Mine water and land integrated subsidence monitoring method

Abstract

The application discloses a kind of mining area water and land integration subsidence monitoring method.The method comprises the following steps: determining the flight trajectory of the unmanned aerial vehicle and the navigation trajectory of the unmanned ship according to the coordinate system information and the elevation datum of the to-be-measured mining area block;Obtain the laser point cloud data of the land area in the to-be-measured mining area block, the single-beam reflection data of the water area in the to-be-measured mining area block and the real-time dynamic position information at the preset verification point;Perform data fusion to determine the digital elevation model of the to-be-measured mining area block to determine the subsidence condition of the to-be-measured mining area block.The application can ensure data fusion accuracy on the basis of performing subsidence monitoring for land and water area full-area coverage, avoid errors when data splicing, also can compare and screen filtering algorithm and interpolation algorithm, and further utilize the dynamic information at the verification point to perform precision verification on land and water area DEM, to ensure the modeling accuracy of land and water area DEM, so as to ensure the accuracy and reliability of subsidence condition detection.

Description

Technical Field

[0001] This application relates to a method for monitoring subsidence in mining areas that integrates land and water resources, and belongs to the fields of surveying and mapping science and technology and mine safety. Background Technology

[0002] Long-term mining of mineral resources such as coal often leads to land subsidence. In mining areas with high groundwater levels, subsidence areas are prone to forming large-scale water accumulation zones, posing a serious threat to the ecological environment, infrastructure, and food security. While current monitoring methods for mining subsidence, those relying on leveling and GNSS technologies offer high accuracy, their results are discrete and point-like, making it difficult to achieve full regional coverage. UAV-borne lidar can acquire 3D land topography, but its limitations prevent it from acquiring underwater topography. While unmanned surface vessel (USV) single-beam bathymetry can acquire underwater 3D topography, its single-beam characteristics prevent it from covering land surface deformation. Furthermore, if both methods are used simultaneously to acquire land and underwater topography information, inherent differences in coordinate systems, data structures, and sampling densities make it difficult to achieve a unified data benchmark, potentially leading to splicing misalignment and accuracy loss during data fusion. Summary of the Invention

[0003] This application discloses a method for monitoring subsidence in mining areas that integrates land and water resources.

[0004] The integrated land-water subsidence monitoring method for mining areas in this application includes the following steps:

[0005] Based on the coordinate system information and elevation datum of the mining area to be measured, the flight trajectory of the UAV and the navigation trajectory of the unmanned vessel are determined, wherein the flight trajectory and the navigation trajectory have the same time and space datum. When the UAV flies along the flight path and the unmanned vessel travels along the navigation path, the system acquires laser point cloud data of the land area in the mining area to be tested, single beam reflection data of the water area in the mining area to be tested, and real-time dynamic location information of one or more preset verification points located in the land area of ​​the mining area to be tested. The real-time dynamic location information of the preset verification points is configured to perform accuracy verification on the laser point cloud data and the single beam reflection data. Based on the laser point cloud data, the single-beam reflection data, and the real-time dynamic location information of the preset verification points, data fusion is performed to determine the digital elevation model of the mining area to be tested, so as to determine the subsidence status of the mining area to be tested based on the digital elevation model.

[0006] In some implementations, determining the flight path of the UAV and the navigation path of the unmanned surface vessel based on the coordinate system information and elevation datum of the mining area to be measured includes: Based on the preset positioning system, the plane coordinate system of the UAV and the unmanned vessel is determined as the first plane coordinate system, and the elevation datum of the UAV and the unmanned vessel is determined as the first elevation datum. Based on the meteorological and topographical conditions of the mining area to be tested, a common operating time benchmark is determined for the UAV and the unmanned vessel. Based on the first plane coordinate system, the first elevation datum, and the operation time datum, the flight trajectory and the navigation trajectory are planned.

[0007] In some implementations, the first plane coordinate system is CGCS2000, and the first elevation datum is the 1985 National Elevation Datum.

[0008] In some embodiments, when the UAV flies along the flight path and the unmanned vessel travels along the navigation path, acquiring laser point cloud data of the land area in the mining area to be tested, single-beam reflection data of the underwater area in the mining area to be tested, and real-time dynamic location information of one or more preset verification points located on land in the mining area to be tested includes: Under suitable weather conditions, based on the operation time reference, the laser point cloud data is acquired while the UAV flies along the flight trajectory; Under the specified suitable weather conditions, based on the specified operation time reference, and while the unmanned vessel is navigating along the specified navigation trajectory, the single-beam reflection data is acquired. Under suitable weather conditions, based on the operation time reference, a preset real-time information acquisition device is controlled to acquire the real-time dynamic location information of the preset verification point.

[0009] In some embodiments, when the UAV flies along the flight path and the unmanned vessel travels along the navigation path, acquiring laser point cloud data of the land area in the mining area to be tested, single-beam reflection data of the underwater area in the mining area to be tested, and real-time dynamic location information of one or more preset verification points located on land in the mining area to be tested, further includes: When the water area in the mining area to be tested meets the first preset conditions, the unmanned vessel is controlled to navigate along the navigation trajectory in order to acquire the single-beam reflection data under manual remote control.

[0010] In some implementations, the step of performing data fusion based on the laser point cloud data, the single-beam reflection data, and the real-time dynamic location information of the preset verification points to determine the digital elevation model of the mining area to be measured includes: Based on the laser point cloud data, point cloud filtering is performed to determine the land area digital elevation model; Based on the single-beam reflection data, data correction and interpolation are performed to determine the digital elevation model of the water area. Data fusion is performed on the land digital elevation model and the water digital elevation model to determine the digital elevation model of the mining area to be measured.

[0011] In some implementations, the step of performing point cloud filtering based on the laser point cloud data to determine the land area digital elevation model includes: The laser point cloud data is subjected to denoising and thinning processes to determine the denoised point cloud data; Based on the denoised point cloud data, the first point cloud filtering method is selected from a plurality of preset point cloud filtering methods; Based on the first point cloud filtering method, multiple ground point information is obtained according to the denoised point cloud data; Based on the ground point information, the land area digital elevation model is determined using Delaunay triangulation and linear interpolation.

[0012] In some implementations, the step of performing data correction and error elimination based on the single-beam reflection data to determine the digital elevation model of the water area includes: Based on the single-beam reflection data, the positioning information of the unmanned vessel, and the depth correction information of the water area in the mining area to be tested, the underwater three-dimensional point cloud data is determined. Based on environmental characteristics and equipment attributes, the underwater 3D point cloud data is corrected and error is eliminated to determine the corrected underwater 3D point cloud data. Based on the underwater three-dimensional point cloud data and the real-time dynamic location information of the preset verification points, the first interpolation method is selected from multiple preset interpolation methods. Based on the first interpolation method, the digital elevation model of the water area is determined according to the corrected underwater three-dimensional point cloud data.

[0013] In some implementations, the step of performing data fusion on the land digital elevation model and the water digital elevation model to determine the digital elevation model of the mining area to be measured includes: In the overlapping boundary area of ​​land and water in the mining area to be measured, based on the water digital elevation model, unreliable data in the land digital elevation model are replaced to determine a corrected land digital elevation model; Unify the grid spacing of the modified land digital elevation model and the water digital elevation model; A digital elevation model of the mining area to be measured is generated by performing data fusion through grid resampling and multiple preset interpolation methods.

[0014] In some implementations, determining the subsidence status of the mining area block to be measured based on the digital elevation model includes: Obtain multiple sets of digital elevation models of the mining area to be measured at different times; Based on multiple sets of digital elevation models, differential calculations are performed to determine the subsidence distribution map of the mining area to be measured. Based on the subsidence distribution map, the subsidence characteristic parameters of the mining area to be tested are determined to determine the subsidence situation of the mining area to be tested, wherein the subsidence situation includes the subsidence, curvature, horizontal movement and deformation pattern of the mining area to be tested.

[0015] The beneficial effects of this application are as follows: The integrated land-water subsidence monitoring method in the mining area embodiments of this application can ensure the data fusion accuracy when fusing water area data and land area data, while performing subsidence monitoring covering the entire land and water area, and avoiding coordinate transformation errors during data stitching. Furthermore, this application can also verify the accuracy of land and water area DEMs by comparing and filtering algorithms and interpolation algorithms, and further utilizing dynamic information at verification points, to ensure the modeling accuracy of land and water area DEMs, thereby guaranteeing the accuracy and reliability of subsidence detection. Attached Figure Description

[0016] Figure 1 This is one of the flowcharts of the integrated land-water subsidence monitoring method in the mining area according to the embodiments of this application; Figure 2 This is the second flowchart of the integrated land-water subsidence monitoring method for mining areas in the embodiments of this application; Figure 3 This is one of the application scenarios of the integrated land-water subsidence monitoring method in the mining area according to the embodiments of this application; Figure 4 This is the third flowchart of the integrated land-water subsidence monitoring method for mining areas in the embodiments of this application; Figure 5 This is the fourth flowchart of the integrated land-water subsidence monitoring method for mining areas in the embodiments of this application; Figure 6 This is the fifth flowchart of the integrated land-water subsidence monitoring method for mining areas in the embodiments of this application; Figure 7 This is the second schematic diagram of the application scenario of the integrated land-water subsidence monitoring method in the mining area according to the embodiments of this application; Figure 8 This is the sixth flowchart of the integrated land-water subsidence monitoring method for mining areas in the embodiments of this application; Figure 9 This is the seventh flowchart of the integrated land-water subsidence monitoring method for mining areas in the embodiments of this application; Figure 10 The eighth flowchart of the integrated land-water subsidence monitoring method for mining areas in this application embodiment; Figure 11 This is the third schematic diagram of the application scenario of the integrated land-water subsidence monitoring method in the mining area according to the embodiments of this application; Figure 12 This is the fourth schematic diagram of the application scenario of the integrated land-water subsidence monitoring method in the mining area according to the embodiments of this application. Detailed Implementation

[0017] Please see Figure 1 The integrated land-water subsidence monitoring method for mining areas in this application includes the following steps: Step 01: Based on the coordinate system information and elevation datum of the mining area to be measured, determine the flight trajectory of the UAV and the navigation trajectory of the unmanned surface vessel. The flight path and the navigation path mentioned therein have the same time reference and spatial reference.

[0018] Specifically, when detecting land subsidence in mining areas, data fusion is difficult because the coordinate and elevation benchmarks used by the land information acquired by UAVs and the water information acquired by UAVs differ. Therefore, for example, before planning the flight paths of UAVs and the navigation paths of UAVs, it is necessary to first unify the time and spatial benchmarks of the UAVs and UAVs. This ensures that the flight and navigation paths have the same time and spatial benchmarks, and consequently, that the land topography data acquired by UAVs and the water topography data acquired by UAVs also have the same time and spatial benchmarks, facilitating the fusion of the land and water topography data.

[0019] Furthermore, in some implementations, please refer to Figure 2 Step 01 specifically includes: Step 011: Based on the preset positioning system, the plane coordinate system of the UAV and the unmanned vessel is determined as the first plane coordinate system, and the elevation datum of the UAV and the unmanned vessel is determined as the first elevation datum. Step 012: Determine the common operating time benchmark for both UAVs and unmanned vessels based on the meteorological and topographical conditions of the mining area to be tested. Step 013: Based on the first plane coordinate system, the first elevation datum, and the operation time datum, plan the flight trajectory and navigation trajectory.

[0020] Specifically, based on the above implementation method, regarding the unification of time and spatial references, a pre-set positioning system can be used to simultaneously provide the same positioning service to both the UAV and the unmanned surface vessel (USV) via the Ntrip protocol. While providing this service, the planar coordinate system and elevation reference of both the UAV and USV are kept consistent. For example, the positioning system can employ the BeiDou ground-based augmentation system, providing real-time, high-precision differential positioning services to both the UAV and USV via the Ntrip protocol. Based on this, the planar coordinate system of both the UAV and USV is uniformly set to CGCS2000, and the elevation reference is unified to the 1985 National Elevation Datum. In this way, the spatial reference of the UAV and USV can be unified. For example, please refer to... Figure 3 , Figure 3 The diagram shows the unmanned vessel navigation route planned in the waters of mining area A (corresponding to the mining area to be measured) in a coal mine, based on the aforementioned time and spatial references.

[0021] The standardization of time references is mainly based on the actual terrain and meteorological conditions of the mining area to be tested. For example, based on the local weather forecast information (including temperature, weather conditions, and wind speed) of the mining area to be tested, and combined with the actual terrain conditions of the mining area to be tested, a time period suitable for both drone flight and unmanned vessel navigation can be selected. Then, the start point of this time period can be used as the common operating time reference for both, thereby achieving standardization of time references between the two.

[0022] Under the condition that the time and space references are unified, the planned UAV flight trajectory and the UAV navigation trajectory will naturally have the same time and space references. This can provide a data foundation for the high-precision fusion of subsequent land and water data, and avoid errors in data stitching caused by differences in coordinate system, elevation and time references.

[0023] Please continue reading. Figure 1 The integrated land-water subsidence monitoring method for mining areas in this application also includes: Step 02: With the UAV flying along its flight path and the unmanned vessel traveling along its navigation path, acquire laser point cloud data of the land area in the mining area to be tested, single-beam reflection data of the water area in the mining area to be tested, and real-time dynamic location information of one or more preset verification points located on the land area in the mining area to be tested. The real-time dynamic location information at the preset verification points is configured to perform accuracy verification on laser point cloud data and single-beam reflection data.

[0024] Specifically, based on the above implementation method, and under the condition that the flight trajectory of the UAV and the navigation trajectory of the unmanned surface vessel are planned according to the same time and space references, the UAV can be automatically controlled to fly according to the flight trajectory, and the unmanned surface vessel can be automatically controlled to navigate according to the navigation trajectory. During the navigation, laser point cloud data of the land area and single-beam reflection data of the water area in the mining area to be tested can be acquired based on the UAV and the unmanned surface vessel. In addition, real-time dynamic position information of preset verification points used to perform accuracy verification on the aforementioned laser point cloud data and single-beam reflection data can also be acquired.

[0025] Furthermore, in some implementations, please refer to Figure 4 Step 02 further includes: Step 021: Under suitable weather conditions, based on the operation time reference, acquire laser point cloud data while the UAV flies along its flight path; Step 022: Under suitable weather conditions, based on the operation time reference, acquire single-beam reflection data while the unmanned vessel is navigating along its navigation trajectory; Step 023: Under suitable weather conditions, based on the operation time reference, control the preset real-time information acquisition equipment to acquire the real-time dynamic location information of the preset verification point.

[0026] It should be noted that steps 021, 022, and 023 are generally executed simultaneously. Figure 4 The execution order shown is only a step illustration and should not be interpreted as a limitation on the execution order.

[0027] Specifically, based on the above implementation method, when the UAV flies along the flight path, the airborne lidar installed on the UAV is used to perform land terrain detection on the land area covered by the flight path, thereby obtaining the lidar point cloud data of the land area in the mining area to be tested. After fitting, the lidar point cloud data can accurately show the terrain of the land area.

[0028] The process of acquiring laser point cloud data can be illustrated by the following example: During UAV flight, the UAV simultaneously collects static GNSS positioning information from its associated base station and its own POS data. The POS data typically includes at least the UAV's own GNSS positioning information and IMU attitude information. Based on this information, a UAV track file can be determined through integrated navigation calculations. This file generally includes at least the actual track record and real-time attitude angle information. Simultaneously, the onboard LiDAR on the UAV also acquires raw laser point cloud data of the terrain near the flight path during flight. Therefore, based on the aforementioned UAV track file and raw laser point cloud data, point cloud calculations can be performed to obtain the aforementioned laser point cloud data.

[0029] Meanwhile, while the unmanned vessel is navigating along its navigation path, the single-beam echo sounder installed on the unmanned vessel is used to perform underwater topographic surveys on the water area covered by the navigation path, thereby obtaining single-beam reflection data of the water area in the mining area to be surveyed.

[0030] Furthermore, for example, considering that the aforementioned laser point cloud data and single-beam reflection data may still have data acquisition errors and data fluctuations, a GNSS-RTK device (i.e., a real-time differential positioning device using GNSS positioning technology) can be used under the same time reference to obtain real-time differential positioning information (i.e., RTK information, corresponding to real-time dynamic location information) at a certain number of preset verification points in relatively stable areas of the mining area to be tested. The selection of these preset verification points generally depends directly on the stability of various locations in the land area of ​​the mining area to be tested. For example, preliminary screening can be performed at various locations in the land area based on the actual geological conditions, selecting several locations with stable geological conditions. Then, experimental RTK information is obtained at the locations obtained after the preliminary screening using a GNSS-RTK device, and several locations with higher RTK information accuracy are selected as the aforementioned preset verification points. The main function of the RTK information at the preset verification points is to serve as an accuracy verification benchmark when performing accuracy checks on the laser point cloud data collected by the UAV and the single-beam reflection data collected by the unmanned surface vessel.

[0031] In addition, the acquisition process of the aforementioned laser point cloud data, single beam reflection data, and RTK information at each preset verification point needs to be carried out under suitable weather conditions. The aforementioned suitable weather conditions generally refer to weather conditions where there is no precipitation and the temperature, humidity, and wind force can support the normal operation of UAVs, unmanned ships, and GNSS-RTK equipment.

[0032] In some implementations, step 02 further includes: When the water area in the mining area to be tested meets the first preset conditions, the unmanned vessel is controlled to navigate along the navigation trajectory in order to obtain single-beam reflection data under manual remote control.

[0033] Specifically, in the waters of the mining area to be measured, there may be complex underwater environments such as aquatic plants and fishing nets in the nearshore area. This may affect the accuracy of underwater topographic measurements and may also cause unmanned vessels that automatically follow their navigation paths to become trapped in the aforementioned underwater environment during their journey.

[0034] To address the aforementioned situation, for example, if complex environmental conditions such as aquatic plants or fishing nets exist in the nearshore area that may affect the navigation safety of the unmanned vessel (corresponding to the fulfillment of the first preset condition), the unmanned vessel can be manually controlled to temporarily deviate from its navigation trajectory, thereby avoiding the aforementioned complex environment. In this way, only the manual control process needs to be recorded, and this record can be taken into account when performing data processing on the single-beam reflection data acquired by the unmanned vessel, ensuring the accuracy of the data processing.

[0035] Please continue reading. Figure 1 The integrated land-water subsidence monitoring method for mining areas in this application also includes: Step 03: Based on the laser point cloud data, single beam reflection data, and the real-time dynamic location information of the preset verification points, perform data fusion to determine the digital elevation model of the mining area to be tested, so as to determine the subsidence of the mining area to be tested based on the digital elevation model.

[0036] Specifically, based on the above implementation method, after acquiring laser point cloud data using drones, single-beam reflection data using unmanned vessels, and RTK information of each preset verification point using GNSS-RTK equipment, corresponding data processing is performed on the above three sets of data, and the data is fused after processing to generate a complete digital elevation model for the mining area to be tested, so as to further analyze the overall subsidence of the mining area to be tested based on the obtained digital elevation model.

[0037] In some implementations, please refer to Figure 5 Step 03 specifically includes: Step 031: Based on the laser point cloud data, perform point cloud filtering to determine the land area digital elevation model.

[0038] Further, please refer to Figure 6 Step 031 specifically includes: Step 0311: Perform denoising and thinning processing on the laser point cloud data to determine the denoised point cloud data; Step 0312: Based on the denoised point cloud data, select and determine the first point cloud filtering method from multiple preset point cloud filtering methods; Step 0313: Based on the first point cloud filtering method, obtain information on multiple ground points according to the denoised point cloud data; Step 0314: Based on the ground point information, determine the land area digital elevation model using Delaunay triangulation and linear interpolation.

[0039] Specifically, the data processing procedure for laser point cloud data will be briefly explained below.

[0040] First, based on the aforementioned laser point cloud data, noise and outlier points are removed and thinned to obtain denoised point cloud data, thereby eliminating invalid and redundant data to ensure the accuracy and conciseness of the laser point cloud data.

[0041] Next, using the aforementioned denoised point cloud data as a foundation, various filtering algorithms currently available in related technologies are employed to perform operations on the denoised point cloud data, attempting to extract some ground point information. This allows for the selection of the most effective filtering algorithm from among these different algorithms for use in the data processing of laser point cloud data. For example, among current filtering algorithms such as progressive triangulation encryption filtering, mathematical morphology filtering, and cloth simulation filtering, the selection can be based on the filtering and extraction effect of each algorithm on ground point information in the denoised point cloud data. Specific selection criteria can comprehensively consider the Type I error, Type II error, and total error of the filtering algorithm, choosing the algorithm with the smallest overall error performance. For example, the progressive triangulation encryption filtering algorithm, in... Figure 3 In the mining area shown, the Type I error is 1.92%, the Type II error is 2.80%, and the total error is 2.07%, making its filtering effect the best among the three filtering algorithms mentioned above.

[0042] Based on the selected filtering algorithm, a complete filtering operation is performed on the denoised point cloud data to extract ground point information completely. Then, Delaunay triangulation is used to construct an irregular triangular network (TIN) based on the ground points. Next, linear interpolation is performed on the constructed TIN to finally generate a land digital elevation model with grid spacing meeting the preset requirements. The land digital elevation model (hereinafter referred to as the land DEM model) digitally simulates the elevation information of the ground terrain using limited terrain elevation data. It is the core data form for expressing terrain undulation in the field of geospatial information. Simply put, a DEM uses an ordered array of values ​​to represent the elevation of each location on the Earth's surface, thus intuitively and accurately reflecting the topographic relief. For example, please refer to... Figure 7 , Figure 7(a) shows the morphology of the land area DEM model of mining block A after imaging, where the grid spacing is 2 meters.

[0043] In some implementations, please refer to [the relevant documentation]. Figure 5 Step 03 also includes: Step 032: Based on the single-beam reflection data, perform data correction and interpolation to determine the digital elevation model of the water area.

[0044] Further, please refer to Figure 8 In some embodiments, step 032 further includes: Step 0321: Determine the three-dimensional point cloud data of the underwater surface based on the single-beam reflection data, the positioning information of the unmanned vessel, and the depth correction information of the water area in the mining area to be tested; Step 0322: Based on environmental characteristics and equipment attributes, perform correction and error removal on the underwater 3D point cloud data to determine the corrected underwater 3D point cloud data; Step 0323: Based on the underwater 3D point cloud data and the real-time dynamic location information of the preset verification points, select and determine the first interpolation method from multiple preset interpolation methods; Step 0324: Based on the first interpolation method, determine the digital elevation model of the water area according to the corrected underwater three-dimensional point cloud data.

[0045] Specifically, based on the above implementation method, the data processing procedure for single-beam reflection data will be briefly described below.

[0046] First, considering the application of single-beam reflection information, the underwater three-dimensional point cloud data can be obtained by combining the single-beam reflection data with the GNSS positioning information of the unmanned vessel and the water depth correction information. This allows for a relatively accurate representation of the underwater topography of the aforementioned water area after subsequent data processing.

[0047] Next, based on the water depth of the aforementioned water area, the attribute information of the single-beam reflector onboard the unmanned vessel, and even the control process records when the unmanned vessel deviates from its original course, the obtained underwater 3D point cloud data can be corrected to obtain corrected underwater 3D point cloud data. Specific corrections generally include sound speed correction, draft correction, attitude correction, and outlier removal. These corrections can be performed using relevant correction methods currently available in related technologies, and this application does not impose specific limitations.

[0048] Next, using the aforementioned corrected underwater 3D point cloud data as the data foundation, various interpolation algorithms in current related technologies are employed to perform interpolation operations on the corrected underwater 3D point cloud, thereby generating initial digital elevation models (hereinafter referred to as initial water area DEM models) corresponding to each interpolation algorithm. Then, using the RTK information at each preset verification point as a comparison object, accuracy verification is performed on the aforementioned initial water area DEM models to select the preferred interpolation algorithm for generating the water area DEM model. For example, the selection process can be performed in the aforementioned manner among interpolation algorithms such as inverse distance weighted interpolation (IDW), Kriging interpolation, and natural neighborhood interpolation (NNI). The accuracy verification process generally involves evaluating the accuracy of each point in the initial water area DEM model based on the RTK information at each preset verification point using indicators such as standard error, mean absolute error, and goodness of fit. A comprehensive evaluation criterion is considered, prioritizing smaller standard errors and mean absolute errors, and larger goodness of fit, thereby selecting one of the aforementioned three interpolation algorithms. For example, for... Figure 3 For the mining area A shown, accuracy evaluation revealed that the initial water area DEM model determined by the natural neighborhood interpolation method had a mean error of 0.11m. The mean absolute error was 0.009m, the smallest among the three interpolation methods, while the goodness of fit was 0.999, the largest among the three interpolation methods. Therefore, the natural neighborhood method was selected as the preferred interpolation algorithm.

[0049] Finally, based on the interpolation algorithm selected through the above steps, interpolation operations are performed on the corrected underwater 3D point cloud data to obtain the final usable water area DEM model. Please continue reading. Figure 7 , Figure 7 (b) shows the morphology of the water area of ​​mining block A after imaging the DEM model.

[0050] In this way, through the above data processing, by refining and filtering the laser point cloud data of the land area and the corresponding underwater 3D point cloud data of the water area, and by using the RTK information of the preset verification points for accuracy verification, the modeling accuracy of the land area DEM model and the water area DEM model can be ensured, thus creating conditions for performing DEM model fusion while maintaining accuracy.

[0051] In some implementations, please refer to [the relevant documentation]. Figure 5 Step 03 also includes: Step 033: Perform data fusion on the land digital elevation model and the water digital elevation model to determine the digital elevation model of the mining area to be measured.

[0052] Further, please refer to Figure 9 In some embodiments, step 033 further includes: Step 0331: In the overlapping boundary area of ​​land and water in the mining area to be measured, based on the water digital elevation model, replace the unreliable data in the land digital elevation model to determine the corrected land digital elevation model. Step 0332: Unify and correct the grid spacing of the land digital elevation model and the water digital elevation model; Step 0333: Perform data fusion through grid resampling and multiple preset interpolation methods to generate a digital elevation model of the mining area to be measured.

[0053] Specifically, based on the above implementation method, the generated land area DEM model and water area DEM model will be stitched and fused to form a complete DEM model of the mining area to be tested, thereby further analyzing the subsidence of the mining area. Since the lidar recorded by the UAV will generate unreliable land area laser point clouds due to reflection or absorption on the water surface, when stitching and fusing the land area DEM model and the water area DEM model, for the area where the water and land meet, it is necessary to use the water area DEM model as the benchmark and replace some of the land area DEM data generated by laser reflection and absorption, thereby determining the corrected land area DEM model.

[0054] Furthermore, after obtaining the corrected land DEM model, the same grid spacing, such as 2 meters, is uniformly applied to both the corrected land DEM model and the water DEM model. Based on this, grid resampling and various interpolation methods from related technologies are used to ensure the overall continuity of the terrain. If necessary, Gaussian filtering can be used to denoise the fused DEM model, ultimately generating an integrated land and water DEM model corresponding to the mining area to be surveyed. In addition, current full-area accuracy evaluation methods can be used to perform accuracy evaluation on the DEM model corresponding to the mining area to be surveyed, ensuring that the model meets the relevant legal and regulatory requirements of the surveying and mapping industry.

[0055] For example, please continue reading Figure 7 , Figure 7 (c) shows the complete DEM model corresponding to mining area block A obtained by fusing the land area DEM model and the water area DEM model. Accuracy evaluation indicates that... Figure 7 The DEM model shown in (c) has a root mean square error of 0.318m and a mean absolute error of 0.226m, which meets the requirements of relevant industry laws and regulations.

[0056] In some implementations, please refer to Figure 10 Step 03 also includes: Step 0341: Obtain multiple sets of digital elevation models of the mining area to be measured at different periods; Step 0342: Based on multiple sets of digital elevation models, perform differential calculations to determine the subsidence distribution map of the mining area to be measured; Step 0343: Based on the subsidence distribution map, determine the subsidence characteristic parameters of the mining area to be tested, in order to determine the subsidence situation of the mining area to be tested. The subsidence situation includes the subsidence, curvature, horizontal movement, and deformation patterns of the land in the mining area to be tested.

[0057] Specifically, according to the steps in the above embodiments, a set of DEM models for the mining area to be tested can be obtained. Over a period of time, multiple sets of DEM models for different periods can be obtained periodically or irregularly using the above steps. For example, for the aforementioned mining area A, in the year... x The first DEM model was obtained, in the year x The following years y Having obtained the second DEM model, differential calculations are performed based on the first and second DEM models to obtain the subsidence distribution map of the entire mining area A. An example of a subsidence distribution map is shown below. Figure 11 As shown in the diagram, based on the aforementioned subsidence distribution map, subsidence characteristic parameters that can describe the subsidence situation of the tested mining area can be obtained. The subsidence situation generally includes the subsidence, curvature, horizontal movement, and deformation patterns of the tested mining area. The aforementioned subsidence situation can be quantitatively described based on the subsidence characteristic parameters. The specific description method can adopt the methods provided in the current related technologies, and this application does not make any specific limitations.

[0058] In addition, for example, besides obtaining a subsidence distribution map of the entire area of ​​mining block A, it is also possible to obtain a subsidence contour map of the entire area of ​​mining block A. An example of a subsidence contour map is shown below. Figure 12 As shown, the distribution of subsidence values ​​in mining area block A can be analyzed using the subsidence contour map.

[0059] Thus, the integrated land-water subsidence monitoring method in this application can ensure the accuracy of data fusion when fusing water-collected data with land-collected data, while performing subsidence monitoring covering the entire land and water area, and avoiding coordinate transformation errors during data stitching. Furthermore, this application can verify the accuracy of land and water DEMs by comparing and filtering algorithms and interpolation algorithms, and further utilizing dynamic information at verification points, to ensure the modeling accuracy of land and water DEMs, thereby guaranteeing the accuracy and reliability of subsidence detection.

[0060] The above description is merely a preferred embodiment of this application and is not intended to limit this application in any way. Although this application has disclosed the preferred embodiment as above, it is not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the technical solution of this application. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the technical solution of this application, based on the technical essence of this application and within the spirit and principles of this application, shall still fall within the protection scope of the technical solution of this application.

Claims

1. A method for monitoring land and water integrated subsidence in a mining area, characterized by, The method includes: Based on the coordinate system information and elevation datum of the mining area to be measured, the flight trajectory of the UAV and the navigation trajectory of the unmanned vessel are determined, wherein the flight trajectory and the navigation trajectory have the same time datum and spatial datum. When the UAV flies along the flight path and the unmanned vessel travels along the navigation path, the system acquires laser point cloud data of the land area in the mining area to be tested, single beam reflection data of the water area in the mining area to be tested, and real-time dynamic location information of one or more preset verification points located in the land area of ​​the mining area to be tested. The real-time dynamic location information of the preset verification points is configured to perform accuracy verification on the laser point cloud data and the single beam reflection data. Based on the laser point cloud data, the single-beam reflection data, and the real-time dynamic location information of the preset verification points, data fusion is performed to determine the digital elevation model of the mining area to be tested, so as to determine the subsidence status of the mining area to be tested based on the digital elevation model.

2. The method according to claim 1, characterized in that, The process of determining the flight trajectory of the UAV and the navigation trajectory of the unmanned surface vessel based on the coordinate system information and elevation datum of the mining area to be measured includes: Based on the preset positioning system, the plane coordinate system of the UAV and the unmanned vessel is determined as the first plane coordinate system, and the elevation datum of the UAV and the unmanned vessel is determined as the first elevation datum. Based on the meteorological and topographical conditions of the mining area to be tested, a common operating time benchmark is determined for the UAV and the unmanned vessel. Based on the first plane coordinate system, the first elevation datum, and the operation time datum, the flight trajectory and the navigation trajectory are planned.

3. The method according to claim 2, characterized in that, The first plane coordinate system is CGCS2000, and the first elevation datum is the 1985 National Elevation Datum.

4. The method according to claim 2, characterized in that, When the UAV flies along the flight path and the unmanned vessel travels along the navigation path, the acquisition of laser point cloud data of the land area in the mining area to be tested, single-beam reflection data of the underwater area in the mining area to be tested, and real-time dynamic location information of one or more preset verification points located on land in the mining area to be tested includes: Under suitable weather conditions, based on the operation time reference, the laser point cloud data is acquired while the UAV flies along the flight trajectory; Under the specified suitable weather conditions, based on the specified operation time reference, and while the unmanned vessel is navigating along the specified navigation trajectory, the single-beam reflection data is acquired. Under suitable weather conditions, based on the operation time reference, a preset real-time information acquisition device is controlled to acquire the real-time dynamic location information of the preset verification point.

5. The method according to claim 4, characterized in that, The method of acquiring laser point cloud data of the land area in the mining area to be tested, single-beam reflection data of the underwater area in the mining area to be tested, and real-time dynamic location information of one or more preset verification points located on land in the mining area to be tested, when the UAV flies along the flight trajectory and the unmanned vessel travels along the navigation trajectory, further includes: When the water area in the mining area to be tested meets the first preset conditions, the unmanned vessel is controlled to navigate along the navigation trajectory in order to acquire the single-beam reflection data under manual remote control.

6. The method according to claim 1, characterized in that, The step of performing data fusion based on the laser point cloud data, the single-beam reflection data, and the real-time dynamic location information of the preset verification points to determine the digital elevation model of the mining area to be measured includes: Based on the laser point cloud data, point cloud filtering is performed to determine the land area digital elevation model; Based on the single-beam reflection data, data correction and interpolation are performed to determine the digital elevation model of the water area. Data fusion is performed on the land digital elevation model and the water digital elevation model to determine the digital elevation model of the mining area to be measured.

7. The method according to claim 6, characterized in that, The step of performing point cloud filtering based on the laser point cloud data to determine the land area digital elevation model includes: The laser point cloud data is subjected to denoising and thinning processes to determine the denoised point cloud data; Based on the denoised point cloud data, the first point cloud filtering method is selected from a plurality of preset point cloud filtering methods; Based on the first point cloud filtering method, multiple ground point information is obtained according to the denoised point cloud data; Based on the ground point information, the land area digital elevation model is determined using Delaunay triangulation and linear interpolation.

8. The method according to claim 6, characterized in that, The step of performing data correction and error elimination based on the single-beam reflection data to determine the digital elevation model of the water area includes: Based on the single-beam reflection data, the positioning information of the unmanned vessel, and the depth correction information of the water area in the mining area to be tested, the underwater three-dimensional point cloud data is determined. Based on environmental characteristics and equipment attributes, the underwater 3D point cloud data is corrected and error is eliminated to determine the corrected underwater 3D point cloud data. Based on the underwater three-dimensional point cloud data and the real-time dynamic location information of the preset verification points, the first interpolation method is selected from multiple preset interpolation methods. Based on the first interpolation method, the digital elevation model of the water area is determined according to the corrected underwater three-dimensional point cloud data.

9. The method according to claim 6, characterized in that, The step of performing data fusion on the land digital elevation model and the water digital elevation model to determine the digital elevation model of the mining area to be measured includes: In the overlapping boundary area of ​​land and water in the mining area to be measured, based on the water digital elevation model, unreliable data in the land digital elevation model are replaced to determine a corrected land digital elevation model; Unify the grid spacing of the modified land digital elevation model and the water digital elevation model; A digital elevation model of the mining area to be measured is generated by performing data fusion through grid resampling and multiple preset interpolation methods.

10. The method according to any one of claims 1-9, characterized in that, The step of determining the subsidence status of the mining area block to be measured based on the digital elevation model includes: Obtain multiple sets of digital elevation models of the mining area to be measured at different times; Based on multiple sets of digital elevation models, differential calculations are performed to determine the subsidence distribution map of the mining area to be measured. Based on the subsidence distribution map, the subsidence characteristic parameters of the mining area to be tested are determined to determine the subsidence situation of the mining area to be tested, wherein the subsidence situation includes the subsidence, curvature, horizontal movement and deformation pattern of the mining area to be tested.

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