Mining area surface movement deformation prediction method, device and equipment based on airborne LiDAR and storage medium

Abstract

The invention belongs to the field of geological survey, and particularly relates to a mining area earth surface movement deformation prediction method, device and equipment based on airborne LiDAR and a storage medium, and the method comprises the steps: collecting original point cloud data of a target mining area earth surface; extracting point cloud data of a non-growth type fixed ground object based on the echo characteristics of the airborne LiDAR; traversing the preprocessed point cloud data of the non-growing fixed ground features by adopting a moving window method to determine surface characteristic parameters, determining a plurality of subsidence monitoring points of the target mining area and acquiring working face parameters of the subsidence monitoring points, and performing probability integral method parameter inversion by combining a genetic algorithm to obtain probability integral parameters; and constructing a settlement prediction model for surface deformation prediction by using the probability integral parameters, the surface characteristic parameters and the working face parameters. According to the method, settlement monitoring of non-growing fixed ground objects on the mountainous area surface and rapid calculation of characteristic parameters of any point on the mountainous area surface are achieved, and the method has certain reference value and practical significance for monitoring and prediction of mountainous area landform settlement.

Description

Technical Field

[0001] The present invention belongs to the field of geological survey, and in particular relates to a method, device, equipment and storage medium for predicting surface movement and deformation in a mining area based on airborne LiDAR. Background Art

[0002] With the continuous growth of my country's demand for coal resources, coal mining activities have gradually shifted to the western mining areas with more complex geological conditions. However, the mining areas have undulating terrain and complex geological structures. Underground coal mining is not only prone to damage to surface buildings (structures), but also prone to secondary disasters such as landslides and collapses, which seriously expand the scope and degree of impact of mining. Moreover, such disasters are highly unpredictable. According to statistics, more than one-third of my country's coal mines are located in mining areas, which makes the contradiction between coal mining in mining areas and surface safety increasingly acute.

[0003] However, the existing surface subsidence monitoring technology still has the following defects:

[0004] 1. At present, surface subsidence monitoring is mainly divided into two types: point data monitoring and surface data monitoring. Point data monitoring mainly includes leveling, trigonometric height measurement and GPS height measurement. However, these methods have significant limitations in mining applications: Traditional leveling: Due to the complex terrain in the mining area, the measurement is difficult, the monitoring location is limited, and there is a high risk. GPS height measurement: Affected by the terrain and vegetation in the mining area, the GPS signal is relatively weak, and the monitoring area and accuracy are greatly limited. Surface data monitoring methods such as InSAR and LiDAR, although they can realize the monitoring of two-dimensional surface data, they also face challenges: InSAR technology: Imaging conditions are easily affected by the environment, and the accuracy of large deformation monitoring is low. LiDAR technology: Although it has high accuracy, comprehensive data collection and strong flexibility, the amount of monitoring data is large and the data quality evaluation is difficult, which restricts its widespread application.

[0005] 2. Research on the mechanism and prediction of surface movement and deformation caused by coal mining in mining areas has achieved certain results. However, although the existing prediction model has high computational efficiency and simple prediction function, it requires a large number of accurate surface characteristic parameters, which are complex and time-consuming to obtain, limiting the wide application of the model. Summary of the invention

[0006] The purpose of the present invention is to provide a method, device, equipment and storage medium for predicting surface movement and deformation in mining areas based on airborne LiDAR to solve the problems raised in the background technology.

[0007] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0008] In a first aspect, the present invention provides a method for predicting surface movement and deformation in a mining area based on airborne LiDAR, the method comprising:

[0009] Use airborne LiDAR technology to scan the target mining area and collect the original point cloud data of the target mining area;

[0010] Processing the point cloud data based on the echo characteristics of the airborne LiDAR to extract point cloud data of non-growing fixed objects;

[0011] The moving window method is used to traverse the pre-processed point cloud data of the non-growing fixed object to determine the surface characteristic parameters, wherein the surface characteristic parameters include the tilt direction angle, the surface point inclination angle and the surface characteristic coefficient;

[0012] Based on the point cloud data of non-growing fixed objects, several subsidence monitoring points in the target mining area are determined and their working face parameters are obtained. The probability integral method parameter inversion is performed with the genetic algorithm to obtain the probability integral parameters.

[0013] A settlement prediction model is constructed by using the probability integral parameter, the surface characteristic parameter, and the working face parameter, and the settlement prediction model is used to predict surface deformation of the mining area surface point cloud data acquired in real time.

[0014] Furthermore, the processing of the point cloud data to extract the point cloud data of the non-growing fixed features includes:

[0015] Divide the surface point cloud data into grids, and record the number of point clouds and the number of echo points in each grid;

[0016] Threshold conditions are set, and grids that meet the threshold conditions are regarded as non-fixed objects and filtered using the PCL slope method to extract point cloud data of non-growing fixed objects, wherein the growing fixed objects include roads, building tops and bare soil.

[0017] Furthermore, the method of using a moving window method to traverse the point cloud data of the pre-processed non-growing fixed objects to determine the surface characteristic parameters includes: using a moving window with a set grid spacing to traverse the point cloud data of the pre-processed non-growing fixed objects, and calculating the tilt direction angle, the surface inclination angle and the surface characteristic coefficient in each window, as shown in the following formula:

[0018] (1) Tilt angle Find:

[0019]

[0020] (2) Surface inclination angle α x,y Find:

[0021]

[0022] (3) Surface characteristic coefficient D x,y Find:

[0023] D x,y =-200*((d+f+b+h) / 2-2*e) / ds 2

[0024] In the above formula, a, b, c, d, e, f, g, h, i are the minimum values ​​of the point cloud elevation in the corresponding grid, ds is the grid spacing, dx and dy are the differentials along the x and y directions, respectively.

[0025] Furthermore, the point cloud data based on the non-growing fixed features is used to determine several subsidence monitoring points in the target mining area and obtain the working face parameters thereof, and the probability integral method parameter inversion is performed in combination with the genetic algorithm to obtain the probability integral parameters, including:

[0026] Compare the point cloud data of non-growing fixed objects at different time points, analyze the deformation of the surface, identify the subsidence area, determine several subsidence monitoring points and obtain their working surface parameters;

[0027] The working surface parameters of the subsidence monitoring points are used as input data to perform parameter inversion using a probability integral method, and the probability integral parameters of several subsidence monitoring points are solved.

[0028] Furthermore, the working face parameters include corner points, coal seam inclination, and mining thickness; the probability integral parameters include sinking coefficient q, horizontal movement coefficient b, main influencing angle tangent tanβ, mining propagation angle θ, and upper left and lower right inflection point offset s.

[0029] Furthermore, the probability integral parameter, the surface characteristic parameter, and the working face parameter are used to construct a settlement prediction model, and the settlement prediction model is used to predict surface deformation of the mining area surface point cloud data acquired in real time, including:

[0030] Loading a working face prediction file containing probability integral parameters, surface characteristic parameters, and working face parameters into a data processing system, and initializing a settlement prediction model according to the parameters in the prediction file;

[0031] Airborne LiDAR monitoring equipment is used to regularly obtain real-time original surface point cloud data of the mining area, and the pre-processed real-time point cloud data is input into the settlement prediction model. The surface deformation of mining activities in real time is simulated in the data processing system, and the surface deformation prediction results including surface settlement maps and horizontal movement maps are output.

[0032] In a second aspect, the present invention proposes a device for predicting surface movement and deformation in a mining area based on airborne LiDAR, the device comprising:

[0033] The data acquisition module is used to scan the target mining area using airborne LiDAR technology and collect the original point cloud data of the target mining area;

[0034] A data processing module, used to process the point cloud data based on the echo characteristics of the airborne LiDAR, and extract the point cloud data of the non-growing fixed objects;

[0035] A parameter extraction module, for traversing the pre-processed point cloud data of non-growing fixed objects using a moving window method to determine surface characteristic parameters, wherein the surface characteristic parameters include a tilt direction angle, a surface point inclination angle, and a surface characteristic coefficient;

[0036] The parameter inversion module is used to determine several subsidence monitoring points in the target mining area based on the point cloud data of non-growing fixed objects and obtain the parameters of their working surface, and to perform probability integral method parameter inversion in combination with genetic algorithm to obtain probability integral parameters;

[0037] The real-time prediction module is used to construct a settlement prediction model using the probability integral parameter, the surface characteristic parameter, and the working face parameter, and use the settlement prediction model to predict surface deformation of the mining area surface point cloud data acquired in real time.

[0038] Furthermore, the data acquisition module includes a drone equipped with an airborne LiDAR device, which is used to perform aerial scanning of the target mining area and transmit the acquired original point cloud data of the mining area surface to the data processing module in real time.

[0039] In a third aspect, the present invention provides an electronic device, comprising:

[0040] A processor; a memory for storing instructions executable by the processor;

[0041] The processor is configured to execute the instructions to implement the prediction method as described in any one of the above items.

[0042] In a fourth aspect, the present invention proposes a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform any of the prediction methods described above.

[0043] The beneficial effects of the present invention are:

[0044] In order to solve the problem that the amount of data obtained by airborne LiDAR is large and the data quality is difficult to evaluate, the present invention proposes a point cloud extraction method for non-growing fixed objects in mountainous areas based on echo characteristics, thereby realizing settlement monitoring of non-growing fixed objects on the surface of mountainous areas and improving monitoring accuracy; and in view that the surface movement and deformation prediction model of mining areas requires a large number of surface characteristic parameters, a point cloud computing method based on a moving window is proposed, which realizes the rapid calculation of characteristic parameters of any point on the surface of mountainous areas, and has certain reference value and practical significance for monitoring and prediction of terrain subsidence in mountainous areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of a method for predicting surface movement and deformation in mining areas based on airborne LiDAR in the present invention.

[0046] Figure 2 Another schematic diagram of the process of predicting surface movement and deformation in mining areas based on airborne LiDAR in the present invention.

[0047] Figure 3 It is a schematic diagram of multiple echoes of an airborne LiDAR system in a specific implementation manner of the present invention.

[0048] Figure 4 The color-recovered point cloud and echo ratio threshold diagram of Experiment 1 in the specific implementation manner of the present invention; wherein, (a) is the color-recovered point cloud of Experiment 1, (b) is the echo ratio threshold of 0%, (c) is the echo ratio threshold of 10%, and (d) is the echo ratio threshold of 20%.

[0049] Figure 5 The diagram shows the color-recovered point cloud and echo ratio threshold value of Experiment 2 in a specific implementation manner of the present invention; wherein, (a) is the color-recovered point cloud of Experiment 2, (b) is the echo ratio threshold value of 0%, (c) is the echo ratio threshold value of 10%, and (d) is the echo ratio threshold value of 20%.

[0050] Figure 6 It is a schematic diagram of a virtual grid in a specific implementation manner of the present invention.

[0051] Figure 7 It is a schematic diagram of a moving window in a virtual grid in a specific implementation manner of the present invention.

[0052] Figure 8 This is a relative position diagram of the working surface and the monitoring points in the actual processing routine of the specific implementation method of the present invention.

[0053] Fig. 9 This is a distribution map of point cloud extraction of non-growing fixed objects on the working surface in the actual processing routine of the specific implementation method of the present invention.

[0054] Fig.10This is a comparison chart of the subsidence measured by leveling and the subsidence extracted by point cloud in the actual processing routine of the specific implementation method of the present invention.

[0055] Fig.11 This is a distribution diagram of points extracted from the point cloud of working face parameter inversion adopted in the actual processing routine of the specific implementation method of the present invention.

[0056] Fig.12 This is a distribution diagram of leveling monitoring points for working face parameter inversion that has been adopted in the actual processing routine of the specific implementation method of the present invention.

[0057] Fig.13 It is the estimated fitting sinking curve of the point cloud extraction point in the parameter solution in the actual processing routine of the specific embodiment of the present invention.

[0058] Fig.14 It is the estimated fitting subsidence curve of the level monitoring point in the parameter solution in the actual processing routine of the specific implementation method of the present invention.

[0059] Fig.15 It is a surface inclination distribution diagram in the actual processing routine of the surface characteristic parameters and the predicted subsidence contour distribution diagram in the specific implementation method of the present invention.

[0060] Fig.16 It is a distribution diagram of the tilt direction angle in the actual processing routine of the surface characteristic parameters and the predicted subsidence contour line distribution diagram in the specific implementation method of the present invention.

[0061] Fig.17 It is a distribution diagram of surface characteristic coefficients in the actual processing routine of the specific implementation method of the present invention and the distribution diagram of surface characteristic parameters and expected subsidence contour lines.

[0062] Fig.18 It is a predicted subsidence contour line distribution map in the actual processing routine of the surface characteristic parameters and the predicted subsidence contour line distribution map in the specific implementation method of the present invention. DETAILED DESCRIPTION

[0063] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0064] Example 1

[0065] like Figure 1-2 As shown, this embodiment proposes a method for predicting surface movement and deformation in a mining area based on airborne LiDAR (laser radar), and the method includes the following steps:

[0066] S1. Use airborne LiDAR technology to scan the target mining area and collect the original point cloud data of the target mining area;

[0067] S2, processing the point cloud data based on the echo characteristics of the airborne LiDAR to extract the point cloud data of non-growing fixed objects;

[0068] S3, using a moving window method to traverse the pre-processed point cloud data of non-growing fixed objects to determine surface characteristic parameters, the surface characteristic parameters including the tilt direction angle, the surface point inclination angle and the surface characteristic coefficient;

[0069] S4. Determine several subsidence monitoring points in the target mining area based on the point cloud data of non-growing fixed objects and obtain their working face parameters, and perform probability integral method parameter inversion in combination with genetic algorithm to obtain probability integral parameters;

[0070] S5. Construct a settlement prediction model using probability integral parameters, surface characteristic parameters, and working face parameters, and use the settlement prediction model to predict surface deformation of the mining area surface point cloud data acquired in real time.

[0071] In this embodiment, due to the complex terrain of mountainous areas, traditional filtering methods are difficult to effectively deal with this special terrain. At the same time, the study of surface deformation laws requires multi-period data. The existence of time intervals will lead to tree growth and crop replacement, which will further reduce the reliability of point cloud processing results. In comparison, non-growing fixed objects in mining areas (such as roads, building tops, bare soil and other surface data) are less affected by time differences, so they are more reliable as basic data. Therefore, this embodiment proposes a point cloud extraction method for non-growing fixed objects based on the echo characteristics of airborne LiDAR.

[0072] Understandably, combined Figure 3 The basic principle of airborne LiDAR technology is to calculate the distance of the target object by emitting laser pulses and recording the time difference from the emission to the reception of the laser pulses. Laser has strong penetrability. When the laser is irradiated to the ground, it will penetrate certain objects and generate multiple reflected laser pulse signals. The reflection frequency varies significantly depending on factors such as the type, structure and material of the object. For example, when the laser signal penetrates vegetation, multiple echoes are usually generated; when the laser signal is irradiated to a building or road with a smooth surface, only a single echo is usually generated. By analyzing the echo information, it can help determine the type of point cloud.

[0073] Further preferably, the point cloud data is processed to extract the point cloud data of non-growing fixed features, including: gridding the surface point cloud data and recording the number of point clouds and the number of echo points in each grid; setting a threshold condition (echo ratio threshold), treating the grids that meet the threshold condition as non-fixed features and filtering them using the PCL slope method to extract the point cloud data of non-growing fixed features, wherein the growing fixed features include roads, building tops and bare soil.

[0074] More specifically, combined Figure 4 According to the echo characteristics of the point cloud, the point cloud is first divided into grids, the total number of point clouds M in the grid is recorded, and the number of echo points N in the grid is determined, and their proportion is calculated. If the proportion reaches a certain threshold (N / M ≥ 10%), the grid is filtered as a non-fixed feature. According to the probability integral method, the trend of surface subsidence is continuous. At the same time, according to the third law of geography (the more similar the geographical environment, the closer the geographical target characteristics), when expressing the subsidence trend, the subsidence amount of the mining area surface in a certain area can be regarded as the same. The virtual grid is composed of adjacent small area grids, so the subsidence amount in the grid is generally consistent, so the grid can be averaged.

[0075] The echo ratio threshold is determined based on the following experiments 1 and 2:

[0076] The key to extracting point cloud data of non-growing objects is to determine the threshold of the echo ratio within the grid. In order to determine the threshold of the echo ratio, the experiment selected the area near a university teaching building in the plain area as the research object (Experiment 1), and used Pegasus D2000 to orthophototically obtain LiDAR point clouds. The total number of points is 8569590, the average point cloud density is 95 points / m2, and the first echo accounts for 94.51%. Figure 4 As shown in (a), the experimental area contains non-growing fixed objects such as roads, building tops, and bare soil. The echo ratio thresholds of 0%, 10%, and 20% are set for the experimental area to extract the point cloud of non-growing fixed objects. The results are shown in Figure 4 (b), (c), (d).

[0077] The results of Experiment 1 show that the echo ratio threshold of 0%, 10%, and 20% can all achieve the extraction of fixed objects, but under the 0% condition, some bare soil data will be lost, while the data extracted from 10% to 20% tend to be consistent and have comparable accuracy. Therefore, considering the time cost and spatial complexity of the calculation, it is considered more reasonable to set the echo ratio threshold at 10% in the plain area.

[0078] Considering that different terrain conditions may affect the echo ratio threshold setting, the experiment further selected the LiDAR point cloud obtained above a working surface in the mountainous area (Experiment 2). The total number of points is 4527915, the average point cloud density is 15 points / m2, and the first echo accounts for 95.13%. Figure 5 As shown in (a), the experimental area contains non-growing fixed objects such as snow-covered roads and bare soil. Similarly, the echo ratio thresholds of 0%, 10%, and 20% are set to extract the point cloud of non-growing fixed objects. The results are shown in Figure 5 (b), (c), (d).

[0079] The results of Experiment 2 show that: the snow-covered road extraction is incomplete at the echo ratio threshold of 0%, and some bare soil data is missing; while the snow-covered road extraction is basically complete at the echo ratio threshold of 10%, and some bare soil data is lost; and the snow-covered road extraction is more complete at 20%, and some bare soil data is lost less, but the amount of extracted data does not change significantly compared with the 10% case. Therefore, under mountainous terrain conditions, the echo ratio threshold is set to 10% and can still meet the basic point cloud extraction of non-growing fixed objects.

[0080] In summary, when the echo ratio threshold is set at 10%, shrubs, crops and other objects in the point cloud can be removed regardless of the plain or mountainous areas, thereby obtaining data on non-growing fixed objects such as roads, building tops, and bare soil.

[0081] In this embodiment, the point cloud data of the monitoring range of the mining area is obtained to determine the scope of the sinking basin. Specifically, the initial point cloud data is processed using a small-scale grid spacing (ds = 1m), and the point cloud data of non-growing fixed objects such as roads, building tops, and bare soil are obtained by combining the airborne LiDAR echo characteristics and the PCL slope method. The subtraction basin is obtained by subtracting the two periods of fixed object data.

[0082] It can be understood that in the case of mining horizontal and gently inclined coal seams with an average slope angle of less than 30°, the subsidence value W at any point in the mountainous area is s The (x,y) prediction formula is as follows:

[0083]

[0084] Where W(x,y) refers to the subsidence displacement of any point (x,y) on flat ground under the same geological and mining technology conditions. x,y is the surface characteristic coefficient of point (x,y). P[x] and P[y] are the slip influence functions of the strike and dip main sections. φ is the tilt direction angle (°) of point (x,y). α x,y is the inclination angle (°) of the surface point (x,y).

[0085] Among them, the sliding influence function of the point (x, y) along the x direction is:

[0086]

[0087] The sliding influence function of a point (x,y) along the y direction is:

[0088]

[0089] In the above formula, r is the main influence radius, W max is the maximum sinking value under flat ground conditions, and A, t, and P are the slip influencing parameters.

[0090] From the above content, it can be seen that the premise of accurately predicting the surface movement and deformation in mountainous areas is not only to obtain the predicted parameters of the prediction model, but also the surface characteristic parameters (surface characteristic coefficient D x,y , tilt direction angle Inclination angle of ground surface point α x,y ) also needs to be calculated accurately.

[0091] Combination Figure 7 In order to simply and quickly obtain the surface characteristic parameters required for the above mountain area prediction, according to the concept of virtual grid in 1.1, a 3*3 moving window is used to traverse the LiDAR point cloud and calculate the mountain area prediction parameters, where a, b, c, d, e, f, g, h, i are the minimum values ​​of the point cloud elevation in the corresponding grid, and ds is the grid spacing. Since the survey area is much larger than the working surface range, the problem of the unmonitored basin boundary caused by surface movement caused by underground coal mining can be avoided.

[0092] Further preferably, a moving window method is used to traverse the point cloud data of the pre-processed non-growing fixed features to determine the surface characteristic parameters, including: using a moving window with a set grid spacing to traverse the point cloud data of the pre-processed non-growing fixed features, and calculating the tilt direction angle, the surface inclination angle and the surface characteristic coefficient in each window, as shown in the following formula:

[0093] (1) Tilt angle Find:

[0094]

[0095] (2) Surface inclination angle α xy Find:

[0096]

[0097] (3) Surface characteristic coefficient D x,y Find:

[0098] D x,y=-200*((d+f+b+h) / 2-2*e) / ds 2

[0099] In the above formula, a, b, c, d, e, f, g, h, i are the minimum values ​​of the point cloud elevation in the corresponding grid, ds is the grid spacing, dx and dy are the differentials along the x and y directions, respectively.

[0100] The surface characteristic coefficient of mountainous areas is divided into the following four categories according to the characteristics of the topsoil layer and ground vegetation, as shown in Table 1. It can be seen that the topsoil layer and ground vegetation characteristics are classified into concave landforms and convex landforms. Combined with the definition of curvature in ArcGIS, positive values ​​represent convex landforms, negative values ​​represent concave landforms, and 0 represents flat land.

[0101] Table 1 Surface characteristic coefficient D in mountainous areas x,v Classification table

[0102]

[0103] Combined with the point cloud echo characteristics in this embodiment, the proportion of multiple echoes (N / M) is used as a parameter for judging the characteristics of ground vegetation, with 10%, 20%, 30%, and 40% corresponding to Class I, II, III, and IV of the surface types respectively.

[0104] Further preferably, based on the point cloud data of non-growing fixed features, several subsidence monitoring points in the target mining area are determined and their working face parameters are obtained, and the probability integral method parameter inversion is performed in combination with the genetic algorithm to obtain the probability integral parameters, including:

[0105] The point cloud data of non-growing fixed objects at different time points are compared, the deformation of the ground surface is analyzed, the subsidence area is identified, and several subsidence monitoring points are determined and their working surface parameters are obtained; the working surface parameters of the subsidence monitoring points are used as input data for parameter inversion using the probability integral method, and the probability integral parameters of several subsidence monitoring points are solved.

[0106] Further preferably, the working face parameters include corner points, coal seam inclination, and mining thickness; the probability integral parameters include subsidence coefficient q, horizontal movement coefficient b, main influencing angle tangent tanβ, mining propagation angle θ, and upper left and lower right inflection point offset s.

[0107] Further preferably, the probability integral parameter, the surface characteristic parameter, and the working face parameter are used to construct a settlement prediction model, and the settlement prediction model is used to predict the surface deformation of the mining area surface point cloud data obtained in real time, including:

[0108] Loading a working face prediction file containing probability integral parameters, surface characteristic parameters, and working face parameters into a data processing system (such as a host computer system), and initializing a settlement prediction model according to the parameters in the prediction file;

[0109] Airborne LiDAR monitoring equipment is used to regularly obtain real-time raw surface point cloud data of the mining area. The pre-processed real-time point cloud data is input into the settlement prediction model. The surface deformation of mining activities in real time is simulated in the data processing system, and the surface deformation prediction results including surface settlement map and horizontal movement map are output.

[0110] Combination Figure 2 According to the above embodiment of the present invention, by processing the point cloud data obtained by the airborne LiDAR and combining the mountain surface movement deformation model and the rapid acquisition method of its characteristic parameters, an integrated solution for mountain subsidence monitoring and prediction is constructed. The detailed steps are as follows:

[0111] (1) Obtain point cloud data of the monitoring range of the mining area to determine the scope of the subsidence basin. Use a small-scale grid spacing (ds = 1m) to process the initial point cloud data, combine the airborne LiDAR echo characteristics and the PCL slope method to obtain point cloud data of non-growing fixed objects such as roads, building tops, and bare soil, and make a difference between the two periods of fixed object data to obtain the subsidence basin;

[0112] (2) Calculate the surface characteristic parameters. First, set the grid spacing, and traverse the grid data by moving the window to calculate the surface characteristic parameters, namely the surface inclination angle, tilt direction angle, and surface characteristic coefficient;

[0113] (3) Obtain working face information. Consult the data to obtain working face parameters, such as corner points, coal seam inclination, mining thickness, etc.;

[0114] (4) Non-growth type ground settlement data extraction and parameter inversion. Select the exposed point cloud data with high reliability from the subsidence basin, and use the genetic algorithm to perform probability integral method parameter inversion;

[0115] (5) Prediction of surface movement and deformation during coal mining under the mountain. The obtained probability integral method parameters, surface characteristic parameters, and working face parameters are combined into a working face prediction file to predict the subsidence of the working face.

[0116] The above prediction method is further described in detail below in conjunction with an actual processing routine.

[0117] 1. Overview of study area and data collection:

[0118] The study area is located in a mining area in Jincheng, Shanxi Province, with an average mining depth of about 675m. The mined coal seam is No. 3 coal, with an average inclination of 5°, a strike length of 1113m, a dip length of 165m, and an average mining height of 2.82m. The working face is mined using the full caving method. The terrain is medium and low mountains, with mountains in the north, east-west ridges in the middle, and gentle mountains and hills in the south. The maximum height difference within the working face is about 200m, and the vegetation is dense.

[0119] Due to the dense vegetation and complex terrain above the working face, it is difficult to lay out the monitoring line on the main section, so it is scattered around the working face along the mountain road. Figure 8 As shown: al line is laid along the dirt road in the strike direction, deviating from the main section by about 200m; c line is laid along the dirt road in the strike direction and obliquely crosses the working face, and the points are located in the middle and north of the working face; z line is laid along the bare rock in the strike direction, and the points are located in the middle of the working face; a line is laid along the strike direction, mainly concentrated in the middle and south; bl line is laid along the hardened road in the dip direction, and the points are located in the southwest of the working face; bb line and bc line are both laid along the dip direction, located in the east of the working face. A total of 168 monitoring points are laid, with a spacing of 50m between the measuring points. The regular observation period is one month, in which the elevation is based on the third-class leveling and the XY coordinates are based on GNSS RTK measurement.

[0120] The airborne LiDAR data was acquired by orthophotography of the Pegasus D2000 quad-rotor drone, and the coordinates of the image control points were obtained by static measurement. The specific information collected is shown in Table 2. The working face was mined in August 2023, and as of January 20, 2024, the working face had advanced 510 m in total.

[0121] Table 1 Working surface LiDAR data collection information table

[0122]

[0123]

[0124] 2. Verification of point cloud extraction of non-growing fixed objects based on airborne LiDAR echo characteristics:

[0125] from Figure 8 From the image, we can see that there is a lot of vegetation above the working surface, and there are also some hardened roads without vegetation, bare rocks, and buildings far away from the working surface. Based on these discrete fixed objects, this experiment processed the two-phase LiDAR point cloud data according to the above point cloud processing method. The results are as follows: Fig. 9 shown.

[0126] from Fig. 9 It can be seen that the southwest of the working surface is a residential area, and it can be clearly seen that the road and roof have been stripped off. There is a highway in the southeast of the working surface, and it can be seen that the highway part has been completely extracted. It can be concluded that in complex mountainous terrain conditions, the echo characteristics of airborne LiDAR can be used to achieve point cloud extraction of fixed objects.

[0127] To further verify the reliability of the data, the settlement values ​​near the monitoring points were selected and compared with the settlement values ​​obtained by actual leveling measurements. Considering that some monitoring points do not have point cloud data, data verification was performed on 71 monitoring points with point cloud data. The final results are as follows: Figure 8 .

[0128] like Fig.10 As shown in the figure, (1) in general, the cumulative mean error of extraction accuracy is 35mm, the maximum subsidence of level monitoring is about 800mm (the actual maximum subsidence of the surface is greater due to the layout of monitoring points), and the relative error is only 4.4%, which meets the engineering requirements. (2) In terms of accuracy, the cumulative mean error of the al line is 34mm and the cumulative mean error of the c line is 51mm, both of which are laid along the dirt road. The accuracy of the c line is obviously lower, while the accuracy of the al line is higher. Combined with the surface image map ( Figure 8 ) It can be found that the vegetation coverage of line c is higher than that of line al. Therefore, the increase of vegetation will interfere with the accuracy of fixed object extraction. (3) The cumulative mean error of line bl is 20mm, and the cumulative mean error of line z is 22mm; by comparing the observation lines, it can be seen that the accuracy of line z and line bl laid along bare stone and hardened roads is significantly better than that of line al and line c laid along dirt roads. The reason is that the vegetation coverage of bare stone and hardened roads is low, and on the other hand, the echo reflectivity of bare stone and hardened roads is better than that of dirt roads.

[0129] In summary, the point cloud extraction method of non-growing fixed objects based on the echo characteristics of airborne LiDAR can realize the extraction of non-growing fixed objects on the surface, and the accuracy meets the engineering requirements. When the vegetation coverage rate near non-growing fixed objects is high, the extraction accuracy will be reduced. And the point cloud extraction accuracy along bare rocks and hardened roads is better than that of dirt roads. Therefore, it is recommended to select non-growing fixed objects such as bare rocks and hardened roads with low vegetation coverage and high reliability for data research in point cloud extraction.

[0130] 3. Parameter inversion

[0131] In order to further study the influence of monitoring point settlement on the deformation of the mined working face, comparative experiments were conducted on the settlement values ​​of level monitoring and point cloud extraction. Fig.11 As shown in Figure 1, a total of 31 points are selected for point cloud extraction, 22 points (1-22) are selected for trend, and 9 points (23-31) are selected for inclination. Fig.10 As shown, a total of 33 leveling monitoring points were selected, including 21 points for strike (z1-z9, a11-al10, a18-2, a18-3) and 12 points for inclination (bl17-bl21, bl23-bl29).

[0132] like Fig.11 and 12As shown in the figure, in terms of inversion data selection, point cloud extraction points can be selected based on the main section, while leveling points can only be selected from points near the working surface due to terrain reasons. Therefore, monitoring ground deformation through airborne LiDAR has more advantages than traditional monitoring methods in detecting the center of mining subsidence.

[0133] The genetic algorithm was used to solve the parameters of the prediction model. To avoid the randomness of the parameter solution, the algorithm was run 5 times, and the average of the 5 running results was taken as the final result. The results are shown in Table 3. The comparison of the predicted fitting sinking curve in the parameter solution is shown in Fig.13 and 14 The absolute values ​​of the errors in the figure are the differences between the two sets of data.

[0134] Table 2 Comparison of inversion results of mountain subsidence prediction model parameters

[0135]

[0136] Combined with Table 3, Fig.13 and 14 It can be seen that: (1) in terms of the accuracy of subsidence fitting, the mean error of the fitting of the leveling monitoring data is 77mm, while the mean error of the fitting of the point cloud monitoring data is 100mm. Relative to the maximum subsidence values ​​of 800mm and 1300mm of the respective monitoring points, the relative errors are 9.6% and 7.6% respectively, and the fitting accuracy is comparable; (2) by comparing the parameter inversion results of the two different monitoring data, it can be found that the subsidence coefficient inverted by the point cloud monitoring data is much larger than that of the leveling monitoring data. This indicates that the missing maximum surface subsidence value will lead to a smaller inversion result of the subsidence coefficient, and further indicates that the parameter values ​​inverted using airborne LiDAR monitoring data are more in line with reality.

[0137] Therefore, compared with traditional monitoring methods, airborne LiDAR monitoring technology is not only unrestricted in monitoring, but its monitoring data has better applications in subsequent regularity research and deformation prediction, and can provide more reliable data support for subsequent parameter inversion and deformation prediction.

[0138] 4. Calculation of surface characteristic parameters and deformation prediction

[0139] The LiDAR point cloud data collected in the first phase are selected to calculate the surface characteristic parameters of the mountainous area. The acquisition area is 3 kilometers long and 2 kilometers wide. The grid spacing of the surface prediction points is set to 10m (ds = 10m), and the ground prediction range is a rectangular area. Based on the above content, the surface characteristic parameters obtained during the subsidence prediction are as follows: Fig.15 , 16 and 17.

[0140] Table 3 Comparison of sinking boundaries

[0141]

[0142]

[0143] The probability integral method parameters obtained by inverting the above parameters and the surface characteristic parameters of the mountain area obtained by LiDAR point cloud are used to predict the deformation of the working face. The sinking contour is shown in Fig.18 As shown in Table 4, the subsidence center is located in the center of the mined area, and the maximum impact boundary of the working face is located near points bc9, bb10, a10 and bl15. As of January 20, 2024, the comparison of the above subsidence boundaries based on the leveling monitoring results is shown in Table 4. As shown in Table 4, for edge detection, the cumulative mean error is only 3.2 mm.

[0144] In summary, the characteristic parameters of any point on the mountain surface can be quickly calculated by moving the window, and the subsidence of the working face is predicted based on the mountain surface movement model. The results show that the edge detection is consistent with the measured leveling monitoring data, proving that the prediction method can achieve a more reliable subsidence prediction.

[0145] Example 2

[0146] Based on the same inventive concept, this embodiment further proposes a mining area surface movement deformation prediction device based on airborne LiDAR, which is used to implement the mining area surface movement deformation prediction method proposed in the above embodiment 1, and the device includes:

[0147] The data acquisition module is used to scan the target mining area using airborne LiDAR technology and collect the original point cloud data of the target mining area;

[0148] The data processing module is used to process the point cloud data based on the echo characteristics of the airborne LiDAR and extract the point cloud data of non-growing fixed objects;

[0149] A parameter extraction module, which is used to traverse the pre-processed point cloud data of non-growing fixed objects using a moving window method to determine surface characteristic parameters, where the surface characteristic parameters include a tilt direction angle, a surface point inclination angle, and a surface characteristic coefficient;

[0150] The parameter inversion module is used to determine several subsidence monitoring points in the target mining area based on the point cloud data of non-growing fixed objects and obtain the parameters of their working surface, and to perform probability integral method parameter inversion in combination with genetic algorithm to obtain probability integral parameters;

[0151] The real-time prediction module is used to construct a settlement prediction model based on probability integral parameters, surface characteristic parameters, and working face parameters, and use the settlement prediction model to predict surface deformation of the mining area surface point cloud data obtained in real time.

[0152] It should be noted here that each module in the above-mentioned prediction system corresponds to steps S1 to S5 in implementing the above-mentioned prediction method, and the instances and application scenarios implemented by multiple modules are the same as those of the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiment 1, and this prediction system has all the beneficial effects of the above-mentioned prediction method, which will not be repeated here.

[0153] Further preferably, the data acquisition module includes a drone equipped with an airborne LiDAR device, which is used to perform aerial scanning of the target mining area and transmit the acquired original point cloud data of the mining area surface to the data processing module in real time.

[0154] Example 3

[0155] This embodiment further provides an electronic device, including:

[0156] a processor; a memory for storing instructions executable by the processor;

[0157] The processor is configured to execute instructions to implement the prediction method as described above.

[0158] Example 4

[0159] This embodiment also proposes a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the prediction method as described above.

[0160] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0161] In addition, each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0162] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0163] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting surface movement and deformation in mining areas based on airborne LiDAR, characterized in that: The method comprises: Use airborne LiDAR technology to scan the target mining area and collect the original point cloud data of the target mining area; Processing the point cloud data based on the echo characteristics of the airborne LiDAR to extract point cloud data of non-growing fixed objects; The moving window method is used to traverse the pre-processed point cloud data of the non-growing fixed object to determine the surface characteristic parameters, wherein the surface characteristic parameters include the tilt direction angle, the surface point inclination angle and the surface characteristic coefficient; Based on the point cloud data of non-growing fixed objects, several subsidence monitoring points in the target mining area are determined and their working face parameters are obtained. The probability integral method parameter inversion is performed with the genetic algorithm to obtain the probability integral parameters. A settlement prediction model is constructed by using the probability integral parameter, the surface characteristic parameter, and the working face parameter, and the settlement prediction model is used to predict surface deformation of the mining area surface point cloud data acquired in real time.

2. The method for predicting surface movement and deformation in a mining area based on airborne LiDAR according to claim 1, characterized in that: The step of processing the point cloud data to extract the point cloud data of the non-growing fixed features includes: Divide the surface point cloud data into grids, and record the number of point clouds and the number of echo points in each grid; Threshold conditions are set, and grids that meet the threshold conditions are regarded as non-fixed objects and filtered using the PCL slope method to extract point cloud data of non-growing fixed objects, wherein the growing fixed objects include roads, building tops and bare soil.

3. The method for predicting surface movement and deformation in mining areas based on airborne LiDAR according to claim 1, characterized in that: The method of using a moving window method to traverse the point cloud data of the pre-processed non-growing fixed objects to determine the surface characteristic parameters includes: using a moving window with a set grid spacing to traverse the point cloud data of the pre-processed non-growing fixed objects, and calculating the tilt direction angle, the surface inclination angle and the surface characteristic coefficient in each window, as shown in the following formula: (1) Tilt angle Find: (2) Surface inclination angle α x,y Find: (3) Surface characteristic coefficient D x,y Find: D x,y =-200*((d+f+b+h) / 2-2*e) / ds 2 In the above formula, a, b, c, d, e, f, g, h, i are the minimum values ​​of the point cloud elevation in the corresponding grid, ds is the grid spacing, dx and dy are the differentials along the x and y directions, respectively.

4. The method for predicting surface movement and deformation in mining areas based on airborne LiDAR according to claim 3 is characterized in that: The method of determining several subsidence monitoring points in the target mining area based on point cloud data of non-growing fixed objects and obtaining working surface parameters thereof, and performing probability integral method parameter inversion in combination with genetic algorithm to obtain probability integral parameters, includes: Compare the point cloud data of non-growing fixed objects at different time points, analyze the deformation of the surface, identify the subsidence area, determine several subsidence monitoring points and obtain their working surface parameters; The working surface parameters of the subsidence monitoring points are used as input data to perform parameter inversion using a probability integral method, and the probability integral parameters of several subsidence monitoring points are solved.

5. The method for predicting surface movement and deformation in mining areas based on airborne LiDAR according to claim 4, characterized in that: The working face parameters include corner points, coal seam inclination, and mining thickness; the probability integral parameters include sinking coefficient q, horizontal movement coefficient b, main influencing angle tangent tanβ, mining propagation angle θ, and upper left and lower right inflection point offset s.

6. The method for predicting surface movement and deformation in mining areas based on airborne LiDAR according to claim 5, characterized in that: The method comprises: constructing a settlement prediction model by using the probability integral parameter, the surface characteristic parameter, and the working face parameter, and using the settlement prediction model to predict surface deformation of the mining area surface point cloud data acquired in real time, including: Loading a working face prediction file containing probability integral parameters, surface characteristic parameters, and working face parameters into a data processing system, and initializing a settlement prediction model according to the parameters in the prediction file; Airborne LiDAR monitoring equipment is used to regularly obtain real-time original surface point cloud data of the mining area, and the pre-processed real-time point cloud data is input into the settlement prediction model. The surface deformation of mining activities in real time is simulated in the data processing system, and the surface deformation prediction results including surface settlement maps and horizontal movement maps are output.

7. A device for predicting surface movement and deformation in mining areas based on airborne LiDAR, characterized in that: The device comprises: The data acquisition module is used to scan the target mining area using airborne LiDAR technology and collect the original point cloud data of the target mining area; A data processing module, used to process the point cloud data based on the echo characteristics of the airborne LiDAR, and extract the point cloud data of the non-growing fixed objects; A parameter extraction module, for traversing the pre-processed point cloud data of non-growing fixed objects using a moving window method to determine surface characteristic parameters, wherein the surface characteristic parameters include a tilt direction angle, a surface point inclination angle, and a surface characteristic coefficient; The parameter inversion module is used to determine several subsidence monitoring points in the target mining area based on the point cloud data of non-growing fixed objects and obtain the parameters of their working surface, and to perform probability integral method parameter inversion in combination with genetic algorithm to obtain probability integral parameters; The real-time prediction module is used to construct a settlement prediction model using the probability integral parameter, the surface characteristic parameter, and the working face parameter, and use the settlement prediction model to predict surface deformation of the mining area surface point cloud data acquired in real time.

8. The device for predicting surface movement and deformation in mining areas based on airborne LiDAR according to claim 7, characterized in that: The data acquisition module includes a drone equipped with an airborne LiDAR device, which is used to perform aerial scanning of the target mining area and transmit the acquired original point cloud data of the mining area surface to the data processing module in real time.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the prediction method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the prediction method according to any one of claims 1 to 6.

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