A mining subsidence prediction method and system considering terrain factors

Abstract

The application discloses a kind of mining subsidence prediction method and system considering terrain factor, it is related to mining subsidence prediction technical field, comprising: obtaining the mining depth of mining face corner point in mining area and the coordinate of prediction point, extracting the elevation value of grid point in preset prediction range in mining face, generate maximum and first average elevation in preset prediction range;According to the maximum and first average elevation, the mining depth of mining area working face corner point in preset prediction range is corrected, and the maximum mining depth after correction and the mining influence range of mining area working face are obtained, the elevation value in influence range is extracted, the second average elevation is generated, and the mining depth of prediction point is corrected and the main influence radius is obtained;With the coordinate of prediction point and the main influence radius, probability integration is carried out, and the subsidence prediction result is obtained.The application corrects the mining depth of prediction point, considers the influence of terrain factor on subsidence result, uses world terrain to extract ground elevation value to correct the moving deformation value under the condition of flat ground mining, and improves the prediction effect in mountainous area.

Description

Technical Field

[0001] This invention relates to the field of mining subsidence technology, and in particular to a mining subsidence prediction method and system that takes into account topographic factors. Background Technology

[0002] Mining subsidence is a common problem encountered in underground mining activities, mainly manifested as subsidence basins, ground fissures, benches, and subsidence pits. It impacts surface buildings, farmland, rivers, and roads, and can even trigger geological disasters such as landslides, threatening the safety of people and property. Mining subsidence prediction plays a crucial role in coal mining under buildings, water bodies, and railways. It can accurately predict the scope, degree, and development trend of coal mining subsidence, providing a basis for the optimized design of underground mining faces and the assessment of mining-induced damage to surface buildings.

[0003] Currently, mining subsidence prediction methods mainly include empirical methods, profile function methods, influence function methods, analytical methods, physical model methods, and numerical methods. Among these, the probability integral method is widely used in mining subsidence prediction. The probability integral method is based on the theory of stochastic media, and the model contains a probability integral function whose upper and lower limits are determined by the shape of the working face in the mining area. Conventional probability integral method models have many limitations and do not meet the subsidence prediction requirements of actual coal mine working face mining. Mining subsidence prediction is not limited to the prediction of a single mining face, but involves the coordinated prediction of mining subsidence across multiple mining faces. Furthermore, mining subsidence is a complex and dynamic process; the prediction result using a conventional probability integral method model is a steady-state basin, which cannot reflect the development process of surface moving basins. Most existing mining subsidence prediction software is based on client-server architecture software developed using MATLAB, AutoCAD secondary development, and C#, or is standalone software. These software programs all suffer from drawbacks such as inconvenient multi-source data management, cumbersome overlay of prediction results onto image base maps, and the inability to provide real-time monitoring and early warning.

[0004] Furthermore, probabilistic integral method models often predict a flat surface, ignoring the influence of topographic factors. While they exhibit good accuracy in plains areas, they show significant errors in predictions with large topographic relief or in mountainous regions. Traditional probabilistic integral method models, because they predict on flat terrain and do not consider the impact of topographic relief, have high accuracy in predicting subsidence in horizontal coal seams and gently dipping coal seams in plains. Existing mountain prediction models consider landslide and slippage factors, introducing a slippage influence function into the modified model. The basic idea is to treat surface movement and deformation in mountainous areas as the superposition of surface movement and deformation vectors in flat areas and mountainous areas under the same geological and mining conditions. Some scholars also believe that the amount of slippage deformation in mountainous areas is caused by upslope slippage intensifying surface subsidence, while downslope accumulation and uplift reduce surface subsidence. Compared to the probability integral method prediction model for flat land, the prediction model for mountainous areas takes into account the influence of the slip deformation vector, which significantly improves the prediction accuracy when the terrain is undulating. However, the actual landslide slip influencing factors are more complex, and the slip influence function in the prediction model for mountainous areas cannot be used as the basis for landslide occurrence. In addition, the model has many parameters, and a large amount of measured data is needed to invert the model parameters. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the prior art by providing a mining subsidence prediction method and system that takes into account topographic factors, thereby solving the problems in the prior art.

[0006] The present invention specifically provides the following technical solution: A method for predicting mining subsidence that takes into account topographic factors includes: Obtain the mining depth at the corner of the working face in the mining area, and obtain the coordinates of the predicted points in the working face of the mining area; The elevation values ​​of grid points within a preset prediction range in the mining working face are extracted by setting a sampling interval, and the maximum elevation and the first average elevation within the preset prediction range are generated using the grid point elevation values. The mining depth at the corner point of the working face is corrected by using the maximum elevation and the first average elevation within the preset prediction range. The corrected maximum mining depth and the mining influence range of the working face with the maximum mining depth are obtained within the preset prediction range. The grid point elevation values ​​within the mining influence range of the working face are extracted to generate the second average elevation. The mining depth of the predicted point is corrected by using the second average elevation and the predicted point elevation values ​​in the grid points. The main influence radius is obtained by using the mining depth correction value of the predicted point. The subsidence prediction result is obtained by performing probability integration on the predicted point coordinates and the main influence radius.

[0007] Preferably, the step of correcting the mining depth at the corner point of the working face in the mining area by using the maximum elevation and the first average elevation within a preset prediction range to obtain the corrected maximum mining depth within the preset prediction range specifically involves: The maximum mining depth correction within the preset prediction range is obtained by summing the average mining depth at the corner points of the mining face and the maximum elevation within the preset prediction range, and then subtracting the average elevation within the preset prediction range. The specific expression is: ; In the formula: This is the maximum mining depth after correction within the preset prediction range. This represents the average mining depth at the corner of the working face in the mining area. The maximum elevation within the preset prediction range, This is the first average elevation.

[0008] The preferred mining area affected by the working face at the maximum mining depth is as follows: The mining influence range of the working face in the mining area is obtained by combining the maximum mining depth after correction within the preset prediction range with the tangent value of the main influence angle, which serves as the maximum main influence radius; the specific expression is: ; In the formula: For the maximum major influence radius, This is the maximum sampling depth after correction within the preset prediction range. The tangent of the main influence angle is used to characterize the range of influence of mining on surface deformation and is obtained through verification by measured data.

[0009] Preferably, the step of correcting the sampling depth of the predicted points using the second average elevation and the predicted point elevation values ​​in the grid points specifically involves: The predicted mining depth correction is obtained by summing the average mining depth at the corner point of the mining face and the predicted elevation value, and then subtracting the average elevation within the affected area; the specific expression is: ; In the formula: The depth is adjusted after the prediction point is determined. This represents the average mining depth at the corner of the working face in the mining area. For the predicted point elevation value, This is the second average elevation.

[0010] Preferably, the main influence radius is obtained by using the depth correction value of the predicted point, specifically as follows: The main influence radius is obtained by comparing the predicted depth correction of the point with the tangent of the main influence angle; the specific expression is: ; in, The radius of the main influence corresponding to the predicted point. The depth is adjusted after the prediction point is determined. The tangent value is the main influencing angle.

[0011] Preferably, the step of performing probability integration on the predicted point coordinates and the main influence radius to obtain the subsidence prediction result specifically involves: The subsidence prediction result is generated using the probability integral method, with the main influencing radius parameter and the coordinates of the prediction point as input parameters; the specific expression is as follows: ; ; ; ; in, For the revised The sinking value of the point, The coordinates of the predicted point; The main influence radius of the predicted point after correction; To obtain thicker materials; This is the subsidence coefficient; The dip angle of the coal seam; This represents the subsidence value along the main cross-section along the strike. This represents the subsidence value along the dip of the main cross-section; This refers to the length of the working face in the mining area. This represents the dip length of the working face in the mining area. The depth is adjusted after the prediction point is determined; It is the error function; For integration parameters.

[0012] This invention provides a mining subsidence prediction system that takes into account topographic factors, comprising: The data acquisition module is used to obtain the mining depth of the corner points of the working face in the mining area, and to obtain the coordinates of the predicted points in the working face of the mining area; The elevation acquisition module is used to extract the elevation values ​​of grid points within a preset prediction range in the working face of the mining area through a set sampling interval, and to generate the maximum elevation and the first average elevation within the preset prediction range using the grid point elevation values. The correction module is used to correct the mining depth of the corner points of the working face in the mining area by using the maximum elevation and the first average elevation within the preset prediction range. It obtains the corrected maximum mining depth and the mining influence range of the working face with the maximum mining depth within the preset prediction range, extracts the grid point elevation values ​​within the mining influence range of the working face in the mining area, generates the second average elevation, and corrects the mining depth of the predicted points by using the second average elevation and the predicted point elevation values ​​in the grid points. The main influence radius is obtained by using the mining depth correction value of the predicted points. The prediction module is used to perform probability integration on the coordinates of the prediction point and the main influence radius to obtain the subsidence prediction result.

[0013] The present invention provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor performs the steps of the above-described method for predicting mining subsidence taking into account topographic factors.

[0014] The present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for predicting mining subsidence taking into account topographic factors.

[0015] Compared with the prior art, the present invention has the following significant advantages: This invention obtains the mining depth and predicted point coordinates of the corner points of the working face in the mining area, extracts the maximum elevation and the first average elevation within the preset prediction range, and corrects the mining depth of the corner points of the working face. At the same time, it obtains the mining influence range and the second average elevation through the corrected maximum mining depth, further corrects the mining depth of the predicted points and obtains the main influence radius, thereby obtaining the subsidence prediction result. It can be seen that this invention comprehensively considers the influence of terrain factors on the subsidence result. The operation does not require a large number of inversion parameters. It only uses the terrain to extract the surface elevation value and predicted point coordinates to correct the mining depth of the predicted points within the influence range. It realizes the acquisition of the corrected movement deformation value under the condition of mining on flat land, avoiding the situation where the traditional landslide influence factor cannot be accurately used as the basis for sliding when using traditional methods, thus improving the prediction effect of the mining area. Attached Figure Description

[0016] Figure 1 A flowchart of a mining subsidence prediction method that takes into account topographic factors, provided by the present invention; Figure 2 A schematic diagram illustrating the principle of depth correction provided by this invention; Figure 3 This invention provides a simulated mining area working face region map; Figure 4 This invention provides a simulated topographic map of the area surrounding the working face of a mining area. Figure 5 The image shows the prediction results of the conventional probability integral method for the simulated mining face provided by this invention. Figure 6 A simulation result diagram of the working face of a mining area provided by the present invention, after taking the terrain into account. Figure 7 A comparison diagram of the trend profile of the prediction results provided by this invention; Figure 8 A comparison chart of trend profiles for the case prediction results provided by this invention; Figure 9 The diagram shows the actual working face and the calculated working face of the mining area provided by this invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] Numerous scholars have conducted in-depth research on the problems existing in the probability integral method and have achieved fruitful results. For example, Xu Dong, Teng Yongjia, and Tan have realized the subsidence prediction of any mining face by dividing the mining area into working faces and converting different integral methods. Xu Liangji, Jiang Qian, and Wang have realized the dynamic prediction of mining subsidence by combining the probability integral method with different time functions. Wang Lei and Han Kuifeng proposed a mountain prediction model considering the sliding factor. Wang et al. derived a semi-analytical calculation formula for surface subsidence and movement in mountain mining based on the theory of stochastic media and the propagation angle of landslide influence. The existing probability integral method is applicable to the static subsidence prediction, dynamic subsidence prediction, and mountain mining subsidence prediction of any mining face. Its theoretical model is relatively mature and shows good versatility in mining subsidence prediction, but the model needs to be modified when used in mountainous conditions.

[0019] like Figure 1 and Figure 2 As shown, based on the above problems, this invention has found through research that the impact range of the goaf considering and not considering terrain is inconsistent with the impact range predicted by the model. The impact range of mining subsidence is determined by the main impact radius of the mining face, which is related to the mining depth and the tangent of the main impact angle. In the probability integral method model, terrain factors are mainly reflected in the mining depth of the mining face. This is also reflected in the derivation of the subsidence impact function of unit mining using the probability integral method. Equation (1) is the calculation formula for unit mining derived by the probability integral method.

[0020] (1); In the formula, Indicates unit mining caused The probability of a point sinking; The actual meaning is caused by unit mining. The probability of point subsidence is numerically equal to the probability of subsidence caused by unit mining. Point sinking value For the Earth's surface, equal to mining depth If is a constant, then It is also a constant ( (This is referred to as the main radius of influence).

[0021] Considering the influence of topographic factors on mining subsidence prediction results, different elevation values ​​and mining depths at the prediction points correspond to different main influence radii, and thus different main influence radii in subsidence prediction. Therefore, it is necessary to correct the mining depth at the prediction points. Based on the above analysis, and considering the influence of topographic factors on subsidence results, a method is proposed to use Cesium World Topography to extract surface elevation values ​​and correct the movement deformation values ​​under flat ground mining conditions.

[0022] This embodiment proposes a mining subsidence prediction method that takes into account topographic factors, including the following steps: Step S1: Obtain the mining depth of the corner point of the working face in the mining area, and obtain the coordinates of the predicted point in the working face of the mining area.

[0023] Step S2: Extract the grid point elevation values ​​within the preset prediction range in the mining working face through the set sampling interval, and generate the maximum elevation, minimum elevation and first average elevation within the preset prediction range using the grid point elevation values.

[0024] Step S3: Correct the mining depth of the corner point of the working face in the mining area by using the maximum elevation and the first average elevation within the preset prediction range, obtain the corrected maximum mining depth and the mining influence range of the working face with the maximum mining depth within the preset prediction range, extract the grid point elevation values ​​within the mining influence range of the working face in the mining area, generate the second average elevation, correct the mining depth of the predicted point with the second average elevation and the predicted point elevation values ​​in the grid points, and obtain the main influence radius through the mining depth correction value of the predicted point.

[0025] (1) Maximum mining depth correction within the preset prediction range: The maximum mining depth correction within the preset prediction range is obtained by the difference between the sum of the average mining depth at the corner point of the working face in the mining area and the maximum elevation within the preset prediction range, and the average elevation within the preset prediction range; the specific expression is: (2); In the formula: This is the maximum mining depth after correction within the preset prediction range. This represents the average mining depth at the corner of the working face in the mining area. The maximum elevation within the preset prediction range, The average elevation within the preset prediction range is the first average elevation. The average elevation is calculated directly based on the elevation value of each grid point within the preset prediction range.

[0026] (2) Calculate the maximum main influence radius corresponding to the corrected maximum mining depth within the preset prediction range: The mining influence range of the working face in the mining area is obtained by combining the corrected maximum mining depth within the preset prediction range with the tangent of the main influence angle, which is used as the maximum main influence radius. The specific expression is as follows: (3); In the formula: The maximum primary influence radius is calculated based on the maximum mining depth after correction within the preset prediction range. This is the maximum sampling depth after correction within the preset prediction range. The main influence angle tangent is the core parameter in mining subsidence prediction. It is used to characterize the range of influence of mining on surface deformation and is obtained through verification by measured data.

[0027] (3) Predicted mining depth correction: The predicted mining depth correction is obtained by summing the average mining depth of the corner points of the working face in the mining area and the elevation value of the predicted point, and then comparing it with the average elevation within the affected area; the specific expression is: (4); In the formula: The depth is adjusted after the prediction point is determined. The average mining depth at the corner of the working face in the mining area. For the predicted point elevation value, The average elevation within the affected area is calculated directly from the elevation value of each grid point within the affected area.

[0028] (4) Calculation of the dynamic main influence radius of the prediction point: The main influence radius is obtained by the ratio of the depth correction of the prediction point to the tangent of the main influence angle; the specific expression is: (5); in, The radius of the main influence corresponding to the predicted point. The depth is adjusted after the prediction point is determined. The tangent value is the main influencing angle.

[0029] Step S4: Perform probability integration on the predicted point coordinates and the main influence radius to obtain the subsidence prediction result.

[0030] Subsidence prediction model taking topographic factors into account: Since the mining subsidence prediction model uses the probability integral method, we will only illustrate it using the subsidence at an arbitrary point on the ground surface as an example. The probability integral method is used, with the main influence radius parameter and the coordinates of the prediction point as input parameters, to generate the subsidence prediction result; the specific expression is:

[0031] (6); (7); (8); (9); in, For the revised The sinking value of the point, The coordinates of the predicted point; The main influence radius of the predicted point after correction; To obtain thicker materials; This is the subsidence coefficient; The dip angle of the coal seam; This represents the subsidence value along the main cross-section along the strike. This represents the subsidence value along the dip of the main cross-section; This refers to the length of the working face in the mining area. This represents the dip length of the working face in the mining area. The depth is adjusted after the prediction point is determined; It is the error function; For integration parameters.

[0032] Design and implementation of a mining subsidence prediction platform taking into account topographic factors: The platform is based on a modified mining subsidence prediction model using the conventional probability integral method. Drawing on the advantages of existing mining subsidence prediction software, it designs and implements a mining subsidence prediction platform that takes topographic factors into account. The platform aims for ease of operation, a reasonable architecture, good visualization, and practical value, primarily implementing functions such as conventional mining subsidence prediction, mining subsidence prediction considering topography, and contour line drawing.

[0033] Cesium, an open-source, WebGL-based 3D earth rendering engine, is widely used in GIS due to its powerful spatial data processing and 3D display capabilities. Therefore, a web-based coal mine subsidence prediction platform is of significant research and practical importance, as Cesium's world topography provides high-precision surface elevation data. Comparison and testing revealed the following advantages of this platform:

[0034] (1) This platform adopts front-end and back-end separation technology, and is developed using B / S structure. It is based on Cesium platform and Vue framework and front-end component library development technology to realize mining subsidence prediction platform that takes into account terrain factors. The platform has good compatibility.

[0035] (2) The mining area data management is convenient and the user interaction is strong. It uses PostgreSQL and PostGIS extension components to store mining area related data. The subsidence prediction function is calculated in the form of back-end interface, which has high calculation efficiency.

[0036] (3) High level of data visualization. This platform combines WebGIS technology, mining subsidence prediction theory and the principle of automatic generation of contour lines to realize the automatic drawing function of surface deformation contour lines. The prediction results are directly superimposed on the image base map, making the impact range and subsidence amount of mining subsidence more intuitive.

[0037] I. Platform Implementation Process: (1) New construction projects: Click "New Project" on the platform's main page to open the "New Project" modal.

[0038] a) Enter the project name in the "Project Name" input box and the mine working face name in the "Mine Working Face Name" input box.

[0039] b) Click the "Save" button to save the data to the database.

[0040] (2) Collect information on the working face in the mining area: Click "Collect Mining Area Working Face Information" to open the "Collect Mining Area Working Face Information" modal.

[0041] a) Select the saved project name from "New Project" in the "Project Name" drop-down box.

[0042] b) Select the saved mining face name from "New Project" in the "Mining Face Name" drop-down box.

[0043] c) Enter the upper boundary sampling depth in the "Upper Boundary Sampling Depth" input box, in meters.

[0044] d) Enter the lower boundary sampling depth in the "Lower Boundary Sampling Depth" input box, in meters.

[0045] e) Click the "Get Corner Coordinates of Rectangular Mining Face" button, use the middle mouse wheel to scroll to the location of the mining face in the satellite image, left-click to start drawing the mining area, and right-click to complete the drawing. If the mining face has a dip angle relative to the north-south direction, click the "Get Corner Coordinates of Any Mining Face" button, left-click to pick the corner coordinates, and right-click to complete the drawing. If the coal seam has a dip angle, click the "Coal Seam Dip Azimuth" button, left-click to pick the coordinates of the first point, right-click to pick the coordinates of the second point, and complete the drawing. The drawing result is a line segment, which represents the coal seam dip azimuth. To redraw, after drawing, click "Delete Drawing Area," and then click the completed mining face area or line segment to delete it.

[0046] f) Click the "Save" button to save the data to the database.

[0047] (3) Grid generation: Click "Grid Division" to open the "Collect Mining Area Working Face Information" modal.

[0048] a) The “Project Name” and “Mining Area Working Face Name” will be selected by default as the project name and mining area working face name when “Collecting Mining Area Working Face Information”.

[0049] b) Enter the sampling interval in the “Grid Spacing ΔX” input box, in meters.

[0050] c) Enter the sampling interval in the "Grid Spacing ΔY" input box, in meters.

[0051] d) Click the "Draw Preset Prediction Range" button, click the left mouse button to start drawing the preset prediction range, and click the right mouse button to finish drawing.

[0052] e) Click the "Save" button to save the data to the database.

[0053] f) Click the "Get Predicted Point Elevation Values" button. After the elevation values ​​are obtained, the system will automatically calculate and fill the "Maximum Elevation within Preset Predicted Range", "Minimum Elevation within Preset Predicted Range" and "Average Elevation within Preset Predicted Range" input boxes and store the data in the database.

[0054] (4) Prediction parameter settings: Click "Prediction Parameter Settings" to open the "Prediction Parameter Settings" modal.

[0055] a) The “Project Name” and “Mining Area Working Face Name” will be selected by default as the project name and mining area working face name when “Collecting Mining Area Working Face Information”.

[0056] b) The prediction parameters are: q is the sinking coefficient, tanβ is the tangent of the main influence angle, b is the horizontal movement coefficient, θ is the maximum sinking angle, S1 is the left inflection point offset, S2 is the right inflection point offset, S3 is the upper inflection point offset, and S4 is the lower inflection point offset, as detailed below. Figure 9 As shown, the inflection points Sleft, Sright, Sup, and Sdown are defined as follows: Figure 9 As shown, the inside of the goaf is marked with "+", and the outside is marked with "-".

[0057] c) Parameters that require user input: thickness m, subsidence coefficient q, tangent of the main influence angle tanβ, horizontal movement coefficient b, main influence propagation angle θ, offset distance of the left inflection point S1, offset distance of the right inflection point S2, offset distance of the upward inflection point S3, offset distance of the downward inflection point S4, and coal seam dip angle a.

[0058] d) Click the "Save" button to save the data to the database.

[0059] e) Click the "Get Elevation Values ​​within the Influence Area" button. After the elevation values ​​are obtained, the "Maximum Elevation within the Influence Area", "Minimum Elevation within the Influence Area" and "Average Elevation within the Influence Area" input boxes will be automatically calculated and filled, and the data will be stored in the database.

[0060] (5) Subsidence prediction: Click "Subsidence Prediction" to open the "Subsidence Prediction" modal.

[0061] a) The “Project Name” and “Mining Area Working Face Name” will be selected by default as the project name and mining area working face name when “Collecting Mining Area Working Face Information”.

[0062] b) Click the "Start Prediction" button, and the platform will perform subsidence prediction on the selected mining face and save the prediction results to the database.

[0063] (6) Generate contour lines: Click "Generate Contours" to open the "Generate Contours" modal.

[0064] a) Enter the interval of the subsidence contour lines in the "Please enter contour line interval" input box.

[0065] b) Click the "Subsidence", "Corrected Subsidence", "East-West Tilt", "North-South Tilt", "East-West Horizontal Movement", "North-South Horizontal Movement", "East-West Horizontal Deformation", and "North-South Horizontal Deformation" buttons respectively to visualize the subsidence prediction results of the mining area working face on the image base map.

[0066] II. Specific Case Implementation: Specific test examples Figures 3-6 As shown.

[0067] The subsidence prediction errors of mining subsidence prediction platforms that take into account topographic factors mainly include: ① model error and ② parameter error.

[0068] ① Model Error Analysis: The platform uses a prediction model based on the probability integral method, proposing a mining subsidence prediction model that takes into account topographic factors. Compared to the probability integral method model recommended in the "Regulations on the Retention of Coal Pillars and Coal Mining under Pressure in Buildings, Water Bodies, Railways and Main Shafts," this model considers the influence of topographic relief on the prediction results, incorporates surface elevation factors into the model, and uses the main influence radius as a prediction model parameter to correct the prediction model. However, the model still has certain errors in predicting mining subsidence under conditions such as steeply inclined coal seams and extremely insufficient mining. Overall, the subsidence prediction model has high accuracy in predicting mining subsidence in gently inclined coal seams, near-fully mined areas, and flat surface areas, while its accuracy is slightly lower in cases of steeply inclined coal seams and extremely insufficient mining.

[0069] ② Parameter error: The correctness of the selection of mining subsidence prediction parameters directly affects the accuracy of the prediction results. Due to the relatively limited rock movement observation data and the certain gaps in existing data in the study area, the prediction parameters for the mining area can only be obtained based on the established prediction parameter system and engineering analogy method.

[0070] Results analysis: The prediction results were analyzed by drawing one strike profile and two dip profiles separately. The differences between the prediction results obtained using the traditional probability integral method and those corrected for topographic factors were compared. The strike profile and dip profile are shown below. Figure 7 , Figure 8 As shown, the probability integral method for predicting subsidence boundaries after incorporating topographic factors differs from the traditional probability integral method. This difference stems from the varying mining depths at each prediction point, resulting in different primary influence radii for each prediction point. Therefore, the mining subsidence prediction results that take topographic factors into account are more consistent with actual surface movement.

[0071] This invention proposes a mining subsidence prediction system that takes into account topographic factors, comprising: a data acquisition module, an elevation acquisition module, a correction module, and a prediction module.

[0072] The data acquisition module is used to acquire the mining depth of the corner points of the working face and the coordinates of the predicted points in the working face. The elevation acquisition module is used to extract the elevation values ​​of grid points within a preset prediction range in the working face through a set sampling interval, and generate the maximum elevation and the first average elevation within the preset prediction range using the grid point elevation values. The correction module is used to correct the mining depth of the corner points of the working face through the maximum elevation and the first average elevation within the preset prediction range, obtain the corrected maximum mining depth and the mining influence range of the working face with the maximum mining depth within the preset prediction range, extract the grid point elevation values ​​within the mining influence range of the working face, generate the second average elevation, correct the mining depth of the predicted points using the second average elevation and the elevation values ​​of the predicted points in the grid points, and obtain the main influence radius using the mining depth correction value of the predicted points. The prediction module is used to perform probability integration on the coordinates of the predicted points and the main influence radius to obtain the subsidence prediction result.

[0073] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the processor performs the steps of a mining subsidence prediction method that takes into account topographic factors.

[0074] According to the disclosed embodiments, the computer device can communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth communication, etc.) or with any device that enables the computing device to communicate with one or more other computing devices (e.g., router, demodulator, etc.).

[0075] The present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a mining subsidence prediction method that takes into account topographic factors.

[0076] According to the disclosed embodiments, the storage medium can be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0077] The above description, in conjunction with specific preferred embodiments, provides a more detailed explanation of the present invention. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for predicting mining subsidence that takes into account topographic factors, characterized in that, include: Obtain the mining depth at the corner of the working face in the mining area, and obtain the coordinates of the predicted points in the working face of the mining area; The elevation values ​​of grid points within a preset prediction range in the mining working face are extracted by setting a sampling interval, and the maximum elevation and the first average elevation within the preset prediction range are generated using the grid point elevation values. The mining depth at the corner point of the working face is corrected by using the maximum elevation and the first average elevation within the preset prediction range. The corrected maximum mining depth and the mining influence range of the working face with the maximum mining depth are obtained within the preset prediction range. The grid point elevation values ​​within the mining influence range of the working face are extracted to generate the second average elevation. The mining depth of the predicted point is corrected by using the second average elevation and the predicted point elevation values ​​in the grid points. The main influence radius is obtained by using the mining depth correction value of the predicted point. The subsidence prediction result is obtained by performing probability integration on the predicted point coordinates and the main influence radius.

2. The mining subsidence prediction method considering topographic factors as described in claim 1, characterized in that, The step of correcting the mining depth at the corner point of the working face in the mining area by using the maximum elevation and the first average elevation within a preset prediction range to obtain the corrected maximum mining depth within the preset prediction range is as follows: The maximum mining depth correction within the preset prediction range is obtained by summing the average mining depth at the corner points of the mining face and the maximum elevation within the preset prediction range, and then subtracting the average elevation within the preset prediction range. The specific expression is: ； In the formula: This is the maximum mining depth after correction within the preset prediction range. This represents the average mining depth at the corner of the working face in the mining area. The maximum elevation within the preset prediction range, This is the first average elevation.

3. The mining subsidence prediction method considering topographic factors as described in claim 2, characterized in that, The impact range of mining operations at the maximum mining depth is as follows: The mining influence range of the working face in the mining area is obtained by combining the maximum mining depth after correction within the preset prediction range with the tangent value of the main influence angle, which serves as the maximum main influence radius; the specific expression is: ； In the formula: For the maximum major influence radius, This is the maximum sampling depth after correction within the preset prediction range. The tangent of the main influence angle is used to characterize the range of influence of mining on surface deformation and is obtained through verification by measured data.

4. The mining subsidence prediction method considering topographic factors as described in claim 1, characterized in that, The correction of the sampling depth of the predicted points using the second average elevation and the predicted point elevation values ​​in the grid points is specifically as follows: The predicted mining depth correction is obtained by summing the average mining depth at the corner point of the mining face and the predicted elevation value, and then subtracting the average elevation within the affected area; the specific expression is: ； In the formula: The depth is adjusted after the prediction point is determined. This represents the average mining depth at the corner of the working face in the mining area. For the predicted point elevation value, This is the second average elevation.

5. The mining subsidence prediction method considering topographic factors as described in claim 4, characterized in that, The method of obtaining the main influence radius by using the depth correction value of the predicted point is as follows: The main influence radius is obtained by comparing the predicted depth correction of the point with the tangent of the main influence angle; the specific expression is: ； in, The radius of the main influence corresponding to the predicted point. The depth is adjusted after the prediction point is determined. The tangent value is the main influencing angle.

6. The mining subsidence prediction method considering topographic factors as described in claim 1, characterized in that, By performing probability integration on the predicted point coordinates and the main influence radius, the subsidence prediction result is obtained, specifically: The subsidence prediction result is generated using the probability integral method, with the main influencing radius parameter and the coordinates of the prediction point as input parameters; the specific expression is as follows: ； ； ； ； in, For the revised The sinking value of the point, The coordinates of the predicted point; The main influence radius of the predicted point after correction; To obtain thicker materials; This is the subsidence coefficient; The dip angle of the coal seam; This represents the subsidence value along the main cross-section along the strike. This represents the subsidence value along the dip of the main cross-section; This refers to the length of the working face in the mining area. This represents the dip length of the working face in the mining area. The depth is adjusted after the prediction point is determined; It is the error function; For integration parameters.

7. A mining subsidence prediction system that takes into account topographic factors, characterized in that, include: The data acquisition module is used to obtain the mining depth of the corner points of the working face in the mining area, and to obtain the coordinates of the predicted points in the working face of the mining area; The elevation acquisition module is used to extract the elevation values ​​of grid points within a preset prediction range in the working face of the mining area through a set sampling interval, and to generate the maximum elevation and the first average elevation within the preset prediction range using the grid point elevation values. The correction module is used to correct the mining depth of the corner points of the working face in the mining area by using the maximum elevation and the first average elevation within the preset prediction range. It obtains the corrected maximum mining depth and the mining influence range of the working face with the maximum mining depth within the preset prediction range, extracts the grid point elevation values ​​within the mining influence range of the working face in the mining area, generates the second average elevation, and corrects the mining depth of the predicted points by using the second average elevation and the predicted point elevation values ​​in the grid points. The main influence radius is obtained by using the mining depth correction value of the predicted points. The prediction module is used to perform probability integration on the coordinates of the prediction point and the main influence radius to obtain the subsidence prediction result.

8. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores a program that, when executed by the processor, causes the processor to perform the steps of a mining subsidence prediction method that takes into account topographic factors as described in any one of claims 1 to 6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the mining subsidence prediction method that takes into account topographic factors, as described in any one of claims 1 to 6.

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