Coal mining monitoring method and device, electronic equipment and storage medium

Abstract

The invention discloses a coal mining monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of mining. The method comprises the steps of obtaining deformation data of a to-be-monitored mining area according to a preset deformation extraction algorithm and corrected multiple different-waveband synthetic aperture radar (SAR) data of the to-be-monitored mining area, and obtaining multiple goaf parameters corresponding to the to-be-monitored mining area in combination with a preset inversion algorithm, and establishing a goaf three-dimensional space model corresponding to the to-be-monitored mining area for monitoring the to-be-monitored mining area. According to the method, SAR data of different wavebands are processed and deformation data of a to-be-monitored mining area is extracted, so that a plurality of goaf parameters corresponding to the to-be-monitored mining area are obtained through a preset inversion algorithm and deformation data calculation, a goaf three-dimensional space model is established, and the to-be-monitored mining area is monitored; therefore, the accuracy and efficiency of collection and acquisition of the surface deformation condition and analysis and judgment of the super-layer boundary crossing mining behavior in the coal mining monitoring process are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mining, in particular to a coal mining monitoring method and device, an electronic device and a storage medium. BACKGROUND

[0002] In the process of coal mining, if overlayer cross-border mining behavior occurs, not only will it lead to waste of mineral resources, but also will easily cause major safety accidents such as surface subsidence, landslides, gas explosions, etc. Therefore, monitoring in the process of coal mining to prevent overlayer cross-border mining behavior is an important work in the process of coal mining.

[0003] In the current coal mining monitoring scheme, InSAR (Interferometric Synthetic Aperture Radar) technology has been gradually used to replace traditional manual patrol, microseismic monitoring or optical remote sensing technology which has small patrol range, long cycle, low precision and is seriously affected by weather.

[0004] However, although InSAR technology has improved the problems of small patrol range, long cycle, low precision and serious weather interference to some extent compared with traditional manual patrol, microseismic monitoring or optical remote sensing technology, due to the particularity and complexity of the topography of the mining area, there are still problems such as loss of coherence caused by vegetation coverage, atmospheric delay phase interference, and difficulty in phase unwrapping under large deformation gradient when using InSAR technology for coal mining monitoring. Ultimately, using the current coal mining monitoring scheme, the collection and acquisition of surface deformation and the analysis and judgment accuracy of overlayer cross-border mining behavior are poor and the efficiency is low. SUMMARY

[0005] The main purpose of the present application is to provide a coal mining monitoring method, device, electronic device and storage medium, which aims to improve the collection and acquisition of surface deformation and the analysis and judgment accuracy and efficiency of overlayer cross-border mining behavior in the process of coal mining monitoring.

[0006] In a first aspect, the present application provides a coal mining monitoring method, comprising: obtaining a plurality of synthetic aperture radar (SAR) data of a to-be-monitored mining area collected by a plurality of collection devices, wherein the plurality of SAR data includes SAR data of different wave bands; correcting and processing the SAR data to obtain processed SAR data; obtaining deformation data of the to-be-monitored mining area according to a preset deformation extraction algorithm and the processed SAR data; calculating a plurality of goaf parameters corresponding to the to-be-monitored mining area according to a preset inversion algorithm and the deformation data; According to the mined-out area parameters and the subsidence center coordinates of the to-be-monitored mining area, a three-dimensional space model of the mined-out area corresponding to the to-be-monitored mining area is established, and the three-dimensional space model of the mined-out area is used to monitor the to-be-monitored mining area.

[0007] In an optional implementation, the correction processing of the SAR data to obtain the processed SAR data includes: The SAR data is geometrically corrected by using a geometric correction algorithm with a preset resolution to obtain the geometrically corrected SAR data.

[0008] In an optional implementation, the preset deformation extraction algorithm includes an interferometric phase combination algorithm, a differential interferometric processing algorithm, a time-series deformation inversion algorithm, and a cumulative deformation algorithm. The deformation data of the to-be-monitored mining area is obtained according to the preset deformation extraction algorithm and the processed SAR data, including: According to the interferometric phase combination algorithm and the processed SAR data, a differential interferogram sequence is obtained. The differential interferogram sequence is processed by using the differential interferometric processing algorithm to obtain the processed differential interferogram sequence. According to the time-series deformation inversion algorithm and the processed differential interferogram sequence, a deformation rate of the to-be-monitored mining area is calculated and obtained. According to the cumulative deformation algorithm and the deformation rate of the to-be-monitored mining area, a cumulative deformation value of the to-be-monitored mining area is calculated and obtained.

[0009] In an optional implementation, the preset deformation extraction algorithm further includes a filtering algorithm, a phase unwrapping algorithm, and an atmospheric delay correction algorithm. After the differential interferogram sequence is processed by using the differential interferometric processing algorithm to obtain the processed differential interferogram sequence, the method further includes: The processed differential interferogram sequence is filtered, phase unwrapped, and atmospheric delay phase removed by using the filtering algorithm, the phase unwrapping algorithm, and the atmospheric delay correction algorithm, respectively.

[0010] In an optional implementation, the multiple mined-out area parameters corresponding to the to-be-monitored mining area are calculated and obtained according to the preset inversion algorithm and the deformation data, including: Multiple high-coherence points of the edges of the subsidence area of the to-be-monitored mining area are obtained by using a preset deformation point screening algorithm, and the search range and the working parameters of the preset inversion algorithm are determined according to the high-coherence points. An initial particle swarm is generated by using the preset inversion algorithm with the determined search range and working parameters, and the initial particle swarm includes the model parameters corresponding to the preset inversion algorithm. iterating at least once according to the initial particle swarm and the preset inversion algorithm until an iteration result meets a preset termination condition, to obtain an iterated particle swarm, the iterated particle swarm including: an iterated model parameter; obtaining a plurality of goaf parameters according to the iterated particle swarm.

[0011] In an optional implementation, the iterating at least once according to the initial particle swarm and the preset inversion algorithm until the iteration result meets the preset termination condition, to obtain the iterated particle swarm includes: calculating and obtaining a fitness corresponding to the particle swarm according to the iterated model parameter, the deformation data and a preset particle fitness evaluation algorithm; when the fitness meets the preset termination condition, or the number of iterations meets the preset termination condition, terminating the iteration, and obtaining the iterated particle swarm.

[0012] In an optional implementation, after the establishing the goaf three-dimensional space model corresponding to the to-be-monitored mining area according to the goaf parameter and the subsidence center coordinate of the to-be-monitored mining area, the method further includes: establishing a mining right area three-dimensional space model according to mining right data; obtaining a goaf planar projection, a goaf boundary, a mining right area planar projection and a mining right area boundary of the to-be-monitored mining area according to the goaf three-dimensional space model and the mining right area three-dimensional space model respectively; calculating and obtaining the coal mining monitoring result according to a preset out-of-boundary judgment algorithm, the goaf planar projection, the goaf boundary, the mining right area planar projection and the mining right area boundary.

[0013] In a second aspect, the present application provides a coal mining monitoring device, including: an obtaining module, configured to obtain a plurality of synthetic aperture radar (SAR) data of a to-be-monitored mining area collected by a plurality of collection devices, the plurality of SAR data including SAR data of different wave bands; a correction module, configured to obtain a foreground image corresponding to the two-dimensional image by using a preset image processing model; a deformation extraction module, configured to obtain deformation data of the to-be-monitored mining area according to a preset deformation extraction algorithm and the processed SAR data; an inversion module, configured to calculate and obtain a plurality of goaf parameters corresponding to the to-be-monitored mining area according to a preset inversion algorithm and the deformation data; a monitoring module, configured to establish a goaf three-dimensional space model corresponding to the to-be-monitored mining area according to the goaf parameter and a subsidence center coordinate of the to-be-monitored mining area, the goaf three-dimensional space model being used for monitoring the to-be-monitored mining area.

[0014] In a third aspect, the present application provides an electronic device, comprising a processor, a storage medium and a bus, the storage medium storing machine readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is running, the processor executing the machine readable instructions to perform the steps of the method of any preceding embodiment.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium storing a computer program, the computer program being executed by a processor to perform the steps of the method of any preceding embodiment.

[0016] The present application has the following beneficial effects: In the coal mining monitoring method provided by the embodiment of the present application, the following steps are included: obtaining a plurality of synthetic aperture radar (SAR) data of a to-be-monitored mining area collected by a plurality of collection devices, wherein the plurality of SAR data includes SAR data of different wave bands; performing correction processing on the SAR data to obtain processed SAR data; obtaining deformation data of the to-be-monitored mining area according to a preset deformation extraction algorithm and the processed SAR data; calculating a plurality of goaf parameters corresponding to the to-be-monitored mining area according to a preset inversion algorithm and the deformation data; and establishing a goaf three-dimensional space model corresponding to the to-be-monitored mining area according to the goaf parameters and the coordinates of a subsidence center of the to-be-monitored mining area, wherein the goaf three-dimensional space model is used for monitoring the to-be-monitored mining area. In this embodiment, a plurality of SAR data of different wave bands of the to-be-monitored mining area are collected by a plurality of collection devices, and after correction processing is performed on the SAR data, deformation data of the to-be-monitored mining area is extracted from the processed SAR data by using a preset deformation extraction algorithm. The plurality of goaf parameters corresponding to the to-be-monitored mining area are calculated by using a preset inversion algorithm and the deformation data of the to-be-monitored mining area. The goaf three-dimensional space model of the to-be-monitored mining area is established based on the goaf parameters and the coordinates of the subsidence center of the to-be-monitored mining area. Finally, the to-be-monitored mining area is monitored by using the goaf three-dimensional space model. Therefore, the collection and acquisition of the ground deformation in the coal mining monitoring process and the analysis and judgment accuracy and efficiency of the overlayer out-of-bound mining behavior are improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from the structures shown in the drawings without creative labor.

[0018] Figure 1A flowchart of a coal mining monitoring method according to an embodiment of the present application is provided; Figure 2 A schematic diagram of a SAR data acquisition scene according to an embodiment of the present application is provided; Figure 3 A flowchart of a coal mining monitoring method according to another embodiment of the present application is provided; Figure 4 A flowchart of a coal mining monitoring method according to yet another embodiment of the present application is provided; Figure 5 A flowchart of a coal mining monitoring method according to still another embodiment of the present application is provided; Figure 6 A structural diagram of a coal mining monitoring device according to an embodiment of the present application is provided; Figure 7 A structural diagram of an electronic device according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings for the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0021] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, thus, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. The term “comprises”, “comprising” or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device. Without more limitations, the element defined by the statement “comprises a” does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0022] Some embodiments of the present application will be described in detail with reference to the drawings. The following embodiments and features of the embodiments described below can be combined with each other without conflict.

[0023] In order to prevent overlayer cross-border mining behavior, the traditional coal mining monitoring scheme usually monitors the coal mine through artificial patrol, microseismic monitoring or optical remote sensing technology. However, artificial patrol, microseismic monitoring or optical remote sensing technology has the problems of small patrol range, long cycle, low precision and serious weather interference, and cannot accurately and fully realize coal mining monitoring.

[0024] The InSAR technology is a monitoring technology with strong continuity and wide monitoring range, which is not affected by weather conditions such as clouds and rainfall. Compared with traditional coal mining monitoring schemes such as artificial patrol, microseismic monitoring or optical remote sensing technology, the InSAR technology has obvious advantages. However, in actual coal mining monitoring work, due to the particularity and complexity of the mine area topography, the use of InSAR technology for coal mining monitoring still has problems such as loss of coherence caused by vegetation coverage, atmospheric delay phase interference, and difficulty in phase unwrapping under large deformation gradient. Ultimately, the current coal mining monitoring scheme has the problems of low accuracy and low efficiency in collecting and acquiring ground deformation and analyzing and judging overlayer cross-border mining behavior.

[0025] In view of the above problems, the main purpose of the present application is to provide a coal mining monitoring method, which mainly aims to improve the accuracy and efficiency of collecting and acquiring ground deformation and analyzing and judging overlayer cross-border mining behavior in the process of coal mining monitoring.

[0026] Figure 1 The coal mining monitoring method flowchart provided by an embodiment of the present application, the execution subject of the method can be a computer or other device with computing processing capability, but is not limited thereto, as shown in Figure 1 The method comprises: S101, acquiring a plurality of synthetic aperture radar (SAR) data of a to-be-monitored mine area collected by a plurality of acquisition devices, wherein the plurality of SAR data includes SAR data of different wave bands.

[0027] For example, the aforementioned multiple acquisition devices may refer to multiple Earth observation satellites with different signal bands and revisit periods. For instance, they may include the Sentinel-1 Earth observation satellite with a C-band signal band and a revisit period of 12 days, and the Land Survey-1 satellite with an L-band signal band and a revisit period of 28 days. The SAR data of the aforementioned different bands may refer to SAR data from multiple Earth observation satellites with different signal bands and revisit periods. It is understood that the specific types and models of Earth observation satellites acquired by the aforementioned acquisition devices, as well as the specific signal bands and revisit periods of the aforementioned Earth observation satellites, can be adjusted and determined according to actual needs, and are not limited to the examples mentioned above.

[0028] Figure 2 This is a schematic diagram of a SAR data acquisition scenario provided in an embodiment of this application. Please refer to... Figure 2 The SAR data of the mining area to be monitored collected by the above-mentioned multiple acquisition devices can be specifically classified as follows: Figure 2 The scenario shown illustrates the collection of SAR data for the aforementioned mining area to be monitored. Figure 2 As can be seen, the SAR data mentioned above can include images such as interferograms, and can also acquire some data such as length, angle, and depth at the same time. However, the specific data acquired can be determined according to the actual situation and is not limited here.

[0029] S102. Correct the above SAR data to obtain the processed SAR data.

[0030] For example, the above-mentioned correction processing of SAR data to obtain the processed SAR data may refer to performing correction processing on the SAR data, including but not limited to geometric correction, to correct and eliminate the deformation caused by factors such as deformation of photographic materials, lens distortion, atmospheric refraction, Earth curvature, Earth rotation, and topographic relief, when the geometric position, shape, size, orientation, and other characteristics of various objects in the SAR data are inconsistent with the expression requirements in the reference system.

[0031] However, the above correction process is not limited to geometric correction, and the specific algorithms and tools used for SAR data correction can be selected and determined according to actual needs, without any restrictions.

[0032] S103. Based on the preset deformation extraction algorithm and the processed SAR data, obtain the deformation data of the above-mentioned mining area to be monitored.

[0033] Exemplarily, the preset deformation extraction algorithm can include one or more of an SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) algorithm, a D-InSAR (Differential Synthetic Aperture Radar Interferometry) algorithm, and the like, and it can be understood that the preset deformation extraction algorithm can also include other specific algorithms, and is not limited to the SBAS-InSAR algorithm and the D-InSAR algorithm.

[0034] The deformation data of the to-be-monitored mining area can be obtained according to the preset deformation extraction algorithm and the SAR data after processing, for example, the SAR data after correction processing can be taken as an input value, and the deformation data of the to-be-monitored mining area can be obtained after calculation by the preset deformation extraction algorithm including but not limited to the SBAS-InSAR algorithm and the D-InSAR algorithm. The deformation data can be used to represent the ground deformation caused by underground mining of the to-be-monitored mining area.

[0035] S104, a plurality of goaf parameters corresponding to the to-be-monitored mining area are calculated and obtained according to a preset inversion algorithm and the deformation data.

[0036] Exemplarily, the plurality of goaf parameters corresponding to the to-be-monitored mining area can be calculated and obtained according to the preset inversion algorithm and the deformation data, for example, the deformation data can be taken as an input value, and the plurality of goaf parameters corresponding to the to-be-monitored mining area can be calculated and obtained by the preset inversion algorithm. It is worth noting that in the process of calculating and obtaining the plurality of goaf parameters corresponding to the to-be-monitored mining area according to the preset inversion algorithm and the deformation data, other data in addition to the deformation data can be used to complete the calculation, for example, related algorithm model parameters in the preset inversion algorithm. However, the specific types and quantities of goaf parameters, the types and quantities of other data in addition to the deformation data, and the like can be adjusted and determined according to actual conditions, and are not limited herein.

[0037] S105, a goaf three-dimensional space model corresponding to the to-be-monitored mining area is established according to the goaf parameters and the subsidence center coordinates of the to-be-monitored mining area. The goaf three-dimensional space model is used to monitor the to-be-monitored mining area.

[0038] Exemplarily, the subsidence center coordinates of the to-be-monitored mining area can be obtained by one or more of algorithms including but not limited to the SBAS-InSAR algorithm and the D-InSAR algorithm. The subsidence center of the to-be-monitored mining area can correspond to the projection center of the underground goaf of the to-be-monitored mining area. After the goaf three-dimensional space model corresponding to the to-be-monitored mining area is established according to the goaf parameters and the subsidence center coordinates of the to-be-monitored mining area, the goaf three-dimensional space model can be used to represent the three-dimensional space situation of the goaf, thereby assisting in monitoring the to-be-monitored mining area.

[0039] In one possible application of the three-dimensional spatial model of the goaf, the model can also be used to compare with the mining rights data of the mining area to be monitored in order to determine whether there is any over-mining or cross-boundary mining in the current goaf of the mining area to be monitored (i.e., mining has been carried out beyond the permitted mining area defined by the aforementioned mining rights data). However, the specific method of comparing the three-dimensional spatial model of the goaf with the mining rights data of the mining area to be monitored can be selected and determined according to the actual situation, and no specific restrictions are imposed here.

[0040] The coal mine monitoring method provided in this application includes: acquiring multiple synthetic aperture radar (SAR) data of a mining area to be monitored from multiple acquisition devices, wherein the multiple SAR data include SAR data of different bands; performing correction processing on the SAR data to obtain processed SAR data; acquiring deformation data of the mining area to be monitored based on a preset deformation extraction algorithm and the processed SAR data; calculating multiple goaf parameters corresponding to the mining area to be monitored based on a preset inversion algorithm and the deformation data; and establishing a three-dimensional spatial model of the goaf corresponding to the mining area to be monitored based on the goaf parameters and the subsidence center coordinates of the mining area to be monitored. The three-dimensional spatial model of the goaf is used to monitor the mining area to be monitored. In this embodiment, multiple acquisition devices collect SAR data of different bands from the mining area to be monitored. After correcting these SAR data, a preset deformation extraction algorithm is used to extract the deformation data of the mining area to be monitored from these processed SAR data. This enables the calculation of multiple goaf parameters corresponding to the mining area to be monitored using a preset inversion algorithm and the deformation data of the mining area to be monitored. Based on these goaf parameters and the subsidence center coordinates of the mining area to be monitored, a three-dimensional spatial model of the goaf of the mining area to be monitored is established. Finally, the mining area to be monitored is monitored using this three-dimensional spatial model of the goaf, thereby improving the accuracy and efficiency of collecting and acquiring surface deformation data and analyzing and judging over-layer and over-boundary mining behavior during coal mining monitoring.

[0041] Optionally, in the above Figure 1 Based on the embodiments, the above-described correction processing of the SAR data to obtain the processed SAR data may include: The SAR data is geometrically corrected by a geometric correction algorithm with a preset resolution to obtain the geometrically corrected SAR data.

[0042] For example, the geometric correction algorithm with the preset resolution mentioned above may refer to a DEM (Digital Elevation Model) algorithm with a resolution of 12.5 meters, but it is not limited thereto. The preset resolution may also be 10 meters, 15 meters, etc., and the geometric correction algorithm may also be an algorithm other than the DEM algorithm.

[0043] The geometric correction algorithm with the preset resolution is used to correct the SAR data, so as to obtain the geometric corrected SAR data. For example, the geometric position, shape, size, orientation and other characteristics of each object in the SAR data can be corrected and eliminated when the expression requirements of the objects in the reference system are inconsistent due to the deformation of the photographic material, lens distortion, atmospheric refraction, earth curvature, earth rotation, terrain undulation and other factors, but not limited thereto.

[0044] Figure 3 The coal mining monitoring method flowchart provided by another embodiment of the application is shown in the following figure: Figure 3 On the basis of the above Figure 1 The preset deformation extraction algorithm can include an interference relative combination algorithm, a differential interference processing algorithm, a time sequence deformation inversion algorithm and a cumulative deformation algorithm.

[0045] The deformation data of the monitored mining area is obtained according to the preset deformation extraction algorithm and the processed SAR data, which can include: S301, the differential interference graph sequence is obtained according to the interference relative combination algorithm and the processed SAR data.

[0046] For example, the interference relative combination algorithm can be based on the SBAS-InSAR algorithm. The differential interference graph sequence is obtained according to the interference relative combination algorithm and the processed SAR data, for example, the SAR data is filtered and the differential interference graph sequence is generated by the SBAS-InSAR algorithm. The SAR data is filtered by the SBAS-InSAR algorithm, for example, the SAR data is filtered according to the preset time baseline threshold and spatial baseline threshold. For example, the time baseline threshold is ΔT < 120 days, and the spatial baseline threshold is <500 meters. On this basis, the differential interference graph sequence is obtained according to the interference relative combination algorithm and the processed SAR data, for example, all pairs of SAR data that meet the time baseline threshold ΔT < 120 days and the spatial baseline threshold <500 meters are combined and filtered to form the differential interference graph sequence.

[0047] It can be understood that the above content about obtaining the differential interferogram sequence according to the above interferometric relative combination algorithm and the processed SAR data is only one possible example, and the actual interferometric relative combination algorithm, the actual differential interferogram sequence acquisition method, and the like are not limited to the above example, and the time baseline threshold is set to ΔT < 120 days, which can prevent time decorrelation caused by too long time baseline and reduce the availability of the interferograms in the obtained differential interferogram sequence. The spatial baseline threshold is set to <500 meters, which can meet the interferogram coherence requirement of the C band while not limiting the number of L band interference pairs, but the specific time baseline threshold and spatial baseline threshold are not limited to the above range, which can effectively balance the interferogram coherence and the number of interference pairs and ensure the accuracy of obtaining the differential interferogram sequence.

[0048] S302, processing the differential interferogram sequence by the differential interferometric processing algorithm to obtain the processed differential interferogram sequence.

[0049] For example, the differential interferometric processing algorithm may, for example, refer to an algorithm based on the D-InSAR algorithm, and specifically, the differential interferometric processing algorithm may, for example, be represented by the following terrain phase removal formula:

[0050] wherein the above is the phase change caused by the terrain fluctuation, is the wavelength of the transmitted electromagnetic wave, is the spatial baseline value, is the slant range of the radar to the target, is the radar incidence angle, is the altitude of the observation point or the height relative to the reference surface, which may, for example, be included in the SAR data or be acquired synchronously when the SAR data is acquired, and the specific acquisition scenario may, for example, be similar to that shown in Figure 2 but is not limited thereto.

[0051] Of course, the terrain phase removal formula is only one possible example, the differential interferometric processing algorithm is not limited to the terrain phase removal formula, and the value acquisition method and representation form in the terrain phase removal formula may be adjusted and determined according to the actual situation, which is not limited herein.

[0052] S303, calculating the deformation rate of the monitored mining area according to the time series deformation inversion algorithm and the processed differential interferogram sequence.

[0053] Exemplarily, the time-series deformation inversion algorithm can be an algorithm based on the SBAS-InSAR algorithm, and a principle of calculating and obtaining the deformation rate of the monitored mining area according to the time-series deformation inversion algorithm and the processed differential interferogram sequence can be that a least square solution in a least norm sense is solved by using a singular value decomposition (SVD) method of a matrix. Specifically, the calculating and obtaining of the deformation rate of the monitored mining area can include the following steps: (1) An interference phase matrix A with a dimension of MxN is constructed, where M is a number of interferograms in SAR data, and N is a number of SAR images in the SAR data minus 1, that is, a number of time intervals between the SAR images, that is, each row in the interference phase matrix A corresponds to an interferogram, and each column in the interference phase matrix A corresponds to a time interval.

[0054] (2) Singular value decomposition is performed on the interference phase matrix A, and the following can be obtained:

[0055] wherein the above is a left singular matrix with a dimension of MxN, the above is a right singular matrix with a dimension of NxN, the above is a singular value diagonal matrix with a dimension of MxN, and the above T represents a transpose of a matrix.

[0056] (3) The deformation rate is solved by using a generalized inverse matrix, and the following can be obtained:

[0057] wherein the above is the deformation rate of the monitored mining area, the above is a pseudo-inverse matrix of the singular value matrix, and the above is an observed interference phase vector.

[0058] S304, according to the cumulative deformation algorithm and the deformation rate of the monitored mining area, a cumulative deformation value of the monitored mining area is calculated and obtained.

[0059] Exemplarily, the calculating and obtaining of the cumulative deformation value of the monitored mining area according to the cumulative deformation algorithm and the deformation rate of the monitored mining area can further include the following steps on the basis of the steps (1)-(3): (4) According to the solved deformation rate, the cumulative deformation is calculated, and the following can be obtained:

[0060] wherein the above is a pixel point accumulated LOS direction deformation, i.e. the accumulated deformation above, is a time baseline of the z-th interferogram, is a deformation rate of the z-th interferogram, which can be determined by the above v.

[0061] (5) Since the LOS direction deformation is synthesized by the three-dimensional deformation of the ground surface (east-west, north-south, and vertical), the accumulated deformation above can be expressed as:

[0062] wherein the above is the deformation in the vertical direction, , is the deformation in the horizontal direction, is the incidence angle of the satellite, is the heading angle, which can be included in the SAR data or obtained synchronously when the SAR data is collected, and the specific collection scenario can be similar to that shown in the Figure 2 , but is not limited thereto.

[0063] It can be understood that the above steps are only one possible example, and how to calculate and obtain the deformation rate of the monitored mining area and how to calculate and obtain the accumulated deformation value of the monitored mining area can be adjusted and determined according to actual conditions, and are not limited to the example contents in the above steps (1) - (5).

[0064] In addition, on the basis of the above Figure 2 embodiment, the deformation extraction algorithm further includes a filtering algorithm, a phase unwrapping algorithm, and an atmospheric delay correction algorithm. After the above differential interferogram sequence is processed by the differential interferometric processing algorithm, the processed differential interferogram sequence is obtained, and the method can further include: The filtered, phase-unwrapped, and atmospheric delay phase-removed processed differential interferogram sequence is filtered, phase-unwrapped, and atmospheric delay phase-removed by the filtering algorithm, the phase unwrapping algorithm, and the atmospheric delay correction algorithm, respectively.

[0065] For example, the filtering algorithm can be a Goldstein filtering algorithm, and filtering the processed differential interferogram sequence by the filtering algorithm can mean that the processed differential interferogram sequence is adaptively filtered by the Goldstein filtering algorithm to suppress noise in the differential interferogram sequence.

[0066] The phase unwrapping algorithm may be, for example, a minimum cost flow (MCF) algorithm. The phase unwrapping of the processed differential interferogram sequence by the phase unwrapping algorithm may refer to, for example, phase unwrapping of the processed differential interferogram sequence by the minimum cost flow (MCF) algorithm to restore the real phase distribution in the differential interferogram sequence.

[0067] The atmospheric delay correction algorithm may be, for example, a GACOS (Global Atmosphere Correction Service) algorithm. The atmospheric delay phase removal from the processed differential interferogram sequence by the atmospheric delay correction algorithm may refer to, for example, atmospheric delay removal from the processed differential interferogram sequence by the GACOS algorithm to eliminate the influence of atmospheric and illumination factors on the ground observation data in the differential interferogram sequence.

[0068] It can be understood that the above content is only an example. The actual filtering algorithm, phase unwrapping algorithm and atmospheric delay correction algorithm are not limited to the above example. Other suitable algorithms can also be selected for filtering, phase unwrapping and atmospheric delay phase removal of the processed differential interferogram sequence.

[0069] Figure 4 A coal mining monitoring method flowchart provided by another embodiment of the present application is shown in FIG. 6. Based on the foregoing Figure 3 embodiments, the method may further include: Figure 1 calculating a plurality of goaf parameters corresponding to the to-be-monitored mining area according to a preset inversion algorithm and the deformation data, which may include: S401: obtaining a high-coherence point of an edge of a subsidence area of the to-be-monitored mining area by a preset deformation point screening algorithm, and determining a search range and working parameters of the preset inversion algorithm according to the high-coherence point.

[0070] For example, the high-coherence point of the edge of the subsidence area of the to-be-monitored mining area may be obtained by a threshold of a coherence coefficient of an InSAR time series, an edge mask, a deformation gradient criterion, and points with a coherence greater than 0.5 on the edge of the subsidence area may be taken as the high-coherence point, but the present application is not limited thereto.

[0071] The preset inversion algorithm may for example include a genetic algorithm optimized particle swarm algorithm (GA-PSO), which may for example be used to quickly calculate and find optimal goaf parameters in a global range. The search range of the preset inversion algorithm may for example refer to the search range when the optimal goaf parameters are quickly calculated and found in the global range. The working parameters may for example include population size, maximum number of iterations, two learning factors, hybridization probability, mutation probability, maximum value of inertia factor, minimum value of inertia factor, maximum value vector and minimum value vector of particle transfer speed, etc. The specific determination method of the search range and working parameters of the preset inversion algorithm according to the high-coherence points may for example be calculation or presetting before using the model, and the specific method is not limited here.

[0072] S402, generate an initial particle swarm by a preset inversion algorithm that determines a search range and working parameters, wherein the initial particle swarm includes model parameters corresponding to the preset inversion algorithm.

[0073] The preset inversion algorithm may for example also include an algorithm constructed based on a tendency main section and a strike main section curve function equation. The tendency main section and the strike main section curve function equation may for example be expressed as the following formula: Further, according to the formula, the following can be obtained: wherein W0 and U0 are respectively surface subsidence and horizontal displacement, q and b are respectively subsidence coefficient and horizontal movement coefficient, and are respectively coal seam inclination, main influence angle and influence propagation angle, s1 and s2 are respectively inflection point offset distance of strike, p(y) is horizontal offset function of different position mining unit and its subsidence basin center, r(y) is influence radius function of different position mining unit, m is coal seam mining thickness, H and H1 are coal seam mining depth, L1 and L2 are respectively strike length of goaf and inclined length of goaf, is the strike calculation length, S is the horizontal distance of the mining center from the calculation origin, and the data are model parameters corresponding to the preset inversion algorithm, which may for example be collected or given an initial value according to actual conditions, so as to perform subsequent first iteration.

[0074] S403, perform at least one iteration according to the initial particle swarm and the preset inversion algorithm until the iteration result meets a preset termination condition, obtain an iterated particle swarm, and the iterated particle swarm includes iterated model parameters.

[0075] ​​​Exemplarily, the particle swarm after iteration includes: the model parameters after iteration, which can be updated in each iteration during the iteration process, but the specific updating process is not limited here.

[0076] The iteration result satisfies the preset termination condition, for example, the preset maximum iteration number is reached, or the parameter for evaluating the particle performance reaches a specific value, and the particle swarm after iteration is obtained. However, the specific preset termination condition is not limited here and can be adjusted adaptively according to actual conditions.

[0077] S404, obtaining a plurality of goaf parameters according to the particle swarm after iteration.

[0078] Exemplarily, the plurality of goaf parameters obtained according to the particle swarm after iteration can be selected from the particle swarm after the last iteration, and the particle with the minimum fitness is selected as the goaf parameter. Exemplarily, the goaf parameters can include seven parameters such as goaf strike length, goaf inclination length, coal seam mining depth, mining center coordinate, coal seam inclination, coal seam mining thickness, and the like, but are not limited thereto.

[0079] Further, based on the above embodiment, the at least one iteration is performed according to the initial particle swarm and the preset inversion algorithm until the iteration result satisfies the preset termination condition, and the particle swarm after iteration is obtained, which can include: The fitness corresponding to the particle swarm is calculated according to the model parameters after iteration, the deformation data, and the preset particle fitness evaluation algorithm. When the fitness satisfies the preset termination condition or the iteration number satisfies the preset termination condition, the iteration is terminated, and the particle swarm after iteration is obtained.

[0080] Exemplarily, the particle fitness evaluation algorithm can be based on the cumulative deformation The particle fitness evaluation function of the particle swarm after iteration is constructed, which can be the following formula: , wherein, the particle fitness is represented by f, the LOS deformation of the surface point i in the monitored mining area calculated by the formula is represented by d i, and the LOS deformation of the surface point i in the monitored mining area is represented by d i. Figure 1 ​​​​​​​​​The LOS deformation of the surface point i of the to-be-monitored mining area obtained by the preset deformation extraction algorithm and the SAR data processed above in the embodiment belongs to the deformation data of the to-be-monitored mining area, m is the total number of pixels contained in the range corresponding to the SAR data, these data may be obtained synchronously with the SAR data, for example, the specific acquisition scene may be similar to the scene shown in Figure 2 but is not limited thereto.

[0081] Further, the fitness satisfies the preset termination condition, or the number of iterations satisfies the preset termination condition, which may be expressed as the following condition: , wherein the difference between the minimum fitness of the two consecutive iterations is min(fit n-1), min(fit n ) and min(fit n-1 ) represent the minimum fitness in the nth and (n-1)th iterations. That is, n represents the current number of iterations, and n max is the maximum number of iterations in the working parameters.

[0082] When the fitness satisfies the preset termination condition, or the number of iterations satisfies the preset termination condition, the iteration is terminated, and the particle swarm after the iteration is obtained.

[0083] Figure 5 Fig. 2 is a flowchart of a coal mining monitoring method according to another embodiment of the present application, as shown in Figure 3 Fig. 2 is based on the foregoing Figure 1 embodiment, after the three-dimensional space model of the goaf corresponding to the to-be-monitored mining area is established according to the goaf parameters and the coordinates of the settlement center of the to-be-monitored mining area, the method may further include: S501, establishing a three-dimensional space model of a mining right area according to mining right data.

[0084] For example, the mining right data may refer to data defining the range of the to-be-monitored mining area that is allowed to be mined, and the three-dimensional space model of the mining right area established by the mining right data may represent the range of the to-be-monitored mining area that is allowed to be mined in the form of a three-dimensional space model.

[0085] S502, obtaining the goaf planar projection, the goaf boundary, the mining right area planar projection, and the mining right area boundary of the to-be-monitored mining area according to the three-dimensional space model of the goaf and the three-dimensional space model of the mining right area, respectively.

[0086] For example, the goaf planar projection of the to-be-monitored mining area may be obtained directly according to the three-dimensional space model of the goaf, and the goaf planar projection of the to-be-monitored mining area may be Based on the aforementioned three-dimensional spatial model of the goaf and the aforementioned three-dimensional spatial model of the mining rights area, the goaf boundary of the mining area to be monitored is obtained. For example, the three-dimensional boundary coordinates of the goaf can be constructed using the following formula: , For example, Xmin / max, Ymin / max, and Zmin / max can represent the three-dimensional boundary coordinates of the goaf, that is, the boundary of the goaf in the mining area to be monitored. X0 and Y0 can, for example, refer to the coordinates through the center of surface subsidence. The coordinates of the surface subsidence center can be determined, for example, through the above... The specific method for determining surface subsidence coordinates is not limited here, but L can specifically be the strike length L1 of the goaf, W can specifically be the strike length L2 of the goaf, H represents the average burial depth of the coal seam in the goaf, and h is the mining height. All of these can be obtained through similar methods. Figure 2 The scene is collected or calculated based on the data in the above embodiments. Based on the three-dimensional spatial model of the mining rights area, the planar projection and boundary of the mining rights area can be obtained. For example, the planar boundary of the mining rights area can be directly obtained in the same coordinate system as the three-dimensional spatial model of the goaf. and the projection plane at the depth plane Z=H of the aforementioned mining rights area. However, this is not the only limit.

[0087] S503. Based on the preset boundary judgment algorithm, the above-mentioned goaf plane projection, the above-mentioned goaf boundary, the above-mentioned mining right area plane projection, and the above-mentioned mining right area boundary, calculate and obtain the above-mentioned coal mine mining monitoring results.

[0088] For example, the coal mine mining monitoring results are calculated based on the preset boundary judgment algorithm, the goaf plane projection, the goaf boundary, the mining rights area plane projection, and the mining rights area boundary. For instance, the goaf plane projection can be calculated first. With Mining Rights Projection The spatial intersection, if ∩ And there exists a point that crosses the boundary (X, Y) ∈ \ If the above-mentioned planar projection of the goaf area overlaps with the planar projection of the mining rights area (i.e., part of the goaf area is located within the mining rights area), and at least one point in the planar projection of the goaf area is not within the mining rights projection area, then the minimum distance from the boundary crossing point to the mining rights boundary can be calculated. The calculation formula can be, for example:

[0089] When the minimum distance from the above cross-border point to the mining right boundary is greater than a preset threshold, for example, greater than 50 meters, it is determined that the coal mining monitoring result is over-layer cross-border mining. When the minimum distance from the above cross-border point to the mining right boundary is greater than a preset threshold, for example, greater than 50 meters, it is determined that the coal mining monitoring result is over-layer cross-border mining.

[0090] Of course, the above content is only one possible example, and the actual goaf plane projection, goaf boundary, mining right area plane projection, mining right area boundary determination method, actual preset cross-border judgment algorithm, actual coal mining monitoring result calculation and acquisition method, etc. can be the same as or different from the content in the above example.

[0091] Figure 6 A structure diagram of a coal mining monitoring device provided by an embodiment of the present application, which can execute the above coal mining monitoring method. The device can be integrated into a computer or other device with computing processing capability, as shown in the figure. The device can include: Figure 6 The acquisition module 610 is configured to acquire a plurality of synthetic aperture radar (SAR) data of a to-be-monitored mining area collected by a plurality of acquisition devices, and the plurality of SAR data includes SAR data of different wave bands.

[0092] The correction module 620 is configured to acquire a foreground image corresponding to the two-dimensional image by using a preset image processing model.

[0093] The deformation extraction module 630 is configured to acquire deformation data of the to-be-monitored mining area according to a preset deformation extraction algorithm and the processed SAR data.

[0094] The inversion module 640 is configured to calculate and acquire a plurality of goaf parameters corresponding to the to-be-monitored mining area according to a preset inversion algorithm and the deformation data.

[0095] The monitoring module 660 is configured to establish a three-dimensional space model of a goaf corresponding to the to-be-monitored mining area according to the goaf parameters and the subsidence center coordinates of the to-be-monitored mining area, and the three-dimensional space model of the goaf is used to monitor the to-be-monitored mining area.

[0096] ​​The coal mining monitoring method provided in the embodiments of the present application comprises the following steps: acquiring a plurality of synthetic aperture radar (SAR) data of a to-be-monitored mining area collected by a plurality of collection devices, wherein the plurality of SAR data comprises SAR data of different wave bands; performing correction processing on the SAR data to obtain processed SAR data; acquiring deformation data of the to-be-monitored mining area according to a preset deformation extraction algorithm and the processed SAR data; calculating a plurality of goaf parameters corresponding to the to-be-monitored mining area according to a preset inversion algorithm and the deformation data; and establishing a three-dimensional space model of the goaf corresponding to the to-be-monitored mining area according to the goaf parameters and the coordinates of the subsidence center of the to-be-monitored mining area, wherein the three-dimensional space model of the goaf is used for monitoring the to-be-monitored mining area. In the embodiments, the plurality of SAR data of different wave bands of the to-be-monitored mining area are acquired through a plurality of collection devices, the SAR data are corrected, the deformation data of the to-be-monitored mining area are extracted from the processed SAR data through the preset deformation extraction algorithm, the plurality of goaf parameters corresponding to the to-be-monitored mining area are calculated through the preset inversion algorithm and the deformation data of the to-be-monitored mining area, the three-dimensional space model of the goaf of the to-be-monitored mining area is established based on the goaf parameters and the coordinates of the subsidence center of the to-be-monitored mining area, and finally the to-be-monitored mining area is monitored through the three-dimensional space model of the goaf, thereby improving the collection and acquisition of the surface deformation in the coal mining monitoring process and the analysis and judgment accuracy and efficiency of the overlayer out-of-bound mining behavior.

[0097] Optionally, the correction module 620 can be specifically configured to perform geometric correction on the SAR data through a preset resolution geometric correction algorithm to obtain geometrically corrected SAR data.

[0098] Optionally, the preset deformation extraction algorithm can comprise an interferometric phase combination algorithm, a differential interferometric processing algorithm, a time-series deformation inversion algorithm, and a cumulative deformation algorithm.

[0099] The deformation extraction module 630 can be specifically configured to acquire a differential interferogram sequence according to the interferometric phase combination algorithm and the processed SAR data, process the differential interferogram sequence through the differential interferometric processing algorithm to obtain processed differential interferogram sequence, calculate the deformation rate of the to-be-monitored mining area according to the time-series deformation inversion algorithm and the processed differential interferogram sequence, and calculate the cumulative deformation value of the to-be-monitored mining area according to the cumulative deformation algorithm and the deformation rate of the to-be-monitored mining area.

[0100] Optionally, the preset deformation extraction algorithm can further comprise a filtering algorithm, a phase unwrapping algorithm, and an atmospheric delay correction algorithm.

[0101] The deformation extraction module 630 can also be configured to filter, phase unwrap and remove atmospheric delay phase from the processed differential interferogram sequence by using the filtering algorithm, the phase unwrapping algorithm and the atmospheric delay correction algorithm respectively.

[0102] Optionally, the inversion module 640 can be configured to obtain high-coherence points of edges of the plurality of subsidence zones in the to-be-monitored mining area by using a preset deformation point screening algorithm, and determine a search range and working parameters of the preset inversion algorithm according to the high-coherence points. An initial particle swarm is generated by using the preset inversion algorithm with the determined search range and working parameters, and the initial particle swarm includes model parameters corresponding to the preset inversion algorithm. At least one iteration is performed according to the initial particle swarm and the preset inversion algorithm until an iteration result meets a preset termination condition, and an iterated particle swarm is obtained, and the iterated particle swarm includes iterated model parameters. The plurality of goaf parameters are obtained according to the iterated particle swarm.

[0103] Optionally, the inversion module 640 can be configured to calculate and obtain fitness corresponding to the particle swarm according to the iterated model parameters, the deformation data and a preset particle fitness evaluation algorithm. When the fitness meets the preset termination condition or the number of iterations meets the preset termination condition, the iteration is terminated, and the iterated particle swarm is obtained.

[0104] Optionally, the monitoring module 660 can also be configured to establish a three-dimensional space model of a mining right area according to mining right data. The goaf planar projection, the goaf boundary, the mining right area planar projection and the mining right area boundary of the to-be-monitored mining area are obtained according to the goaf three-dimensional space model and the mining right area three-dimensional space model respectively. The coal mining monitoring result is calculated and obtained according to a preset boundary-crossing judgment algorithm, the goaf planar projection, the goaf boundary, the mining right area planar projection and the mining right area boundary.

[0105] The apparatus is configured to execute the method provided in the foregoing embodiments, and has similar implementation principles and technical effects, which will not be described here in detail.

[0106] Figure 7 A structural schematic diagram of an electronic device is provided for the embodiments of the present application. The electronic device can be a computer or other device with computing processing capability, as shown in Figure 7 The device 700 includes a processor 710, a storage medium 720 and a bus 730. The processor 710 and the storage medium 720 are communicatively connected through the bus 730.

[0107] The storage medium 720 stores machine readable instructions executable by the processor 710, and when the electronic device is running, the processor 710 executes the machine readable instructions to perform the coal mining monitoring method.

[0108] It should be understood that, Figure 7 The structure shown is only a structural schematic diagram of the electronic device, and the electronic device can further include more or less components than those shown in the Figure 7 or have a different configuration from that shown in the Figure 7 . Figure 7 The components shown in the can be implemented in hardware, software, or a combination thereof.

[0109] The embodiment of the present application further provides a computer readable storage medium, the computer readable medium stores a computer program, and the computer program can be executed by the processor to implement the coal mining monitoring method described in the method embodiment.

[0110] The computer readable storage medium can be an electronic storage such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer readable storage medium includes a non-transitory computer readable storage medium. The computer readable storage medium has a storage space for program codes for executing any method steps in the above method. The program codes can be read from or written into one or more computer program products. The program codes can be compressed in a suitable form, for example.

[0111] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented by other manners. The apparatus embodiments described above are merely illustrative, for example, the flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that in some alternative implementation manners, the functions noted in the blocks can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can also be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for executing the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0112] In addition, the function modules in the embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0113] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product in essence or the part of the technical solutions that make contributions to the prior art, or part of the technical solutions. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the method of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0114] The above description is only the preferred embodiments of the present application, and does not limit the patent scope of the present application, and any equivalent structural transformation made by using the contents of the present application specification and drawings, or direct / indirect application in other related technical fields is included in the patent protection scope of the present application.

Claims

1. A method of monitoring coal mining, characterised by, The method comprises the following steps: acquiring a plurality of synthetic aperture radar (SAR) data of a to-be-monitored mining area collected by a plurality of collection devices, wherein the plurality of SAR data comprises SAR data of different wave bands; performing correction processing on the SAR data to obtain processed SAR data; acquiring deformation data of the to-be-monitored mining area according to a preset deformation extraction algorithm and the processed SAR data; calculating a plurality of goaf parameters corresponding to the to-be-monitored mining area according to a preset inversion algorithm and the deformation data; establishing a three-dimensional space model of the goaf corresponding to the to-be-monitored mining area according to the goaf parameters and the coordinates of the subsidence center of the to-be-monitored mining area, and the three-dimensional space model of the goaf is used for monitoring the to-be-monitored mining area.

2. The method of claim 1, wherein, The method further comprises the following steps: performing geometric correction on the SAR data by using a geometric correction algorithm with a preset resolution to obtain geometrically corrected SAR data.

3. The method of claim 2, wherein, The preset deformation extraction algorithm comprises an interferometric phase combination algorithm, a differential interferometric processing algorithm, a time-series deformation inversion algorithm, and a cumulative deformation algorithm. The method further comprises the following steps: acquiring a differential interferogram sequence according to the interferometric phase combination algorithm and the processed SAR data; processing the differential interferogram sequence by using the differential interferometric processing algorithm to obtain processed differential interferogram sequence; calculating the deformation rate of the to-be-monitored mining area according to the time-series deformation inversion algorithm and the processed differential interferogram sequence; calculating the cumulative deformation value of the to-be-monitored mining area according to the cumulative deformation algorithm and the deformation rate of the to-be-monitored mining area.

4. The method of claim 3, wherein, The preset deformation extraction algorithm further comprises a filtering algorithm, a phase unwrapping algorithm, and an atmospheric delay correction algorithm. The method further comprises the following steps:

5. The method of claim 1, wherein, filtering, phase unwrapping, and removing atmospheric delay phase from the processed differential interferogram sequence by using the filtering algorithm, the phase unwrapping algorithm, and the atmospheric delay correction algorithm, respectively. The method further comprises the following steps: acquiring a plurality of high-coherence points at the edges of a plurality of subsidence areas of the to-be-monitored mining area by using a preset deformation point screening algorithm, and determining the search range and working parameters of the preset inversion algorithm according to the high-coherence points; generating an initial particle swarm by using the preset inversion algorithm with the determined search range and working parameters, wherein the initial particle swarm comprises model parameters corresponding to the preset inversion algorithm; performing at least one iteration according to the initial particle swarm and the preset inversion algorithm until the iteration result meets a preset termination condition to obtain an iterated particle swarm, wherein the iterated particle swarm comprises iterated model parameters; acquiring a plurality of goaf parameters according to the iterated particle swarm.

6. The method of claim 5, wherein, The at least one iteration is performed according to the initial particle group and the preset inversion algorithm until an iteration result meets a preset termination condition, and an iterated particle group is obtained, including: According to the iterated model parameters, the deformation data, and a preset particle fitness evaluation algorithm, a fitness corresponding to the particle group is calculated and obtained. When the fitness meets the preset termination condition, or the number of iterations meets the preset termination condition, the iteration is terminated, and the iterated particle group is obtained.

7. The method of claim 1, wherein, After the goaf three-dimensional space model corresponding to the to-be-monitored mining area is established according to the goaf parameters and the subsidence center coordinates of the to-be-monitored mining area, the method further includes: A mining right area three-dimensional space model is established according to mining right data; According to the goaf three-dimensional space model and the mining right area three-dimensional space model, a goaf plane projection, a goaf boundary, a mining right area plane projection, and a mining right area boundary of the to-be-monitored mining area are obtained; According to a preset out-of-boundary judgment algorithm, the goaf plane projection, the goaf boundary, the mining right area plane projection, and the mining right area boundary, a coal mine mining monitoring result is calculated and obtained.

8. A coal mining monitoring device, characterised in that, The method includes: An acquisition module is configured to acquire a plurality of synthetic aperture radar (SAR) data of a to-be-monitored mining area collected by a plurality of acquisition devices, wherein the plurality of SAR data includes SAR data of different wave bands; A correction module is configured to acquire a foreground image corresponding to the two-dimensional image by using a preset image processing model; A deformation extraction module is configured to acquire deformation data of the to-be-monitored mining area according to a preset deformation extraction algorithm and the processed SAR data; An inversion module is configured to calculate and acquire a plurality of goaf parameters corresponding to the to-be-monitored mining area according to a preset inversion algorithm and the deformation data; A monitoring module is configured to establish a goaf three-dimensional space model corresponding to the to-be-monitored mining area according to the goaf parameters and subsidence center coordinates of the to-be-monitored mining area, and the goaf three-dimensional space model is used to monitor the to-be-monitored mining area.

9. An electronic device, comprising: The method includes: A processor, a storage medium, and a bus, the storage medium stores machine-readable instructions executable by the processor, when the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is executed by the processor to perform the steps of the method according to any one of claims 1-7.

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