponding to the i-th interferogram. This is the radar wavelength.

[0009] Preferably, the registration of the synthetic aperture radar image data specifically includes: acquiring digital elevation model data of the target mining area; extracting the complex values ​​of a single view from the synthetic aperture radar image data; selecting one image from the synthetic aperture radar image data as the main image, and using the complex values ​​of a single view of the main image as a reference, registering and cropping the complex values ​​of the remaining images using the digital elevation model data.

[0010] Preferably, the processing of the small baseline set interferometric network to generate an unwrapped phase map specifically includes: based on the initial baseline, performing differential interferometry, filtering, and phase unwrapping sequentially on the interferometric pairs in the small baseline set interferometric network to obtain initial phase unwrapped information; calculating and removing orbital errors and atmospheric delay phases based on the initial phase unwrapped information to eliminate non-deformation signal interference in the initial phase unwrapped information; calculating and removing elevation residuals based on the phase unwrapped information obtained after removing orbital errors and atmospheric delay phases to correct the digital elevation model; and re-performing differential interferometry, filtering, and phase unwrapping sequentially on the interferometric pairs in the small baseline set interferometric network based on the corrected digital elevation model to obtain unwrapped phases and generate the unwrapped phase map.

[0011] Preferably, solving the deformation model specifically includes: encoding the maximum settlement amount, the occurrence time of the maximum settlement velocity, and the settlement rate coefficient to be determined as chromosomes of a genetic algorithm; setting the search range of each parameter based on prior data of the mining area; and using the selection operator, crossover operator, and mutation operator of the genetic algorithm to perform iterative search to obtain the optimal parameter combination of the maximum settlement amount, the occurrence time of the maximum settlement velocity, and the settlement rate coefficient.

[0012] Preferably, the stopping condition for the iterative search is: reaching the maximum number of iterations, or the residual between the simulated phase corresponding to the current maximum settlement amount, the occurrence time of the maximum settlement velocity, and the settlement rate coefficient, and the unwrapped phase data in the unwrapped phase diagram is less than a set threshold.

[0013] Preferably, the method further includes: reconstructing and plotting the time-series subsidence field of the target mining area based on the parameter combination of the obtained maximum subsidence amount, the occurrence time of the maximum subsidence velocity, and the subsidence rate coefficient, combined with the time variable t.

[0014] A second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the nonlinear deformation monitoring method for mining areas as described above.

[0015] A third aspect of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the nonlinear deformation monitoring method for mining areas as described above.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a method, equipment, and medium for monitoring nonlinear deformation in mining areas. Based on the Boltzmann time function, a functional model characterizing the change of deformation in mining areas over time is established. The deformation is an S-shaped nonlinear deformation, including the maximum settlement amount, the occurrence time of the maximum settlement velocity, and the settlement rate coefficient. By introducing the Boltzmann function as a constraint for monitoring, the parameters directly correspond to the physical process of settlement in the mining area. Compared with traditional methods, the single-point monitoring results obtained by this invention can more accurately describe the nonlinear settlement process in the mining area, with lower monitoring errors, and significantly improve the accuracy and reliability of nonlinear settlement monitoring in mining areas. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 The flowchart of the nonlinear deformation monitoring method for mining areas provided by the present invention is shown.

[0019] Figure 2 The dynamic evolution process of the nonlinear subsidence field in the mining area is plotted using this method.

[0020] Figure 3 A comparison chart of the time-series settlement curves of representative monitoring points plotted using this method with those of traditional methods and measured leveling data.

[0021] Figure 4A comparison diagram of the dynamic evolution process of the strike profile of the mining area plotted using this method and the results of the traditional method is shown. The two views from left to right in the figure correspond to the traditional method and this method, respectively.

[0022] Figure 5 A schematic diagram of the electronic device provided by the present invention. Detailed Implementation

[0023] In view of the shortcomings of the prior art, the inventors of this invention, through long-term research and extensive practice, have proposed the technical solution of this invention. The following will further explain and illustrate this technical solution, its implementation process, and its principles.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0025] Furthermore, in the description of this invention, it should be understood that the terms "upper," "lower," "inner," "outer," "horizontal," "vertical," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0026] In the description of this specification, the references to terms such as "an embodiment," "a particular embodiment," or "the embodiment" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0027] Figure 1 A flowchart illustrating the nonlinear deformation monitoring method for mining areas provided by this invention. Please refer to [link / reference]. Figure 1 The nonlinear deformation monitoring method in the mining area includes the following operations S100-S500.

[0028] Operation S100 establishes a functional model of the deformation of the mining area over time based on the Boltzmann time function; the deformation is an S-shaped nonlinear deformation, including the maximum settlement, the time of occurrence of the maximum settlement velocity, and the settlement rate coefficient.

[0029] Using the S200 and joint function model, a deformation model is established to characterize the mapping relationship between the unwrapped phase and the deformation of the mining area in the small baseline interferogram. The deformation model is a nonlinear deformation observation model.

[0030] Operate S300 to acquire synthetic aperture radar image data of the target mining area and register the synthetic aperture radar image data.

[0031] The S400 is operated to construct a small baseline set interferometric network based on the registered synthetic aperture radar image data, and the small baseline set interferometric network is processed to generate an unwrapped phase map.

[0032] By operating S500, the unwound phase data in the unwound phase diagram is substituted into the deformation model to obtain the S-shaped nonlinear deformation of the target mining area.

[0033] This invention addresses the limitations of traditional small baseline set techniques with linear assumptions in mining deformation surveys by proposing a nonlinear deformation monitoring method for mining areas. Based on the Boltzmann time function, a functional model characterizing the time-varying deformation of the mining area is established to describe the S-shaped nonlinear deformation, thereby eliminating monitoring bias caused by the linear model. This enables accurate identification of the nonlinear settlement process in the mining area and precise mapping of the single-point settlement evolution process, improving the accuracy and reliability of nonlinear settlement monitoring in mining areas.

[0034] Preferably, the functional model representing the change of the deformation of the mining area over time established in operation S100 is as follows:

[0035] ;

[0036] in, Let t be the cumulative subsidence of the mining area at time t. This indicates the maximum settlement. Indicates the time when the maximum settlement velocity occurs. This represents the settling rate coefficient. It is related to speed but is not equal to the speed value.

[0037] In this invention, the parameters in the Boltzmann time function have clear geological and mining implications. This represents the maximum settlement at a given pixel when the settlement reaches a stable level. The parameters obtained from the solution... The distribution of these parameters directly reflects the morphology of subsidence basins caused by underground coal mining and also indicates the extent of their impact on the surface. This represents the time corresponding to the inflection point of the settlement curve. The parameters obtained from the solution... The spatial distribution directly reflects the time when the maximum settlement velocity occurs at a particular pixel. Parameters The smaller the value, the greater the sinking speed at that pixel. Parameter The value gradually increases from the center of the subsidence basin towards the edge, indicating that the subsidence rate gradually slows down from the inside of the basin towards the edge.

[0038] This invention utilizes a model based on the Boltzmann time function as a constraint to directly invert the parameters of the Boltzmann time function, and obtains the settlement process at any point through the inverted parameter distribution.

[0039] Preferably, the deformation model established in operation S200, which characterizes the mapping relationship between the unwrapped phase and the deformation of the mining area in the small baseline interferogram, is as follows:

[0040] ;

[0041] in, Indicates the unwrapping phase of the i-th interferogram; , , respectively, represent the acquisition times of the main image and the auxiliary image corresponding to the i-th interferogram. This refers to the radar wavelength.

[0042] The construction principle and performance analysis of the deformation model in this invention are as follows.

[0043] Assuming on the date Acquire N+1 synthetic aperture radar images covering the same mining area, register all images in the same coordinate system, and perform differential interferometry on the main and auxiliary image pairs that meet the spatiotemporal baseline threshold to obtain M small baseline interferograms. The i-th interferogram is then located at pixel [i]. Differential interference phase at the point It can be represented as:

[0044] ;

[0045] in, , These are the elements of the i-th interferometric image. Place , The untangling phase at any given moment, , These represent the line-of-sight deformation at the corresponding time points.

[0046] The mean surface deformation rate after removing topographic residual phase, atmospheric delay phase, and various noise phases. It can be represented as:

[0047] ;

[0048] Then we have:

[0049] ;

[0050] in, , These are the acquisition times of the main image and the secondary image, respectively; For interval The average subsidence rate along the radar line of sight over a given time period. Therefore, the definition is... The matrix equation is obtained as follows:

[0051] ;

[0052] Traditional satellite-based augmentation system (SBAS) methods (such as least squares and singular value decomposition) essentially reconstruct temporal subsidence by solving for and summing the average subsidence rates between adjacent images. These traditional methods are based on the assumption that surface deformation is linear between adjacent observation intervals, and therefore cannot accurately monitor the nonlinear dynamic evolution of subsidence in mining areas.

[0053] This invention addresses the S-shaped nonlinear evolution characteristics of surface subsidence in mining areas. In the traditional SBAS deformation field monitoring process, it introduces the Boltzmann time function as a physical constraint to establish a functional mapping relationship between observed phase data and nonlinear surface subsidence.

[0054] ;

[0055] Pixel The unwrapping phase of the i-th interferogram at point i is denoted as . The imaging dates of the main image and the auxiliary image that make up the i-th interferogram are denoted as follows: , The settlement at the corresponding time can be expressed in Boltzmann function form. Based on the mapping relationship established according to this invention, this observation phase... It is directly determined by the undetermined deformation field parameters, and its mathematical expression is:

[0056] ;

[0057] in, , , These represent the maximum settlement, the time of occurrence of the maximum settlement velocity (i.e., the time corresponding to the inflection point), and the settlement rate coefficient (i.e., the shape of the settlement curve) at that pixel, respectively. By combining the observation data of all interferometric pairs, a phase-deformation field mapping matrix covering the entire time series can be established.

[0058] Preferably, operation S300 specifically includes the following sub-operations S310-S330.

[0059] In sub-operation S310, synthetic aperture radar image data and digital elevation model data of the target mining area are acquired.

[0060] For example, 35 VV polarimetric Sentinel-1A up-orbit images covering the mining area were selected as the data source, with an interval of approximately 12 days. Simultaneously, digital elevation model data covering the mining area were collected for terrain phase removal.

[0061] In sub-operation S320, single-view complex extraction is performed on the synthetic aperture radar image data. Furthermore, multi-view processing is conducted to suppress speckle noise.

[0062] Single-view complex data is raw complex imagery directly acquired by synthetic aperture radar sensors. It contains intensity and phase information and is the fundamental data for achieving high-precision monitoring of nonlinear deformation in mining areas.

[0063] Preferably, the method further includes: interpolating the missing areas of the digital elevation model data, and the digital elevation model data in subsequent operations is the interpolated digital elevation model data.

[0064] In sub-operation S330, one image is selected from the synthetic aperture radar imagery data as the master image. Using the single-view complex data of the master image as a reference, the single-view complex data of the remaining images are registered and cropped using digital elevation model data. This establishes a unified radar coordinate system, providing a standardized data foundation for subsequent deformation calculations.

[0065] Preferably, operation S400 specifically includes the following sub-operations S410-S450, which perform the operation of processing the small baseline set interferometric network to generate an unwrapped phase map.

[0066] In suboperation S410, a small baseline set interferometric network is constructed based on the registered synthetic aperture radar image data.

[0067] Specifically, multi-view processing is performed using the pixel ratio of the azimuth and range directions. At the same time, a spatiotemporal baseline threshold is set, and the registered synthetic aperture radar image data are combined in pairs to construct a small baseline set interferometric network.

[0068] In suboperation S420, based on the initial baseline, differential interference, filtering, and phase unwrapping are sequentially performed on the interference pairs in the small baseline set interferometric network to obtain the initial phase unwrapping information.

[0069] Specifically, differential interferometry is performed based on the combination of interferometer pairs in the small baseline network to generate an initial differential interferogram. The interferometric phase in the initial differential interferogram is calculated based on the initial baseline and the external digital elevation model. Subsequently, a Goldstein filter is used to filter the interferogram to suppress noise. Based on this, phase unwrapping is performed; for example, a coherence threshold of 0.3 is set to mask low-coherence regions, and a minimum cost flow algorithm is used to obtain the accurate initial unwrapped phase.

[0070] In sub-operation S430, orbital errors and atmospheric delay phases are calculated and removed based on the initial phase unwrapping information to eliminate non-deformation signal interference in the initial phase unwrapping information and obtain high-precision surface deformation phase information.

[0071] Specifically, using the initial phase unwrapping information, the orbital error is estimated and removed using a polynomial fitting method; at the same time, the atmospheric delay phase is estimated and separated by spatiotemporal filtering; finally, the above error components are subtracted from the unwrapped phase to obtain a high-precision surface deformation phase.

[0072] In suboperation S440, the elevation residual is calculated and removed based on the phase unwrapping information obtained after removing orbital errors and atmospheric delay phase, in order to correct the digital elevation model. This ultimately yields clean observational phase data free from various noise interferences.

[0073] In suboperation S450, based on the corrected digital elevation model, the interferometric pairs in the small baseline set interferometric network are subjected to differential interferometry, filtering, and phase unwrapping in sequence to obtain the unwrapped phase and generate the unwrapped phase map.

[0074] Furthermore, the phase of the final noise-removed interferometric pair is geocoded, transformed from the synthetic aperture radar imaging coordinate system to the WGS84 geographic coordinate system, and output as raster format data. This data is then used as input data to perform calculations in the MATLAB environment, and the time-series deformation field distribution is plotted.

[0075] Preferably, the solution of the deformation model in operation S500 specifically includes the following sub-operations S510-S530.

[0076] In suboperation S510, the maximum settlement amount to be determined, the occurrence time of the maximum settlement velocity, and the settlement rate coefficient are encoded as chromosomes of the genetic algorithm.

[0077] In sub-operation S520, the search range of each parameter is set according to the prior data of the mining area.

[0078] In suboperation S530, the selection operator, crossover operator and mutation operator of the genetic algorithm are used to perform iterative search to obtain the optimal combination of maximum settlement, maximum settlement velocity occurrence time and settlement rate coefficient.

[0079] Genetic algorithms have a built-in fitness function. The goal is to find a set of parameters A, b, and c, and through iterative searching, eliminate parameter combinations with large errors, continuously moving closer to the optimal solution to minimize the residual between the simulated phase and the actual observed phase. The objective function (i.e., the basis for the iterative search) can be expressed as minimizing the L2 norm:

[0080] ;

[0081] in, For unwrapped phase data in the unwrapped phase diagram, M represents the simulated phase corresponding to the current maximum settlement, the time of occurrence of the maximum settlement velocity, and the settlement rate coefficient obtained from the current search, and M is the number of interferograms.

[0082] Preferably, the stopping condition for iterative search is: reaching the maximum number of iterations, or the residual between the simulated phase corresponding to the current maximum settlement amount, the occurrence time of the maximum settlement velocity, and the settlement rate coefficient and the unwound phase data in the unwound phase diagram is less than a set threshold.

[0083] Preferably, after operating S500, the method further includes: reconstructing and mapping the temporal subsidence field of the mining area based on the solved maximum subsidence, maximum subsidence velocity occurrence time, and subsidence rate coefficient, combined with the time variable t. Specifically, using the determined deformation field parameters and the time variable, the line-of-sight deformation (maximum subsidence, maximum subsidence velocity occurrence time, and subsidence rate coefficient) at any time during the monitoring period is calculated and generated, and the nonlinear subsidence time series map and dynamic evolution process map of the mining area are output.

[0084] To facilitate comparison of the effectiveness of the proposed method in nonlinear settlement monitoring, this invention uses measured leveling data as a reference and compares the effectiveness of the three methods: singular value decomposition, least squares method, and the proposed method.

[0085] Please see Figure 2 The figure shows the dynamic evolution process of the mining area subsidence field obtained by this method. As can be seen from the figure, the mining area subsidence evolution process monitored by this method is consistent with the mining direction.

[0086] Please see Figure 3 The diagram shows a comparison between the time-series settlement curves of representative monitoring points plotted using this method and those plotted using traditional methods and measured leveling data. The two views from left to right are the time-series settlement comparison curves at monitoring point one and monitoring point two, respectively. Figure 3 The paper visually demonstrates the significant differences in the mapping effects of the three methods: the traditional least squares method exhibits severe sawtooth fluctuations and has the worst graphic effect; the singular value decomposition method shows obvious graphic lag and slope distortion during the settlement acceleration stage; the method of this invention is constrained by the physical model, and the drawn curves are smooth and accurately fit the measured leveling data.

[0087] Please see Figure 4This paper illustrates a comparison between the dynamic evolution of the strike profile of the mining area plotted using this method and the results of traditional methods. The comparison shows that the evolution law of the profile plotted by this method, the magnitude of subsidence, and the evolution process of the basin center are highly consistent with those of traditional methods, verifying that this method has reliable dynamic mapping capabilities while maintaining long-term spatial reconstruction capabilities.

[0088] To evaluate the accuracy of this invention in monitoring nonlinear deformation in mining areas, monitoring errors at two representative feature points were plotted. At monitoring points one and two, the root mean square error of this method improved by 81.46% and 41.99% respectively compared to the traditional least squares method; compared to singular value decomposition, the accuracy also improved by 25.64% and 19.92% respectively.

[0089] This invention provides a nonlinear deformation monitoring method for mining areas. It introduces a Boltzmann time function as a monitoring constraint, with parameters directly corresponding to the physical process of mining area subsidence. Parameter A reflects the final subsidence amount, b characterizes the time of maximum subsidence velocity, and c is the subsidence rate coefficient, establishing a direct functional mapping relationship between observed phase data and nonlinear surface subsidence. Parameters are determined through a global optimization strategy, thereby reconstructing and mapping the temporal evolution of mining area subsidence. Compared to traditional methods that only output abstract numerical values, the results of this invention have clear geological and mining implications, improving the interpretability of monitoring results. Compared to traditional methods (such as singular value decomposition and least squares methods), the single-point monitoring results obtained by this invention can more accurately describe the nonlinear subsidence process of the mining area, with lower monitoring errors, significantly improving the accuracy and reliability of nonlinear subsidence monitoring in mining areas. Based on the determined deformation field parameter distribution, the subsidence field at any time within the image coverage period can be reconstructed and mapped, achieving high spatiotemporal resolution continuous dynamic monitoring and mapping of surface deformation in mining areas. Experimental results show that the method of this invention effectively eliminates the mapping bias caused by traditional linear models and significantly improves the accuracy of nonlinear deformation surveys. The root mean square error at representative monitoring points in the experimental mining area was reduced to 6.605 mm and 16.292 mm, respectively, achieving accurate identification and dynamic mapping of the surface subsidence process in the mining area.

[0090] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the nonlinear deformation monitoring method for mining areas described in any of the above embodiments.

[0091] Figure 5This illustration shows a more specific hardware structure diagram of an electronic device provided in this embodiment. The device may include: a processor 410, a memory 420, an input / output interface 430, a communication interface 440, and a bus 450. The processor 410, memory 420, input / output interface 430, and communication interface 440 are interconnected internally via the bus 450.

[0092] The processor 410 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0093] The memory 420 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 420 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 420 and is called and executed by the processor 410.

[0094] Input / output interface 430 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0095] The communication interface 440 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0096] Bus 450 includes a pathway for transmitting information between various components of the device, such as processor 410, memory 420, input / output interface 430, and communication interface 440.

[0097] It should be noted that although the above-described device only shows the processor 410, memory 420, input / output interface 430, communication interface 440, and bus 450, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0098] The electronic devices described above are used to implement the corresponding nonlinear deformation monitoring method for mining areas in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0099] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the nonlinear deformation monitoring method for mining areas as described in any of the above embodiments.

[0100] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, 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 magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0101] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the nonlinear deformation monitoring method for mining areas as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0102] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of the invention as described above, which are not provided in the details for the sake of brevity.

[0103] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of the invention, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of the invention, and this also takes into account the fact that the details of implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of the invention will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of the invention, it will be apparent to those skilled in the art that the embodiments of the invention may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0104] Although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0105] It should be understood that the above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for solving nonlinear deformation in mining areas based on function morphology, characterized in that, include: A functional model is established based on the Boltzmann time function to characterize the changes of the parameters to be estimated in the mining area over time. The deformation is an S-type nonlinear deformation. The parameters to be estimated include the maximum settlement, the time of occurrence of the maximum settlement velocity, and the settlement rate coefficient, which characterize the temporal evolution of the target deformation field. By combining the aforementioned function model, a deformation model is established to characterize the mapping relationship between the unwrapped phase observation values ​​and the parameters to be estimated in the small baseline interferogram of the mining area. The deformation model is a nonlinear deformation observation model. Acquire synthetic aperture radar image data of the target mining area and register the synthetic aperture radar image data; A small baseline set interferometric network is constructed based on the registered synthetic aperture radar image data, and the small baseline set interferometric network is processed to generate an unwrapped phase map; Substituting the unwrapped phase data from the unwrapped phase map into the deformation model, the parameters to be estimated for the corresponding function model of the target mining area are obtained by synchronous inversion in one go. The S-shaped nonlinear deformation parameter distribution obtained by solving the model yields the settlement process at any point. Among them, the distribution of the maximum settlement amount obtained by solving the model reflects the morphology of the settlement basin affected by underground coal mining, the spatial distribution of the occurrence time of the maximum settlement velocity obtained by solving the model reflects the time of occurrence of the maximum settlement velocity at a certain pixel, and the value of the settlement rate coefficient obtained by solving the model gradually increases from the center of the settlement basin to the edge, indicating that the settlement velocity gradually slows down from the inside of the basin to the edge. Specifically, processing the small baseline set interferometric network to generate an unwrapped phase map includes: Based on the initial baseline, differential interference, filtering, and phase unwrapping are sequentially performed on the interference pairs in the small baseline set interferometric network to obtain the initial phase unwrapping information; Based on the initial phase unwrapping information, the orbital error and atmospheric delay phase are calculated and removed to eliminate non-deformation signal interference in the initial phase unwrapping information; Based on the phase unwrapping information obtained after removing orbital errors and atmospheric delay phase, the elevation residual is calculated and removed to correct the digital elevation model of the target mining area. Based on the corrected digital elevation model, differential interferometry, filtering, and phase unwrapping are performed sequentially on the interferometric pairs in the small baseline set interferometric network to obtain the unwrapped phase, thereby generating the unwrapped phase map; The function model is as follows: ； in, Let t be the cumulative subsidence of the mining area at time t. This indicates the maximum settlement. Indicates the time when the maximum settlement velocity occurs. Indicates the settling rate coefficient; The deformation model is: ； in, Indicates the unwrapping phase of the i-th interferogram; , , respectively, represent the acquisition times of the main image and the auxiliary image corresponding to the i-th interferogram. The radar wavelength; The method further includes: reconstructing and mapping the time-series subsidence field of the target mining area based on the obtained maximum subsidence, the occurrence time of maximum subsidence velocity, and the subsidence rate coefficient, combined with the time variable t.

2. The nonlinear deformation solution method for mining areas based on function morphology driving according to claim 1, characterized in that, Registration of the synthetic aperture radar image data specifically includes: Obtain digital elevation model data of the target mining area; Single-view complex number extraction is performed on the synthetic aperture radar image data; One image is selected from the synthetic aperture radar image data as the main image. Based on the single-view complex number of the main image, the single-view complex numbers of the remaining images are registered and cropped using the digital elevation model data.

3. The nonlinear deformation solution method for mining areas based on function morphology driving according to claim 1, characterized in that, Solving the deformation model specifically includes: The maximum settlement amount to be determined, the occurrence time of the maximum settlement velocity, and the settlement rate coefficient are respectively encoded into chromosomes of the genetic algorithm; The search range for each parameter is set based on prior data from the mining area; The optimal combination of parameters, including the maximum settlement, the occurrence time of the maximum settlement velocity, and the settlement rate coefficient, is obtained by iteratively searching using selection, crossover, and mutation operators of a genetic algorithm.

4. The nonlinear deformation solution method for mining areas based on function morphology driving according to claim 3, characterized in that, The stopping condition for the iterative search is: reaching the maximum number of iterations, or the residual between the simulated phase corresponding to the maximum settlement amount, the occurrence time of the maximum settlement velocity, and the settlement rate coefficient obtained in the current search and the unwound phase data in the unwound phase diagram is less than a set threshold.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the nonlinear deformation solution method for mining areas based on function morphology as described in any one of claims 1-4.

6. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the nonlinear deformation solution method for mining areas based on function morphology as described in any one of claims 1-4.

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