Mining area surface subsidence monitoring method and system for correcting LiDAR point cloud based on GNSS

Abstract

The invention relates to a mining area surface subsidence monitoring method and system based on a GNSS correction LiDAR point cloud, belongs to the technical field of mining area surface subsidence monitoring, and solves the problems of insufficient accuracy, comprehensiveness and efficiency of mining area surface subsidence monitoring in the prior art. The method comprises the following specific steps: acquiring GNSS observation data and scanned LiDAR point cloud of each scanning site of a to-be-measured mining area; correcting the scanned LiDAR point cloud in the same period by using a double-stage correction strategy based on the GNSS observation data in the previous period and the later period for the GNSS observation data and the scanned LiDAR point cloud with the unified coordinate system to obtain a corrected LiDAR point cloud; based on the corrected LiDAR point cloud monitoring data of the two stages, the surface subsidence monitoring result is obtained, and the precision, comprehensiveness and efficiency of mining area surface subsidence monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of mining, and more particularly to a method and system for monitoring surface subsidence in mining areas based on GNSS-corrected LiDAR point clouds. Background Technology

[0002] Mining area surface subsidence monitoring technology is the core prerequisite and key foundation for carrying out subsidence disaster early warning, implementing mining area ecological restoration and land reclamation, and ensuring safe production and sustainable development in mining areas.

[0003] Global Navigation Satellite System (GNSS) technology, with its significant advantages such as all-weather operation, high degree of automation, no line-of-sight between stations, and simultaneous determination of three-dimensional coordinates, has been widely used in mining subsidence monitoring and has achieved certain monitoring results. However, GNSS technology still has inherent technical shortcomings. The data it acquires is essentially still discrete "point" data. Even with increased density of observation points, it is difficult to achieve continuous and complete information coverage across the entire basin of the subsidence area. It cannot accurately capture the overall morphological characteristics and subtle deformation details of the subsidence basin, thus limiting its application value in in-depth analysis of the surface movement and deformation patterns across the entire basin.

[0004] In recent years, terrestrial three-dimensional laser scanning (TLS) technology has rapidly emerged as a new method for acquiring spatial data. Its core advantage lies in its ability to quickly and efficiently acquire dense point cloud data in a planar manner, enabling comprehensive and high-precision three-dimensional scanning of the observed object. However, LiDAR point cloud data itself is susceptible to factors such as scanning distance, environmental interference, and instrument errors, resulting in insufficient accuracy when directly used to construct digital elevation models (DEMs), making it difficult to meet the high-precision requirements of DEMs for mining area subsidence monitoring.

[0005] In summary, each existing single monitoring technology has its own technical shortcomings. Traditional point-based monitoring technologies suffer from incomplete data coverage and low efficiency; GNSS technology cannot acquire data from the entire basin; LiDAR point clouds face the challenges of complex data stitching and large errors; and existing elevation conversion methods lack accuracy and stability. These problems are intertwined, severely restricting the accuracy, comprehensiveness, and efficiency of surface subsidence monitoring in mining areas, and failing to meet the practical needs of current mining area disaster early warning, ecological environment reconstruction, and other aspects. Summary of the Invention

[0006] Based on the above analysis, the embodiments of the present invention aim to provide a method and system for monitoring surface subsidence in mining areas based on GNSS-corrected LiDAR point clouds, in order to solve the problems of insufficient accuracy, comprehensiveness and efficiency in the existing technology for monitoring surface subsidence in mining areas.

[0007] The objective of this invention is mainly achieved through the following technical solutions:

[0008] On one hand, embodiments of the present invention provide a method for monitoring surface subsidence in mining areas based on GNSS-corrected LiDAR point clouds, comprising the following steps: Acquire GNSS observation data and post-scanning LiDAR point cloud data from each scanning station in the mining area to be tested; For GNSS observation data of coordinate system one and scanned LiDAR point cloud, based on the GNSS observation data of the two periods before and after, a two-stage correction strategy is used to correct the scanned LiDAR point cloud of the same period to obtain the corrected LiDAR point cloud. Based on the corrected LiDAR point cloud monitoring data from the two periods, the surface subsidence monitoring results were obtained.

[0009] Furthermore, the two-stage correction includes registration optimization and global propagation of residuals; the registration optimization includes registering and optimizing the GNSS observation data and the scanned LiDAR point cloud from the two phases to obtain the registered residuals corresponding to each scanning station; The global propagation of residuals includes globally propagating the registration residuals of each scanning station, correcting each LiDAR point in the scanned LiDAR point cloud, obtaining the corresponding corrected LiDAR point coordinates, and forming the corrected LiDAR point cloud.

[0010] Furthermore, calibration is performed on any LiDAR point, including: Based on the distance between the LiDAR point and the GNSS observation data of each scanning station, the propagation weight of the LiDAR point for each registered residual is determined; The correction amount of the LiDAR points after global propagation is obtained from the registration residuals of all propagation weights and the corresponding scanning stations. Based on the correction amount and the LiDAR point coordinates, the corrected LiDAR point coordinates are obtained.

[0011] Furthermore, the correction weight of the LiDAR point to be corrected is inversely proportional to the distance between the LiDAR point to be corrected and the GNSS observation data of the corresponding scanning station.

[0012] Furthermore, the corresponding registration residuals are obtained, including: Find the nearest LiDAR corresponding point for each scanning station's GNSS observation data, and combine it with a preset distance threshold and normal vector angle threshold to remove LiDAR corresponding points that are greater than the distance threshold or normal vector angle threshold, thus obtaining the initial screening of LiDAR corresponding point cloud. The registration transformation model is used to transform the initial LiDAR point cloud with the same name. The goal is to minimize the sum of squared Euclidean distances between the transformed LiDAR points and the corresponding GNSS observation data. The registration optimization is carried out iteratively to obtain the coordinates of the transformed LiDAR points. The registered residuals are obtained by combining the corresponding GNSS observation data.

[0013] Furthermore, if the coordinate system of the GNSS observation data and the scanned LiDAR point cloud are not unified, coordinate unification is also performed based on the following process: Control stations were set up in the mining area. A leveling transformation model was constructed using GNSS observation data from each control station to convert the geodetic height in the GNSS observation data of each scanning station into normal height, thus obtaining GNSS data with unified elevation for each scanning station. After unifying the elevation of the scanning sites and inputting the GNSS data as known points into each lidar device for orientation calibration, simultaneous observations are performed with the GNSS devices to obtain GNSS observation data and scanned LiDAR point clouds in coordinate system one.

[0014] Furthermore, after obtaining the unified elevation GNSS data for each scanning station, the following data is obtained: By inputting the GNSS plane coordinates of each scanning station into the leveling transformation model, the corresponding geoid anomaly is obtained. Combined with the corresponding geodetic height in the GNSS observation data, the corresponding normal height is obtained.

[0015] Furthermore, the leveling transformation model is trained based on the following process: Leveling measurements were performed at each control station to obtain the normal height of each control station; The elevation anomaly of each control station is calculated based on the normal height of each control station and the geodetic height in the GNSS observation data. Using the normalized plane coordinates of each control station as input and the elevation anomaly of each control station as output, a regression function of a support vector machine is constructed using the radial basis function as the kernel function, and the leveling transformation model is obtained after training.

[0016] Furthermore, the surface subsidence monitoring results were obtained, including: Using the LiDAR point clouds after two periods of correction as the original data, elevation models were constructed using the Delaunay triangulation algorithm to obtain the elevation model values ​​of the early and late periods. Subtracting the later elevation model values ​​from the earlier elevation model values ​​yields the surface subsidence monitoring results for both periods of observation.

[0017] On the other hand, embodiments of the present invention provide a surface subsidence monitoring system for mining areas based on GNSS-corrected LiDAR point clouds, comprising: The acquisition module is set up at each scanning station and control station in the mining area to be measured, and is used to acquire GNSS observation data from each scanning station and control station in the mining area to be measured. The laser scanning acquisition module, set at the scanning site, includes a lidar device, used to synchronously scan the entire mining area under test with GNSS, and obtain the LiDAR point cloud of the mining area before and after the two phases of scanning. The laser scanning data correction module is used for GNSS observation data from two consecutive periods. It uses a two-stage correction strategy to correct the LiDAR point cloud after scanning in the same period to obtain the corrected LiDAR point cloud. The subsidence monitoring module is used to obtain surface subsidence monitoring results based on the corrected LiDAR point cloud monitoring data from two periods.

[0018] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. This invention proposes a combined GNSS and LiDAR point cloud monitoring technology, which combines the advantages of both GPS and LiDAR point clouds. GNSS provides a coordinate system connection for LiDAR point cloud scanning measurements, overcoming the problem of point cloud stitching. Furthermore, it precisely corrects the LiDAR point clouds obtained by TLS, using the corrected LiDAR point clouds to acquire full-basin data on surface subsidence in mining areas. This overcomes the shortcomings of GPS's "point" observation, forming a complementary advantage and improving the accuracy, comprehensiveness, and efficiency of surface subsidence monitoring.

[0019] 2. A two-stage calibration system for GNSS and LiDAR point clouds was constructed. Coarse calibration was achieved through rigid transformation and optimized registration to provide a reliable initial benchmark. Fine calibration was then performed using an error allocation strategy based on inverse range weighted interpolation, optimizing the residual distribution and effectively eliminating systematic and random errors in the LiDAR point cloud. The inverse range weighted interpolation strategy allows for adaptive calibration amounts to be applied to different terrains and mining areas, resulting in a significant improvement in accuracy compared to uncalibrated point clouds and providing high-quality data support for high-precision DEM construction.

[0020] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from the description and drawings, which are particularly pointed out. Attached Figure Description

[0021] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0022] Figure 1This is a flowchart of a mining area surface subsidence monitoring method based on GNSS-corrected LiDAR point cloud, according to an embodiment of the present invention. Figure 2 This is a structural block diagram of the unified coordinate system of LiDAR point clouds and GNSS at each scanning station in an embodiment of the present invention; Figure 3 This is a structural block diagram of the GNSS and LiDAR point cloud combined monitoring method according to an embodiment of the present invention. Detailed Implementation

[0023] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0024] Example 1 A specific embodiment of the present invention discloses a method for monitoring surface subsidence in mining areas based on GNSS-corrected LiDAR point clouds, such as... Figure 1 As shown, it includes the following steps: Step S1: Obtain GNSS observation data and scanned LiDAR point cloud data from each scanning station in the mining area to be tested; Step S2: Based on the GNSS observation data of coordinate system one and the scanned LiDAR point cloud, a two-stage correction strategy is used to correct the scanned LiDAR point cloud of the same period to obtain the corrected LiDAR point cloud. Step S3: Based on the corrected LiDAR point cloud monitoring data from the two periods, the surface subsidence monitoring results are obtained.

[0025] By using the above method, the LiDAR point cloud is corrected using GNSS observation data from each scanning station under a unified coordinate system. This effectively eliminates systematic and random errors, improves the reliability of the point cloud data, and then uses the corrected high-precision LiDAR point cloud for subsidence monitoring. This enables the construction of a high-precision DEM and improves the accuracy of subsequent subsidence monitoring.

[0026] Specifically, in step S1, lidar (TLS) scanning stations are deployed within the monitoring range of the mining area subsidence basin to scan and observe the surface of the mining area to be monitored. GNSS is used to observe each scanning station and each mining area control station to obtain the corresponding GNSS observation data, that is, to obtain the WGS-84 three-dimensional coordinates of each station.

[0027] The number of monitoring stations is as follows: For example, a point with a subsidence of 10 mm is taken as the outermost boundary of the subsidence basin. The monitoring range is determined by the boundary angle method or the probability integral method to ensure that the monitoring range can completely cover the entire subsidence-affected area. Scanning stations are evenly distributed within the monitoring range, and the number of monitoring stations is calculated using the following formula: , Where AB and CD represent the direction and dip lengths of the observation station, respectively, and r is the scanning radius of a single TLS station, determined according to the selected TLS equipment model. This ensures that the scanned point cloud data achieves complete and high-density coverage, providing a sufficient data foundation for subsequent digital elevation model (DEM) construction. At least 15 evenly distributed control stations in the mining area are selected to ensure coverage of the entire monitoring area and uniform distribution. The TLS equipment can be a ground-based 3D laser scanner or a UAV-based LiDAR scanner.

[0028] It should be noted that before using GNSS observation data to correct the scanned LiDAR point cloud, it is essential to ensure that the GNSS observation data and the scanned LiDAR point cloud are in the same coordinate system. If their coordinate systems are not consistent, such as... Figure 2 As shown, the process is as follows: First, a leveling transformation model is constructed using GNSS observation data from each control station. Based on this leveling transformation model, the geodetic height in the GNSS observation data from each scanning station is transformed into normal height, resulting in GNSS data with unified elevation.

[0029] Specifically, since the elevation obtained from GNSS observation data is geodetic height, while my country uses the orthographic height system, elevation unification needs to be achieved through GPS leveling conversion. This invention utilizes a leveling conversion model to fit a local quasi-geoid in the mining area. Specifically, the plane coordinates from the GNSS observation data of each scanning station are input into the leveling conversion model to obtain the corresponding geoid. Combined with the corresponding geodetic height from the GNSS observation data, the orthographic height corresponding to each scanning station is obtained.

[0030] For example, the leveling conversion model is trained using a Support Vector Machine (SVM) model. The specific fitting process includes: first, selecting ≥15 known control stations evenly distributed within the mining area. These points possess both GNSS-measured geodetic height and leveled normal height (obtained through third-order or higher leveling measurements to ensure elevation accuracy). Then, calculating the geoid anomaly ξ (the difference between geodetic height and normal height) for each control station, and normalizing the mining area-specific plane coordinates (x, y) of the control station to the [0, 1] interval to obtain the feature input. This normalization process improves the convergence speed and fitting accuracy of the model training. The SVM model is constructed using the geoid anomaly ξ as the output. The SVM model selects the Radial Basis Function (RBF) as the kernel function, which has good nonlinear fitting ability and generalization performance, expressed as: , in, Set the kernel function factor for the RBF kernel function. The constructed SVM model (i.e., the regression function) is expressed as: , The goal of the SVM model is to find a function f(X) such that the deviation between the predicted values ​​f(Xi) of all training samples and their true values, i.e., the elevation anomaly ξ, does not exceed ε. Simultaneously, the function should be as "flat" as possible, i.e., the norm w of the weight vector should be minimized to avoid overfitting. , b can be obtained by solving the following optimization problem: , , in, C represents the complexity of the model; C>0 is the penalty factor, which controls the penalty for samples outside the ε band. , ε is a slack variable that allows some samples to deviate; ε is an insensitive loss function parameter that defines the acceptable error range of the predicted value.

[0031] Furthermore, when it is necessary to unify the coordinate system to the mining area system, the WGS-84 three-dimensional coordinates of the GNSS observation data of the mining area control stations can be obtained. Based on the WGS-84 plane coordinates of each control station and the corresponding mining area-specific plane coordinates, a plane coordinate transformation model can be established. Through this plane transformation model, the WGS-84 plane coordinates of each scanning station can be converted into mining area-specific plane coordinates (i.e., mining area system plane coordinates), thus achieving the unification of the plane coordinate system.

[0032] Then, the unified elevation GNSS data is used as known points to input into each lidar device for orientation calibration, providing a unified coordinate reference for subsequent synchronous TLS scanning and GNSS observation.

[0033] For example, when setting up a ground-based radar scanning instrument in the field, a re-intersection method is used for station orientation. The 3D coordinates of the TLS (Thunder Detection and Control) device's location are extracted from the high-precision 3D coordinates of each scanning station and input into the TLS device. At least one other known coordinate backsight point is also referenced. After the device calculates and confirms the orientation accuracy, the TLS system scans using the mining area's dedicated coordinate system as a reference. Scanning the mine surface is performed at different stages of the working face advance. Since the coordinates of all scanning stations are already in a unified mining area-specific coordinate system, the point cloud data acquired by each station does not require post-processing stitching. Directly summarizing the scanned point clouds from all scanning stations achieves coordinate system unification, fundamentally avoiding stitching errors and significantly simplifying the data processing flow.

[0034] Specifically, in step S2, the GNSS observation data of coordinate system one and the scanned LiDAR point cloud are corrected using a two-stage correction strategy based on the GNSS observation data from both periods. This strategy includes two parts: registration optimization and residual global propagation; for example... Figure 3 As shown, the specific process is as follows: S21. The LiDAR point clouds from the two scanning periods are registered and optimized with the corresponding GNSS observation data to obtain the registration residuals for each scanning station. The specific steps are as follows: S211. Divide all scanning stations into registration points and check points according to the proportion, and construct the corresponding point relationship between the scanned LiDAR point cloud and the GNSS observation data of the corresponding scanning station based on the registration points. For example, in point cloud processing software, the terrain feature point cloud corresponding to the scanning station is manually identified by magnifying the point cloud data, ensuring that the extracted point cloud can accurately reflect the actual location of the control point and form a scanning station-corresponding point cloud relationship pair.

[0035] S212. Perform registration optimization on the same point cloud pairs to initially eliminate system errors and obtain the optimal matching of the same points and the corresponding registration residuals.

[0036] Specifically, the registration optimization of corresponding point cloud pairs is performed using a nearest-point iterative algorithm. First, the KD-Tree algorithm is used to quickly find the nearest LiDAR point corresponding to the GNSS observation data of each scanning station, reducing computation and improving iteration efficiency. Next, erroneous point pairs are eliminated by setting distance and normal vector angle thresholds. Pairs exceeding the thresholds or with excessively large differences in normal vector angles are removed to avoid anomalies affecting the transformation parameter solution. The corresponding points of each scanning station form the initial screening of LiDAR corresponding point clouds. Then, a registration transformation model is constructed based on the initial screening of LiDAR corresponding point clouds. The scanned LiDAR point clouds are rotated and translated. By minimizing the sum of squared Euclidean distances between the "transformed LiDAR corresponding points" and the "GNSS observation data of each scanning station," the optimal rotation matrix R and translation vector t are solved, as shown in the following formula: , Among them, P i Let q be the coordinates of the corresponding LiDAR point. i For GNSS observation data corresponding to the scanning station, | |2 represents the Euclidean norm, R is the rotation matrix to be determined, and t is the translation vector.

[0037] During the solution process, the rotation matrix R is initialized as the identity matrix and the translation vector t is initialized as the zero vector. The optimal R and t for each iteration are solved by singular value decomposition (SVD), the LiDAR point cloud coordinates are updated, and the sum of squared Euclidean distances is recalculated. The iteration process is repeated until the difference between the sum of squared distances of two iterations is less than a set threshold or the maximum number of iterations is reached. The iteration is then stopped, and the optimized LiDAR corresponding point cloud and the optimal corresponding point relationship pair are obtained. Then, the registration residual is obtained by the difference between the GNSS observation data of each scanning station and the registered point cloud coordinates.

[0038] S213. Accuracy assessment and scanning station optimization based on registration residuals. The optimization goal is to achieve the best global correction effect with the fewest scanning stations. Correction accuracy is verified using independent checkpoints (GNSS observation data not involved in corresponding point pairing). Overall accuracy is assessed by calculating the mean square error (RMSE) of the corrected LiDAR point cloud corresponding to each checkpoint, using the following formula: , Analyze the residual distribution of each checkpoint. If the residual of a certain checkpoint is significantly higher than that of other points, it is identified as an abnormal control point and removed. By comparing the correction accuracy of different numbers of checkpoints, determine the optimal number of scanning stations. That is, use the fewest scanning stations to achieve the accuracy requirements, ensuring that the control points can cover the entire area while maintaining accuracy. Finally, obtain the "optimal set of scanning stations" and the corresponding residual values. These residual values ​​reflect the remaining error of the LiDAR point cloud at the scanning stations, providing a basis for subsequent global interpolation correction.

[0039] S22. Propagate the registration residuals of each scanning station globally, correct each LiDAR point in the scanned LiDAR point cloud, obtain the corresponding corrected LiDAR point coordinates, and form the corrected LiDAR point cloud.

[0040] Specifically, an inverse distance-weighted interpolation strategy is used for correction. The aim is to propagate the residual interpolation at each scanning station to the entire LiDAR point cloud, thereby correcting global systematic errors. The correction for any LiDAR point in the scanned LiDAR point cloud is performed based on the following process: S221. Based on the distance between the LiDAR point and the GNSS observation data of each scanning station, determine the propagation weight of the LiDAR point for each registered residual; wherein, the correction weight of the LiDAR point is inversely proportional to the distance between the LiDAR point and the corresponding GNSS observation data of the scanning station; S222. The correction amount of LiDAR points after global propagation is obtained from the registration residuals of all propagation weights and corresponding scanning stations. S223. Based on the correction amount and the LiDAR point coordinates, obtain the corrected LiDAR point coordinates.

[0041] For example, let the difference between the GNSS observation data of each scanning station after registration and the coordinates of the point cloud after registration (i.e., the residual after registration) be expressed as: , in, For the registration residual of scan site i, , These are the GNSS observation data and the registered point cloud coordinates of scanning station i, respectively.

[0042] For any point O(x,y) to be corrected in the scanned point cloud, the correction amount is obtained by the weighted average of the residuals from each scanning station, expressed as: , , In the formula, It is the planar distance from the point O to be calibrated to the i-th scanning station. It is the propagation weight of the residual after registration of point O with respect to the i-th scanning station. It is an adjustable parameter. Let be the error correction amount for point O, and N be the total number of scanning stations. The final corrected coordinates of point O are expressed as: .

[0043] Through the above correction process, using GNSS coordinates as the "true value," a correspondence between them and LiDAR point clouds is established. An inverse distance-weighted interpolation strategy is used to correct coordinate deviations in each LiDAR point cloud, ensuring that the point cloud data perfectly matches the spatial positions of real terrain and features. Ultimately, this achieves "point cloud accuracy and GNSS accuracy sharing the same source." By using the spatial reference provided by high-precision GNSS control stations, systematic errors (such as instrument calibration errors and coordinate system offsets) and random errors (such as noise caused by environmental interference) inherent in the LiDAR point clouds are eliminated, improving the absolute accuracy of the point cloud data and laying the foundation for subsequent high-precision digital elevation models (DEMs).

[0044] Specifically, in step S3, an elevation model (DEM) is constructed based on the corrected LiDAR point cloud monitoring data from the two periods using the triangular network of irregular networks (TIN) interpolation method for subsidence monitoring. The specific steps are as follows: Using corrected LiDAR point clouds as raw data, and based on the three-dimensional coordinates of the point clouds, the Delaunay triangulation algorithm is used to connect discrete point clouds into an irregular network of non-overlapping triangles covering the entire area. Using the vertices of each triangle, elevation interpolation is performed on the raster nodes within the triangle's coverage area, ultimately generating a regular raster-based DEM. By subtracting the previous DEM from the subsequent one, the dynamic surface subsidence value between the two observation periods is obtained, forming a dynamic surface subsidence basin. This subsidence basin data can intuitively and accurately reflect the spatial distribution characteristics and temporal evolution of surface subsidence in the mining area during the monitoring period.

[0045] Compared with existing technologies, this embodiment provides a method for monitoring surface subsidence in mining areas based on GNSS-corrected LiDAR point clouds. By leveling transformation, the elevation of GNSS observation data is unified. Based on the unified elevation GNSS coordinate system, the data is input into the laser scanning technology TLS system during scanning measurement, incorporating the scanned point cloud data into the unified coordinate system. Then, the accuracy of the scanned LiDAR point cloud is corrected using GNSS observation data measured at the same time. A digital elevation model (DEM) of the subsidence area at a certain mining time is established through the calibrated point cloud. Subsidence parameters are obtained through multiple DEM data, ultimately improving the accuracy and efficiency of mining area subsidence monitoring.

[0046] Example 2 Another specific embodiment of the present invention discloses a surface subsidence monitoring system for mining areas based on GNSS-corrected LiDAR point clouds, comprising: The acquisition module is set up at each scanning station and control station in the mining area to be measured, and is used to acquire GNSS observation data from each scanning station and each control station. The laser scanning acquisition module, set at the scanning site, includes a lidar device, used to synchronously scan the entire mining area under test with GNSS, and obtain the LiDAR point cloud of the mining area before and after the two phases of scanning. The laser scanning data correction module is used for GNSS observation data from two consecutive periods. It uses a two-stage correction strategy to correct the LiDAR point cloud after scanning in the same period to obtain the corrected LiDAR point cloud. The subsidence monitoring module is used to obtain surface subsidence monitoring results based on the corrected LiDAR point cloud monitoring data from two periods.

[0047] The system can be used to monitor surface subsidence according to any of the methods described in Embodiment 1. Related aspects can be referenced from each other, but are not repeated in this embodiment.

[0048] Compared with existing technologies, this embodiment provides a surface subsidence monitoring system for mining areas based on GNSS-corrected LiDAR point clouds. Through the coordinated operation of various modules, it accurately monitors surface subsidence in the mining area under test, demonstrating feasibility and significant application prospects in mining areas. It provides reliable basic data for land reclamation, environmental reconstruction, and ecological restoration in mining areas, and can serve as a tool for predicting and forecasting mining subsidence disasters.

[0049] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0050] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring surface subsidence in mining areas based on GNSS-corrected LiDAR point clouds, characterized in that, Includes the following steps: Acquire GNSS observation data and post-scanning LiDAR point cloud data from each scanning station in the mining area to be tested; For GNSS observation data of coordinate system one and scanned LiDAR point cloud, based on the GNSS observation data of the two periods before and after, the scanned LiDAR point cloud of the same period is corrected by two-stage correction to obtain the corrected LiDAR point cloud. Based on the corrected LiDAR point cloud monitoring data from the two periods, the surface subsidence monitoring results were obtained.

2. The method according to claim 1, characterized in that, The two-stage correction includes registration optimization and global propagation of residuals; the registration optimization includes registering and optimizing the GNSS observation data and the scanned LiDAR point cloud from the two phases to obtain the registration residuals corresponding to each scanning station; The residual global propagation includes globally propagating the registration residuals of each scanning station, correcting each LiDAR point in the scanned LiDAR point cloud, obtaining the corresponding corrected LiDAR point coordinates, and forming the corrected LiDAR point cloud.

3. The method according to claim 2, characterized in that, Correction is performed on any LiDAR point, including: Based on the distance between the LiDAR point and the GNSS observation data of each scanning station, the propagation weight of the LiDAR point for each registered residual is determined; The correction amount of the LiDAR points after global propagation is obtained from the registration residuals of all propagation weights and the corresponding scanning stations. Based on the correction amount and the LiDAR point coordinates, the corrected LiDAR point coordinates are obtained.

4. The method according to claim 3, characterized in that, The correction weight of the LiDAR point to be corrected is inversely proportional to the distance between the LiDAR point to be corrected and the GNSS observation data of the corresponding scanning station.

5. The method according to claim 3, characterized in that, The corresponding registration residuals are obtained, including: Find the nearest LiDAR corresponding point for each scanning station's GNSS observation data, and combine it with a preset distance threshold and normal vector angle threshold to remove LiDAR corresponding points that are greater than the distance threshold or normal vector angle threshold, thus obtaining the initial screening of LiDAR corresponding point cloud. The registration transformation model is used to transform the initial LiDAR point cloud with the same name. The goal is to minimize the sum of squared Euclidean distances between the transformed LiDAR points and the corresponding GNSS observation data. The registration optimization is carried out iteratively to obtain the coordinates of the transformed LiDAR points. The registered residuals are obtained by combining the corresponding GNSS observation data.

6. The method according to any one of claims 1-5, characterized in that, If the coordinate system of the GNSS observation data and the scanned LiDAR point cloud is not consistent, coordinate unification is also performed based on the following process: Control stations were set up in the mining area. A leveling transformation model was constructed using GNSS observation data from each control station to convert the geodetic height in the GNSS observation data from each scanning station into normal height, thus obtaining GNSS data with unified elevation for each scanning station. After unifying the elevation of the scanning sites and inputting the GNSS data as known points into each lidar device for orientation calibration, simultaneous observations were performed with the GNSS devices to obtain GNSS observation data and scanned LiDAR point clouds in coordinate system one.

7. The method according to claim 6, characterized in that, After obtaining the unified elevation of each scanning station, the GNSS data includes: By inputting the GNSS plane coordinates of each scanning station into the leveling transformation model, the corresponding geoid anomaly is obtained. Combined with the corresponding geodetic height in the GNSS observation data, the corresponding normal height is obtained.

8. The method according to claim 7, characterized in that, The leveling transformation model is trained based on the following process: Leveling measurements were performed at each control station to obtain the normal height of each control station; The elevation anomaly of each control station is calculated based on the normal height of each control station and the geodetic height in the GNSS observation data. Using the normalized plane coordinates of each control station as input and the elevation anomaly of each control station as output, a regression function of a support vector machine is constructed using the radial basis function as the kernel function, and the leveling transformation model is obtained after training.

9. The method according to claim 1, characterized in that, The results of surface subsidence monitoring obtained include: Using the LiDAR point clouds after two periods of correction as the original data, elevation models were constructed using the Delaunay triangulation algorithm to obtain the elevation model values ​​of the early and late periods. Subtracting the later elevation model values ​​from the earlier elevation model values ​​yields the surface subsidence monitoring results for both periods of observation.

10. A surface subsidence monitoring system for mining areas based on GNSS-corrected LiDAR point clouds, characterized in that, include: The acquisition module is set up at each scanning station and control station in the mining area to be measured, and is used to acquire GNSS observation data from each scanning station and control station in the mining area to be measured. The laser scanning acquisition module, set at the scanning site, includes a lidar device, used to synchronously scan the entire mining area under test with GNSS, and obtain the LiDAR point cloud of the mining area before and after the two phases of scanning. The laser scanning data correction module is used for GNSS observation data from two consecutive periods. It uses a two-stage correction strategy to correct the LiDAR point cloud after scanning in the same period to obtain the corrected LiDAR point cloud. The subsidence monitoring module is used to obtain surface subsidence monitoring results based on the corrected LiDAR point cloud monitoring data from two periods.

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