A method and system for predicting coal seam mining subsidence

Abstract

The present invention discloses a method and system for predicting coal seam mining subsidence, which relates to the technical field of coal seam mining subsidence, and includes: combining the topographic data of the mining area, giving the average elevation within the predicted range, and giving the mining depth of the corner points of the working face to be predicted according to this average elevation; using a search algorithm to find the grid to which each predicted point belongs in the digital elevation model, and then according to the corner point coordinates of this grid, using the method of bilinear interpolation to interpolate the predicted points, obtaining the elevation of each predicted point, and obtaining the maximum and minimum elevations within the predicted range; respectively correcting the mining depth of the original corner points of the working face to be predicted according to the obtained maximum and minimum elevations, and on this basis, respectively performing subsidence prediction; according to the prediction results corrected by the maximum and minimum elevations, finally obtaining a subsidence prediction result that basically conforms to the actual situation by using linear interpolation. The present invention can accurately reflect the subsidence situation of the mountainous surface.

Description

Technical Field

[0001] The present invention relates to the technical field of coal seam mining subsidence, and particularly relates to a method and a system for predicting coal seam mining subsidence. Background Art

[0002] With the gradual reduction of coal resources and the gradual improvement of mining technologies, coal mining has shifted to the western region with large topographic undulations and large coal seam dips.

[0003] In the prior art, when predicting mining subsidence in mountainous areas, the prediction calculation method of surface movement and deformation in the "three-under" coal mining regulations is usually adopted. The surface movement and deformation in mountainous areas is the vector superposition of the surface movement and deformation on flat ground under the same geological mining conditions and the mining-induced slip deformation in mountainous areas. In practical applications, it is found that there are still some problems in the mountain surface subsidence prediction model in the regulations: due to the limitation of topographic conditions, the influence amount of surface slip above the goaf boundary is sometimes not the largest; in the case of approximate full mining or over-full mining, sometimes the slip influence in the middle of the goaf is not 0; the slip influence function cannot comprehensively reflect the complex surface slip situation in mountainous coal mining, and the mountain slip model needs to be corrected.

[0004] In summary, the above technology simplifies the subsidence caused by slip deformation during mining above the goaf boundary and in the middle of the goaf, resulting in the slip deformation not being able to accurately reflect the subsidence of the mountain surface in coal mining. Summary of the Invention

[0005] The embodiments of the present invention provide a method and a system for predicting coal seam mining subsidence, which can solve the problem in the prior art that the subsidence of the mountain surface cannot be accurately reflected.

[0006] The embodiments of the present invention provide a method for predicting coal seam mining subsidence, including the following steps:

[0007] Obtain the mining depth of the corner points of the mining face within the coal seam mining working area; wherein, the coal seam mining working area includes the mining face and the ground surface above the mining face, and the mining depth of the corner points is the vertical depth of the points at each corner of the mining face relative to the ground surface; Use a search algorithm to obtain the grid to which each subsidence prediction point on the ground surface of the mining working area belongs in the digital elevation model; According to the corner point coordinates of the grid to which each subsidence prediction point belongs, use bilinear interpolation to interpolate the grid to which each subsidence prediction point belongs; According to the interpolation result, obtain the elevation of each subsidence prediction point on the ground surface of the mining working area; Select the maximum elevation and the minimum elevation from the elevations of all prediction points on the ground surface of the mining working area; Input the maximum elevation and the minimum elevation into the mining depth correction model respectively to correct the mining depth of the corner points of the mining face, and obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth; Input the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth into the mining subsidence prediction model respectively, and obtain the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth; According to the difference between the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth, and the difference between the maximum elevation and the minimum elevation, use linear interpolation method to correct the subsidence value of each subsidence prediction point, and predict the subsidence value that conforms to the actual situation.

[0008] Further, the specific steps for obtaining the elevation of each subsidence prediction point on the ground surface of the mining working area include:

[0009] Use bilinear interpolation to interpolate the corner point coordinates of the grid to which each subsidence prediction point belongs;

[0010]

[0011] Wherein, are the elevation coordinates of the four corner points of a grid in the digital elevation model, is the elevation of each subsidence prediction point on the ground surface of the mining working area P , x is the abscissa value of each subsidence prediction point P , x 1 is H 1 and H the abscissa value of 3, x 2 is H 2 and H the abscissa value of 4, y is the ordinate value of each subsidence prediction point P , y 1 is H 1 and H the ordinate value of 2y 2 is H 3 and H the ordinate value of 4.

[0012] Further, the steps for obtaining the maximum corrected corner mining depth and the minimum corrected corner mining depth specifically include:

[0013] Input the maximum elevation into the mining depth correction model to obtain the maximum corrected corner mining depth :

[0014]

[0015] Input the minimum elevation into the mining depth correction model to obtain the minimum corrected corner mining depth :

[0016]

[0017] Wherein, is the mining depth of the maximum corrected mining face corner, is the mining depth of the minimum corrected mining face corner, is the mining depth of the mining face corner, is the maximum elevation, is the minimum elevation, is the average elevation.

[0018] Further, the steps for obtaining the subsidence prediction results of each subsidence prediction point for the maximum corrected corner mining depth prediction and the subsidence prediction results of each subsidence prediction point for the minimum corrected corner mining depth prediction specifically include:

[0019] Respectively input the maximum corrected corner mining depth of the mining face corner and the minimum corrected corner mining depth of the mining face corner into the mining subsidence prediction model:

[0020] W( x , y ) = W0 · C( x ) · C( y )

[0021] Wherein, and are the subsidence distribution coefficients in the main section; x , y are the horizontal and vertical coordinates of each subsidence prediction point respectively, W( x , y ) is the subsidence amount of each subsidence prediction point, and W0 is the settlement amount of the coal seam roof;

[0022] Obtain the subsidence prediction results of each subsidence prediction point estimated by the maximum value-corrected corner mining depth, and the subsidence prediction results of each subsidence prediction point estimated by the minimum value-corrected corner mining depth.

[0023] Further, the method for correcting the subsidence value of each subsidence prediction point by using the linear interpolation method specifically includes the following steps:

[0024] Obtain the ratio of the first difference between the subsidence prediction results of each subsidence prediction point estimated by the maximum value-corrected corner mining depth and the subsidence prediction results of each subsidence prediction point estimated by the minimum value-corrected corner mining depth, and the second difference between the maximum elevation and the minimum elevation.

[0025] According to the ratio of the first difference to the second difference and the elevation of the subsidence prediction point, use linear interpolation to obtain the subsidence value of the subsidence prediction point. :

[0026]

[0027] Wherein, is the corrected subsidence value, is the minimum value subsidence prediction result, is the maximum value subsidence prediction result, is the maximum elevation, is the minimum elevation, is the elevation of the predicted point.

[0028] Further, the method for obtaining the mining depth of the corner points of the mining face in the coal seam mining working area specifically includes the following steps:

[0029] Use the digital elevation model to obtain the elevation data of the surface of the coal seam mining working area;

[0030] Obtain the average elevation according to the elevation data of the surface of the mining working area, and obtain the mining depth of the corner points of the mining face according to the difference between the average elevation and the vertical coordinates of the corner points of the mining face.

[0031] An embodiment of the present invention provides a prediction system for coal seam mining subsidence, including:

[0032] A data acquisition module, configured to acquire the mining depth of the corner points of the mining face in the coal seam mining working area;

[0033] The mining depth correction module is used to obtain the grid to which each subsidence prediction point on the surface of the mining working area belongs in the digital elevation model by using a search algorithm; perform interpolation on the grid to which each subsidence prediction point belongs according to the corner coordinates of the grid to which each subsidence prediction point belongs by using the bilinear interpolation method; obtain the elevation of each subsidence prediction point on the surface of the mining working area according to the interpolation result; select the maximum elevation and the minimum elevation from the elevations of all prediction points on the surface of the mining working area; input the maximum elevation and the minimum elevation into the mining depth correction model respectively to correct the mining depth of the corner points of the mining face, and obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth;

[0034] The subsidence prediction module is used to input the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth into the mining subsidence prediction model respectively, and obtain the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth; according to the difference between the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth, and the difference between the maximum elevation and the minimum elevation, use the linear interpolation method to correct the subsidence value of each subsidence prediction point, and predict the subsidence value that conforms to the actual situation.

[0035] The embodiment of the present invention provides a method and system for predicting coal seam mining subsidence. Compared with the prior art, its beneficial effects are as follows:

[0036] Obtain the grid to which each subsidence prediction point on the surface of the mining working area belongs in the digital elevation model by using a search algorithm; perform interpolation on the grid to which each subsidence prediction point belongs according to the corner coordinates of the grid to which each subsidence prediction point belongs by using the bilinear interpolation method; obtain the elevation of each subsidence prediction point on the surface of the mining working area according to the interpolation result; select the maximum elevation and the minimum elevation from the elevations of all prediction points on the surface of the mining working area; input the maximum elevation and the minimum elevation into the mining depth correction model respectively to correct the mining depth of the corner points of the mining face, and obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth; input the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth into the mining subsidence prediction model respectively, and obtain the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth; according to the difference between the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth, and the difference between the maximum elevation and the minimum elevation, use the linear interpolation method to correct the subsidence value of each subsidence prediction point, and predict the subsidence value that conforms to the actual situation.

[0037] In summary, the maximum elevation and minimum elevation are used to correct the maximum corrected corner mining depth and the minimum corrected corner mining depth. Then, the subsidence prediction results of each subsidence prediction point estimated using the maximum corrected corner mining depth and the minimum corrected corner mining depth are used. Finally, the data estimated between the two subsidence prediction results is further used to correct the subsidence value of each subsidence prediction point using the linear interpolation method, so that the subsidence prediction results of each point in the mining working area can be accurately obtained while considering the influence of elevation on the subsidence results. Description of the Drawings

[0038] Figure 1 It is a method flow chart of a method for predicting coal seam mining subsidence provided by an embodiment of the present invention;

[0039] Figure 2 It is a schematic diagram of the elevation calculation of a predicted point for a method for predicting coal seam mining subsidence provided by an embodiment of the present invention;

[0040] Figure 3 It is the overall structure diagram of the mining subsidence prediction system for a system for predicting coal seam mining subsidence provided by an embodiment of the present invention;

[0041] Figure 4 It is a schematic diagram of the inflection point offset distance for a system for predicting coal seam mining subsidence provided by an embodiment of the present invention;

[0042] Figure 5 It is the DEM data format for a system for predicting coal seam mining subsidence provided by an embodiment of the present invention;

[0043] Figure 6 It is the mountainous terrain simulation prediction area for a system for predicting coal seam mining subsidence provided by an embodiment of the present invention;

[0044] Figure 7 It is the predicted result of a certain working face for a system for predicting coal seam mining subsidence provided by an embodiment of the present invention. Detailed Embodiments

[0045] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following detailed description of the specific embodiments of the present invention is provided in conjunction with the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0046] An embodiment of the present invention provides a method for predicting coal seam mining subsidence, including the following steps:

[0047] Step 1: Obtain the mining depth of the corner points of the mining working face within the coal seam mining working area.

[0048] Step 2: Use the search algorithm to obtain the grid to which each subsidence prediction point on the surface of the mining working area belongs in the digital elevation model; according to the corner coordinates of the grid to which each subsidence prediction point belongs, use the bilinear interpolation method to interpolate the grid to which each subsidence prediction point belongs; according to the interpolation result, obtain the elevation of each subsidence prediction point on the surface of the mining working area; select the maximum elevation and the minimum elevation from the elevations of all the prediction points on the surface of the mining working area; input the maximum elevation and the minimum elevation into the mining depth correction model respectively to correct the mining depth of the corner points of the mining face, and obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth.

[0049] Step 3: Input the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth into the mining subsidence prediction model respectively, and obtain the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth; according to the difference between the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth, and the difference between the maximum elevation and the minimum elevation, use the linear interpolation method to correct the subsidence value of each subsidence prediction point, and predict the subsidence value that conforms to the actual situation.

[0050] The following is a specific explanation of the present invention:

[0051] 1. Improve the defects of the existing mountain subsidence prediction model.

[0052] 1.1 Defects existing in the existing model.

[0053] In practical applications, it is found that there are still some problems in the mountain surface subsidence prediction model in the regulations: ① Due to the limitation of topographic conditions, the surface slip influence amount above the goaf boundary is sometimes not the largest; ② In the case of approximate full mining or over-full mining, sometimes the slip influence in the middle of the goaf is not 0; ③ The slip influence function cannot fully reflect the complex surface slip situation of mountain coal mining, and the mountain slip model needs to be corrected. ④ The parameters of the mountain mining surface subsidence prediction model are numerous, and the conventional method is easily troubled by problems such as parameter divergence and falling into local optimum during the parameter inversion process, and it is often difficult to obtain the optimal solution of the mountain surface movement prediction parameters.

[0054] 1.2 Principle and method of improvement.

[0055] Through research, it is found that on hillsides, due to the action of gravitational pull, there is a downward slope slip, which in turn causes subsidence in the downhill direction; while in the intermediate slopes and valley areas, due to sudden changes in the slip direction or slip volume, extrusion occurs, causing the ground surface to rise. After analyzing the research results of predecessors and considering the influence of surface elevation changes on the subsidence results, a method for correcting the movement and deformation values in the case of flat ground mining using the digital elevation model (DEM) of the mining area surface is proposed. The basic steps of this method are as follows: ① First, combine the topographic data of the mining area to give the average elevation within the predicted range, and based on this average elevation, give the mining depth of the corner points of the working face to be predicted; ② Use the search algorithm to find the grid to which each predicted point belongs in the digital elevation model, and then, according to the corner point coordinates of this grid, use the bilinear interpolation method to interpolate the predicted points to obtain the elevation of each predicted point, and obtain the maximum and minimum elevations within the predicted range; ③ Correct the mining depth of the original corner points of the working face to be predicted according to the obtained maximum and minimum elevations, and on this basis, conduct subsidence predictions respectively; ④ According to the predicted results corrected by the maximum and minimum elevations, finally obtain the subsidence prediction results that basically conform to the actual situation by linear interpolation. The method flow chart is as Figure 1 shown.

[0056] The main formulas of the mountain area correction model are as follows:

[0057] (1) Calculation of the elevation of the predicted point, as Figure 2 shown:

[0058] (1)

[0059] In the formula: are the elevations of the four corner points of the DEM grid; is the elevation of the point to be obtained.

[0060] (2) Calculate the maximum and minimum elevations:

[0061] (2)

[0062] In the formula: is the maximum elevation within the predicted range; is the minimum elevation within the predicted range.

[0063] (3) Mining depth correction:

[0064] (3)

[0065] In the formula: is the corrected maximum mining depth; is the corrected minimum mining depth; is the mining depth set according to the average elevation; is the maximum elevation within the predicted range; is the minimum elevation within the predicted range; is the average elevation within the set predicted range.

[0066] (4) Subsidence prediction:

[0067] Since the probability integral method is adopted in the mining subsidence prediction model, only the subsidence of any point on the ground surface is taken as an example for illustration.

[0068] W( x , y ) = W0·C( x )·C( y ) (4)

[0069] In the formula: and are called the subsidence distribution coefficients in the main section, x , y are the abscissa and ordinate of each subsidence prediction point respectively, and W( x , y ) is the subsidence amount of each subsidence prediction point, and W0 is the subsidence amount of the coal seam roof.

[0070] (5) Correction of the prediction result in mountainous areas:

[0071] (5)

[0072] In the formula: is the corrected subsidence value; is the subsidence value predicted by the minimum mining depth; is the subsidence value predicted by the maximum mining depth; is the maximum elevation within the predicted range; is the minimum elevation within the predicted range; is the elevation of the prediction point.

[0073] The embodiment of the present invention provides a prediction system for coal seam mining subsidence, including:

[0074] A data acquisition module, configured to acquire the mining depth of the corner points of the mining face within the working area of coal seam mining.

[0075] The mining depth correction module is used to obtain the grid to which each subsidence prediction point on the surface of the mining working area belongs in the digital elevation model by using a search algorithm; interpolate the grid to which each subsidence prediction point belongs by using the bilinear interpolation method according to the corner coordinates of the grid to which each subsidence prediction point belongs; obtain the elevation of each subsidence prediction point on the surface of the mining working area according to the interpolation result; select the maximum elevation and the minimum elevation from the elevations of all the prediction points on the surface of the mining working area; input the maximum elevation and the minimum elevation into the mining depth correction model respectively to correct the mining depth of the corner points of the mining face, and obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth.

[0076] The subsidence prediction module is used to input the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth into the mining subsidence prediction model respectively, and obtain the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth; according to the difference between the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth, and the difference between the maximum elevation and the minimum elevation, use the linear interpolation method to correct the subsidence value of each subsidence prediction point, and predict the subsidence value that conforms to the actual situation.

[0077] A specific embodiment is as follows:

[0078] 1. Design a subsidence prediction software for mountainous areas.

[0079] 1.1 System introduction and overall framework.

[0080] Based on the mining subsidence prediction model in the "Regulations on the Retention and Pressurized Coal Mining of Coal Pillars for Buildings, Water Bodies, Railways and Main Roadways" and the self-designed mountain area correction model, and drawing on the advantages of existing mining subsidence prediction software, this system designs and implements a mining subsidence prediction system applicable to a certain mining area. This system strives to be easy to operate, have a reasonable architecture, good visualization effects, and practical value, and mainly realizes functions such as steady-state and dynamic mining subsidence prediction, contour line drawing, profile line drawing, subsidence area and volume analysis, building damage zoning, waterlogging area analysis, protective coal pillars, and parameter calculation.

[0081] Mining subsidence science is an interdisciplinary subject, and computer technology plays an important role in it. In order to automate data processing and visualize data, many researchers have developed mining subsidence prediction systems. Through comparison and testing, it is found that this system has the following advantages:

[0082] (1) This software system is a further improvement based on the previous one. It adopts the development mode of "platform + plug-in", is based on the AutoCAD platform, and uses.NET secondary development technology to realize the prediction and analysis of mining subsidence in the mining area, with good system compatibility.

[0083] (2) The software has a high degree of integration and a high degree of functional integration. Only by using this software can functions such as data calculation and engineering drawing generation be completed, without the need to rely on other software for post-processing of predicted data.

[0084] (3) The data has a high degree of visualization. This system combines the theory of mining subsidence prediction and the principle of automatic generation of contour lines, as well as computer graphics technology, and integrates the function of automatic generation of surface deformation contour lines, with prediction and output integrated, and is convenient to use. Figure 1 Integrated and easy to use.

[0085] The overall structure of the mining subsidence prediction system in a certain mining area is as Figure 3 shown.

[0086] 1.2 Implementation and cases of mountain subsidence prediction.

[0087] <1>. Software implementation process

[0088] (1) Create a project

[0089] Click the "Mountain Area Prediction / Project Information" command in the menu to open the "Create Project" dialog box.

[0090] a) Enter the project name in the "Project Name" text box. After entering, press Enter to get the saved project path. If the user wants to re-specify the project path, they can click the "Select" button.

[0091] b) Click the "OK" button to save the data to the database.

[0092] c) Click the "Record Management" button to view the project information that has been created currently.

[0093] d) Click the "Exit" button or click the "X" in the upper right corner to exit the current form.

[0094] (2) Working face information

[0095] Select the "Mountain Area Prediction / Working Face Information" command to open the "Working Face Information" dialog box.

[0096] a) Select the project name containing the current working face in the "Project Name to Which It Belongs" list box.

[0097] b) Enter the working face number in the "Working Face Name" text box.

[0098] c) Enter the mining thickness in the "Mining Thickness" text box, with the unit being millimeters.

[0099] d) Mining start time: Input the start mining time of the working face.

[0100] e) Mining end time: Input the start mining time of the working face.

[0101] f) Estimated time: Select the estimated time of the working face.

[0102] g) Coal winning rate. The coal winning rate is input according to the average coal winning rate, that is, coal winning rate = working face strike length / (mining end time - mining start time). When the coal winning rate uses "+", the advancing direction of the working face is along the strike; when the coal winning rate uses "-", the advancing direction of the working face is against the strike.

[0103] h) The estimated parameters refer to: q is the subsidence coefficient, tanβ is the tangent of the main influence angle, b is the horizontal movement coefficient, θ is the maximum subsidence 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.

[0104] Note: The definitions of the inflection points S left, S right, S upper, and S lower are as Figure 4 shown. The side towards the inside of the goaf is "+", and the opposite is "-".

[0105] i) Click the "Obtain coal seam dip azimuth angle" button to pick up the coal seam dip azimuth angle. The azimuth angle is picked up in the way of determining the direction by two points, that is, first pick up the starting point (coal seam uphill direction), and then pick up an ending point (coal seam downhill direction). The direction from the starting point to the ending point is the coal seam dip azimuth angle (you can view the prompts in the CAD command line); it can also be directly input in the text box beside.

[0106] j) Click the "Pick up corner point coordinates" button to pick up the working face corner point coordinates. Click the corner point on the CAD drawing, then input the mining depth value of the corner point, and press the Enter key, and the data will be saved. At most 8 corner point coordinates can be picked up counterclockwise. If there are less than 8 points, press the "Esc" key to exit. Note: The software only supports the prediction of convex polygon working faces. If the working face is a concave polygon, the user needs to split it into several convex polygons by himself.

[0107] k) Save.

[0108] l) If you need to continue inputting the working face information, you can click the "Clear" button to clear the information in the text box, but some parameters will be retained, such as: project name, estimated parameters, mining thickness, etc. (you can modify them or not) to improve the input speed.

[0109] m) You can click "Record Management" to open the "Working Face Attribute Information Browsing" dialog box, in which you can click the "Delete Row" button to delete the current record, or click "Browse Working Face Corner Point Coordinates" to browse the corner point coordinates of the currently selected working face.

[0110] n) Click the "Exit" button or click the "X" in the upper right corner to exit the current form.

[0111] (3) Surface prediction target acquisition

[0112] Grid picking: Click the menu command "Mountain area prediction / Surface prediction point information picking" to pop up the "Grid prediction target picking" dialog box (initially, the text boxes in the dialog box are all empty).

[0113] a) Click the "Pick" button to pick grid data. The grid is a rectangle covering the entire predicted working face range (considering that the number of prediction points should not be too many, so attention should be paid to the size of the picking range and the grid division spacing). Determine the rectangle according to the upper left vertex and the lower right vertex, and then a prompt for the prediction direction will appear. The prediction direction is determined by the starting point and the ending point, that is, pick 2 points (you can view the prompts in the CAD command line). Select the project name to which it belongs and enter the division spacing in the pop-up dialog box, and then click the "Save" button to store the current data. Note: If subsequent operations such as drawing contour lines need to be performed, only the "Mesh feature picking" command can be executed.

[0114] b) If you want to view or delete the created data, you can click the "Data browsing" button.

[0115] c) Finally, exit.

[0116] (4) File generation

[0117] After entering the working face parameters, click the menu command "Mountain area prediction / Generate predicted data file" to open the "Generate predicted data file" dialog box. Select a project in the left list box, and the right list box will display the working faces it contains. Select the required working face, then enter the average ground elevation, and finally click the "Generate data file" button, then the predicted file will be generated and stored in the project directory set by the user.

[0118] Click the "Edit data file" button to open the just-generated file.

[0119] Finally, exit.

[0120] (5) Prediction calculation

[0121] The mountain area correction model corrects the prediction results according to the digital elevation model (DEM) of the predicted area. The DEM can be obtained by manual digitization or image acquisition, etc. Its data format is (X, Y, H), and it is saved as a.dat format file, as Figure 5 shown.

[0122] Click on the menu command "Mountain Area Prediction / Estimation Calculation". This function is executed in four steps, namely: fitting the DEM with the elevation of the prediction points, predicting the maximum elevation mining depth, predicting the minimum elevation mining depth, and data integration and processing.

[0123] Step 1: When fitting the DEM with the elevation of the prediction points, the following interface will pop up.

[0124] Select the DEM data file and open it. After the first step is completed, directly proceed with the prediction of the maximum elevation mining depth. Since a large amount of data needs to be processed, it may take a relatively long time. The calculation time will be displayed in the CAD command window at any time. After the calculation is completed, it will be prompted to save as a data file.

[0125] After storing the file, click "Calculation End - Exit". (It should be noted that this process is carried out twice, and just operate according to the prompts.)

[0126] After the second step is completed, directly proceed with the third step of predicting the minimum elevation mining depth. The operation is similar to the execution process of the second step; after the third step is completed, the software will automatically perform the fourth step of data integration and processing. Since the amount of data to be processed may be very large, please be patient during the execution process.

[0127] <2>. Specific case implementation

[0128] Specific test examples are as Figure 6 and Figure 7 shown.

[0129] 1.3 Prediction error and effect analysis.

[0130] The subsidence prediction errors of a certain mining area subsidence prediction system mainly include: ① model error, ② parameter error, ③ mining thickness error.

[0131] ① Model error analysis: Since there are relatively few rock movement observations for mining subsidence in a certain mining area and there are certain deficiencies in the existing data, to ensure the accuracy and reliability of the prediction results, the probability integral method model recommended in the "Regulations on the Setting of Coal Pillars for Buildings, Water Bodies, Railways and Main Shaft Roadways and Coal Mining under Pressure" is adopted for the mining subsidence prediction model of the software. This model has been well applied in many mining areas, but there are still certain errors in the prediction of mining subsidence in cases such as steeply inclined coal seams, extremely insufficient mining, and mountainous areas. For the prediction of surface subsidence in mountainous areas, a mountainous area correction model based on DEM is established to eliminate the influence of surface elevation changes on the subsidence results; for the prediction of subsidence in cases of extremely insufficient mining, the method of modifying the subsidence prediction parameters is used to improve the prediction accuracy. Generally speaking, the subsidence prediction model has a relatively high prediction accuracy for the mining subsidence of gently inclined coal seams, near fully mined areas, and flat surface areas, while the prediction accuracy for the mining subsidence in cases such as steeply inclined coal seams, extremely insufficient mining, and mountainous areas is slightly lower.

[0132] ② Parameter error: The correctness of the selection of mining subsidence prediction parameters will directly affect the accuracy of the prediction results. Due to the relatively small amount of rock movement observation data in the study area and the certain lack of existing data, the prediction parameters of a certain mining area can only be obtained by sorting out the observation station data collected from the four coal mines. Therefore, the subsidence prediction accuracy of the four coal mines is relatively high, while other coal mines can be estimated by analogy with the prediction parameters of these four mines, but the prediction accuracy may be slightly lower. The selection of mining subsidence prediction parameters often requires a certain amount of experience, especially when the mining is not sufficient. If the prediction is still based on the parameters of full mining, the prediction result will be too large; on the contrary, if the working face around the expected working face has been mined, the impact of the mined working face on the expected working face must be considered when selecting parameters, otherwise the prediction result will be too small.

[0133] ③ Error in mining thickness: Due to the good occurrence conditions of coal seams in a certain mining area, top coal caving is mostly used for mining. The mining thickness will directly affect the accuracy of the mining subsidence prediction results. It is best to calculate and verify a more accurate mining thickness based on actual conditions such as recovery rate and coal output, otherwise the predicted results will have a large deviation.

[0134] 1.4 Concluding results.

[0135] (1) Through in-depth data analysis and comparison of the applicability of existing mining subsidence prediction models in a certain mining area, the probability integral method model recommended in the "Regulations on Coal Pillar Retention and Compressed Coal Mining in Buildings, Water Bodies, Railways and Main Shafts and Tunnelings" (referred to as the "Three-Down" regulations) was selected as the surface subsidence prediction model for a certain mining area. The surface subsidence in mountainous areas is significantly different from that in plain areas. According to the topographic and geomorphological characteristics of a certain mining area, a DEM-based mountain correction model was established.

[0136] (2) Combine the existing surface movement observation station data to obtain the mining subsidence prediction parameters, and from the subsidence coefficient ( q ), the main influence angle tangent ( tanβ )、Maximum sinking angle( θ )、inflection point offset( S ), and angular parameters, a comprehensive analysis was conducted on parameter selection, and a guide for selecting mining subsidence prediction parameters suitable for a certain mining area was formed.

[0137] (3)The software system for predicting and mapping mining subsidence in mountainous areas has been popularized and used in ten mining areas belonging to a certain coal industry group. Combining the actual mining working faces of multiple coal mines, the subsidence prediction calculation has been carried out using the subsidence prediction software of a certain mining area. The prediction analysis has been carried out respectively from the situations of insufficient mining of a single working face and full mining of multiple working faces. The prediction calculation results basically conform to the actual situations of each mine. It can be seen from the rock movement observation being carried out in a certain coal mine that the surface movement and deformation values obtained by software prediction are basically consistent with the rock movement observation results and conform to the reality.

[0138] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A method for predicting coal seam mining subsidence, characterized in that It includes the following steps: Obtain the mining depth of the corner points of the mining face in the coal seam mining working area; wherein, the coal seam mining working area includes the mining face and the ground surface above the mining face, and the mining depth of the corner points is the vertical depth of the points at each corner of the mining face relative to the ground surface; Use a search algorithm to obtain the grid to which each subsidence prediction point on the ground surface of the mining working area belongs in the digital elevation model; according to the corner point coordinates of the grid to which each subsidence prediction point belongs, use bilinear interpolation to interpolate the grid to which each subsidence prediction point belongs; according to the interpolation result, obtain the elevation of each subsidence prediction point on the ground surface of the mining working area; select the maximum elevation and the minimum elevation from the elevations of all prediction points on the ground surface of the mining working area; Input the maximum elevation and the minimum elevation into the mining depth correction model respectively to correct the mining depth of the corner points of the mining face, and obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth; Input the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth into the mining subsidence prediction model respectively, and obtain the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth; According to the difference between the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth, and the difference between the maximum elevation and the minimum elevation, use linear interpolation method to correct the subsidence value of each subsidence prediction point, and predict the subsidence value that conforms to the actual situation.

2. The prediction method for coal seam mining subsidence according to claim 1, characterized in that The specific steps for obtaining the elevation of each subsidence prediction point on the ground surface of the mining working area include: Use bilinear interpolation to interpolate the corner point coordinates of the grid to which each subsidence prediction point belongs; Among them, are the elevation coordinates of the four corner points of a grid in the digital elevation model, is the elevation of each subsidence prediction point on the surface of the mining work area P , x is the abscissa value of each subsidence prediction point P , x 1 is H the abscissa value of 1 and H 3, x 2 is H the abscissa value of 2 and H 4, y is the ordinate value of each subsidence prediction point P , y 1 is H the ordinate value of 1 and H 2, y 2 is H the ordinate value of 3 and H 4.

3. The prediction method for coal seam mining subsidence according to claim 1, characterized in that, The specific steps for obtaining the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth include: Input the maximum elevation into the mining depth correction model to obtain the maximum corrected corner point mining depth : Input the minimum elevation into the mining depth correction model to obtain the minimum value corrected corner point mining depth : Among them, is the mining depth for correcting the corner point of the mining face at the maximum value, is the mining depth for correcting the corner point of the mining face at the minimum value, is the mining depth of the corner point of the mining face, is the maximum elevation, is the minimum elevation, is the average elevation.

4. The prediction method for coal seam mining subsidence according to claim 1, characterized in that The specific steps for obtaining the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth include: Input the maximum value corrected corner point mining depth of the corner points of the mining face and the minimum value corrected corner point mining depth of the corner points of the mining face into the mining subsidence prediction model respectively: W( x , y ) = W0·C( x )·C( y ) Among them, and is the subsidence distribution coefficient in the main section; x , y are the abscissa and ordinate of each subsidence prediction point respectively, W( x , y ) is the subsidence amount of each subsidence prediction point, and W0 is the settlement amount of the coal seam roof; Obtain the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth.

5. The prediction method for coal seam mining subsidence according to claim 1, characterized in that The specific steps for using the linear interpolation method to correct the subsidence value of each subsidence prediction point include: According to the first difference between the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth, and the second difference between the maximum elevation and the minimum elevation, obtain the ratio of the first difference to the second difference; According to the ratio of the first difference to the second difference and the elevation of the subsidence prediction point, linear interpolation is used to obtain the subsidence value of the subsidence prediction point : Among them, is the corrected settlement value, is the minimum settlement prediction result, is the maximum settlement prediction result, is the maximum elevation, is the minimum elevation, is the predicted point elevation.

6. The prediction method of coal seam mining subsidence according to claim 1, characterized in that The specific steps for obtaining the mining depth of the corner points of the mining face in the coal seam mining working area include: Use the digital elevation model to obtain the elevation data of the ground surface of the coal seam mining working area; Obtain the average elevation based on the elevation data of the surface of the mining working area, and obtain the mining depth of the corner points of the mining working face according to the difference between the average elevation and the vertical coordinates of the corner points of the mining working face.

7. A prediction system for coal seam mining subsidence, characterized in that Including: A data acquisition module for acquiring the mining depth of the corner points of the mining working face within the coal seam mining working area; A mining depth correction module for using a search algorithm to obtain the grid to which each subsidence prediction point on the surface of the mining working area belongs in the digital elevation model; Interpolate the grid to which each subsidence prediction point belongs using bilinear interpolation according to the corner point coordinates of the grid to which each subsidence prediction point belongs; according to the interpolation result, obtain the elevation of each subsidence prediction point on the surface of the mining working area; select the maximum elevation and the minimum elevation from the elevations of all prediction points on the surface of the mining working area; Input the maximum elevation and the minimum elevation into the mining depth correction model respectively to correct the mining depth of the corner points of the mining working face, and obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth; A subsidence prediction module for inputting the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth into the mining subsidence prediction model respectively to obtain the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth; According to the difference between the subsidence prediction results of each subsidence prediction point predicted by the maximum value corrected corner point mining depth and the subsidence prediction results of each subsidence prediction point predicted by the minimum value corrected corner point mining depth, and the difference between the maximum elevation and the minimum elevation, use linear interpolation to correct the subsidence value of each subsidence prediction point, and predict the subsidence value that conforms to the actual situation.

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