Method and system for estimating coal seam mining subsidence

Abstract

The invention discloses a coal seam mining subsidence estimation method and system, and relates to the technical field of coal seam mining subsidence, and the method comprises the steps: combining mining area topographic data, giving an average elevation in a prediction range, and giving a mining depth of a to-be-predicted working face corner point according to the average elevation; finding a grid to which each predicted point belongs in the digital elevation model by utilizing a search algorithm, and then interpolating the predicted points by adopting a bilinear interpolation method according to the angular point coordinates of the grid, solving the elevation of each predicted point, and obtaining the maximum elevation and the minimum elevation in a predicted range; according to the obtained maximum elevation and minimum elevation, the mining depth of the original working face angular point to be predicted is corrected, and subsidence prediction is conducted on the basis of the mining depth of the original working face angular point to be predicted; and finally obtaining a subsidence prediction result which basically conforms to the actual situation by utilizing linear interpolation according to the prediction results after the maximum elevation and the minimum elevation are corrected. The method can accurately reflect the subsidence condition of the mountainous area surface.

Description

Technical Field

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

[0002] As coal resources gradually decrease and mining technology gradually improves, coal mining has shifted to the western region where the terrain is more undulating and the coal seams are more inclined.

[0003] In the prior art, when predicting mining subsidence in mountainous areas, the mountainous area surface movement deformation prediction calculation method in the "three-down" coal mining regulations is usually adopted. The surface movement deformation in mountainous areas is the vector superposition of the surface movement deformation of flat land and the mining slip deformation in mountainous areas under the same geological mining conditions. In actual applications, it is found that the mountainous area surface subsidence prediction model in the regulations still has some problems: due to the limitations of terrain conditions, the surface slip impact above the boundary of the goaf is sometimes not the largest; in the case of nearly full mining or super-full mining, sometimes the slip impact in the middle of the goaf is not 0; the slip impact function cannot fully reflect the complex surface slip situation of coal mining in mountainous areas, and the mountainous area slip model needs to be corrected.

[0004] In summary, the above technology simplifies the subsidence caused by slip deformation above the goaf boundary and in the middle of the goaf during mining, resulting in that the slip deformation cannot accurately reflect the subsidence of the surface of the mountainous area where coal mining occurs. Summary of the invention

[0005] The embodiment of the present invention provides a method and system for estimating coal seam mining subsidence, which can solve the problem in the prior art that the surface subsidence situation in mountainous areas cannot be accurately reflected.

[0006] The embodiment of the present invention provides a method for estimating coal seam mining subsidence, comprising the following steps: Obtain the mining depth of the corner points of the mining face in the coal seam mining work area; wherein the coal seam mining work area includes the mining face and the 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 surface; use a search algorithm to obtain the grid to which each subsidence estimation point on the surface of the mining work area belongs in the digital elevation model; according to the corner point coordinates of the grid to which each subsidence estimation point belongs, use a bilinear interpolation method to interpolate the grid to which each subsidence estimation point belongs; according to the interpolation result, obtain the elevation of each subsidence estimation point on the surface of the mining work area; select the maximum elevation and the minimum elevation from the elevations of all the estimation points on the surface of the mining work area; input the maximum elevation and the minimum elevation into the mining model respectively; The depth correction model corrects the mining depth of the corner points of the mining working face to obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth; the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth are respectively input into the mining subsidence prediction model to obtain the subsidence estimation result of each subsidence estimation point estimated by the maximum value corrected corner point mining depth and the subsidence estimation result of each subsidence estimation point estimated by the minimum value corrected corner point mining depth; according to the difference between the subsidence estimation result of each subsidence estimation point estimated by the maximum value corrected corner point mining depth and the subsidence estimation result of each subsidence estimation point estimated by the minimum value corrected corner point mining depth, as well as the difference between the maximum elevation and the minimum elevation, the linear interpolation method is used to correct the subsidence value of each subsidence estimation point to estimate the subsidence value that conforms to the actual situation.

[0007] Furthermore, the step of obtaining the elevation of each estimated subsidence point on the surface of the mining area comprises: The bilinear interpolation method is used to interpolate the coordinates of the corner points of the grid to which each subsidence estimation point belongs; in, are the elevation coordinates of the four corner points of a grid in the digital elevation model. For each subsidence estimation point on the surface of the mining work area P The elevation of x For each settlement estimation point P The horizontal axis value of x 1 for H 1 and H The horizontal coordinate value of 3, x 2 for H 2 and H The horizontal axis value of 4, y For each settlement estimation point P The vertical coordinate value of y 1 for H 1 and H The vertical coordinate value of 2, y 2 for H 3 and HThe vertical coordinate value of 4.

[0008] Furthermore, the obtaining of the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth specifically comprises the following steps: Input the maximum elevation into the mining depth correction model to obtain the maximum value correction corner mining depth : Input the minimum elevation into the mining depth correction model to obtain the minimum corrected corner mining depth : in, The mining depth of the corner points of the mining face is corrected to the maximum value. The depth of the corner point of the mining face is corrected to the minimum value. The mining depth of the corner points of the mining face is is the maximum elevation, is the minimum elevation, is the average elevation.

[0009] Furthermore, the step of obtaining the subsidence estimation result of each subsidence estimation point of the maximum value correction corner point mining depth estimation and the subsidence estimation result of each subsidence estimation point of the minimum value correction corner point mining depth estimation specifically includes: The maximum value of the corner point of the mining face is corrected by the corner point mining depth, and the minimum value of the corner point of the mining face is corrected by the corner point mining depth, which are input into the mining subsidence prediction model: W( x , y )=W0·C( x )·C( y ) in, and is the subsidence distribution coefficient within the main section; x , y are the horizontal and vertical coordinates of each subsidence prediction point, respectively, W( x , y ) is the settlement amount of each estimated settlement point, and W0 is the settlement amount of the coal seam roof; The subsidence estimation result of each subsidence estimation point estimated by the maximum value-corrected corner point mining depth and the subsidence estimation result of each subsidence estimation point estimated by the minimum value-corrected corner point mining depth are obtained.

[0010] Furthermore, the linear interpolation method is used to correct the settlement value of each settlement estimation point, and the specific steps include: Obtain a ratio of the first difference to the second difference based on a first difference between a subsidence estimation result of each subsidence estimation point of the maximum value-corrected corner point mining depth estimation and a subsidence estimation result of each subsidence estimation point of the minimum value-corrected corner point mining depth estimation, and a second difference between the maximum elevation and the minimum elevation; According to the ratio of the first difference to the second difference and the elevation of the subsidence estimation point, the subsidence value of the subsidence estimation point is obtained using linear interpolation. : in, is the corrected subsidence value, is the minimum settlement prediction result, is the maximum settlement prediction result, is the maximum elevation, is the minimum elevation, Estimated point elevation.

[0011] Furthermore, the step of obtaining the mining depth of the corner point of the mining face in the coal seam mining working area specifically includes: Use digital elevation model to obtain the elevation data of the ground surface in the mining working area of ​​the coal seam; The average elevation is obtained based on the elevation data of the surface of the mining work area, and the mining depth of the corner point of the mining work face is obtained based on the difference between the average elevation and the vertical coordinates of the corner point of the mining work face.

[0012] An embodiment of the present invention provides a system for estimating coal seam mining subsidence, comprising: A data acquisition module is used to obtain the mining depth of the corner points of the mining face in the coal seam mining working area; The mining depth correction module is used to obtain the grid to which each estimated subsidence point on the surface of the mining work area belongs in the digital elevation model by using a search algorithm; interpolate the grid to which each estimated subsidence point belongs by using a bilinear interpolation method according to the corner point coordinates of the grid to which each estimated subsidence point belongs; obtain the elevation of each estimated subsidence point on the surface of the mining work area according to the interpolation result; select the maximum elevation and the minimum elevation from the elevations of all estimated points on the surface of the mining work 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 work face, and obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth; 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 result of each subsidence prediction point of the maximum value corrected corner point mining depth estimation and the subsidence prediction result of each subsidence prediction point of the minimum value corrected corner point mining depth estimation; according to the difference between the subsidence prediction result of each subsidence prediction point of the maximum value corrected corner point mining depth estimation and the subsidence prediction result of each subsidence prediction point of the minimum value corrected corner point mining depth estimation, as well as the difference between the maximum elevation and the minimum elevation, the settlement value of each subsidence prediction point is corrected using the linear interpolation method to estimate the settlement value that conforms to the actual situation.

[0013] The embodiment of the present invention provides a method and system for estimating coal seam mining subsidence. Compared with the prior art, the beneficial effects thereof are as follows: A search algorithm is used to obtain the grid to which each estimated subsidence point on the surface of the mining work area belongs in the digital elevation model; the grid to which each estimated subsidence point belongs is interpolated using a bilinear interpolation method according to the coordinates of the corner points of the grid to which each estimated subsidence point belongs; the elevation of each estimated subsidence point on the surface of the mining work area is obtained according to the interpolation result; the maximum elevation and the minimum elevation are selected from the elevations of all estimated points on the surface of the mining work area; the maximum elevation and the minimum elevation are respectively input into the mining depth correction model to correct the mining depth of the corner points of the mining work face, and the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth are obtained. ; 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 estimation result of each subsidence estimation point of the maximum value corrected corner point mining depth estimation and the subsidence estimation result of each subsidence estimation point of the minimum value corrected corner point mining depth estimation; according to the difference between the subsidence estimation result of each subsidence estimation point of the maximum value corrected corner point mining depth estimation and the subsidence estimation result of each subsidence estimation point of the minimum value corrected corner point mining depth estimation, as well as the difference between the maximum elevation and the minimum elevation, use the linear interpolation method to correct the subsidence value of each subsidence estimation point, and estimate the subsidence value that conforms to the actual situation.

[0014] To summarize, the maximum elevation and the minimum elevation are used to correct the maximum value and the minimum value to correct the corner mining depth, and then the maximum value and the minimum value are used to correct the corner mining depth to estimate the settlement result of each settlement estimation point, and finally, the linear interpolation method is further used to use the estimated data between the two settlement estimation results to correct the settlement value of each settlement estimation point, so that the settlement prediction result of each point in the mining working area can be accurately obtained while taking into account the influence of elevation on the settlement result. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A method flow chart of a method for estimating coal seam mining subsidence provided by an embodiment of the present invention; Figure 2 A schematic diagram of calculating the elevation of a predicted point in a method for estimating coal seam mining subsidence provided by an embodiment of the present invention; Figure 3 An overall structural diagram of a mining subsidence prediction system for a coal seam mining subsidence prediction system provided by an embodiment of the present invention; Figure 4 A schematic diagram of an inflection point offset distance of a coal seam mining subsidence prediction system provided by an embodiment of the present invention; Figure 5 A DEM data format of a coal seam mining subsidence prediction system provided in an embodiment of the present invention; Figure 6 A mountain terrain simulation prediction area of ​​a coal seam mining subsidence prediction system provided by an embodiment of the present invention; Figure 7 A predicted result of a certain working face of a coal seam mining subsidence prediction system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of 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 violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0017] The embodiment of the present invention provides a method for estimating coal seam mining subsidence, comprising the following steps: Step 1: Obtain the mining depth of the corner points of the mining face in the coal seam mining working area.

[0018] Step 2: Use the search algorithm to obtain the grid to which each estimated subsidence point on the surface of the mining work area belongs in the digital elevation model; use the bilinear interpolation method to interpolate the grid to which each estimated subsidence point belongs according to the corner point coordinates of the grid to which each estimated subsidence point belongs; obtain the elevation of each estimated subsidence point on the surface of the mining work area based on the interpolation result; select the maximum elevation and the minimum elevation from the elevations of all estimated points on the surface of the mining work area; input the maximum elevation and the minimum elevation into the mining depth correction model to correct the mining depth of the corner points of the mining work face, and obtain the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth.

[0019] Step three: 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 estimation result of each subsidence estimation point estimated by the maximum value corrected corner point mining depth and the subsidence estimation result of each subsidence estimation point estimated by the minimum value corrected corner point mining depth; according to the difference between the subsidence estimation result of each subsidence estimation point estimated by the maximum value corrected corner point mining depth and the subsidence estimation result of each subsidence estimation point estimated by the minimum value corrected corner point mining depth, as well as the difference between the maximum elevation and the minimum elevation, use the linear interpolation method to correct the subsidence value of each subsidence estimation point, and estimate the subsidence value that conforms to the actual situation.

[0020] The following is a specific explanation of the present invention: 1. Improve the defects of existing mountain subsidence prediction models.

[0021] 1.1 Deficiencies of existing models.

[0022] In practical applications, it is found that there are still some problems with the surface subsidence prediction model in the mountainous area in the code: ① Due to the limitations of terrain conditions, the surface slip impact above the boundary of the goaf is sometimes not the largest; ② In the case of nearly full mining or super-full mining, the slip impact in the middle of the goaf is sometimes not 0; ③ The slip impact function cannot fully reflect the complex surface slip situation of coal mining in mountainous areas, and the mountain slip model needs to be revised. ④ The surface subsidence prediction model for mountainous mining has many parameters. Conventional methods are easily troubled by parameter divergence and falling into local optimality during the inversion process of parameters, and it is often difficult to obtain the optimal solution for the surface movement prediction parameters in mountainous areas.

[0023] 1.2 Principles and methods of improvement.

[0024] Through research, it was found that usually on hillsides, the gravitational force causes slippage in the downhill direction, which in turn causes sinking in the downhill direction; while in the interslope and valley areas, due to the sudden change in the slip direction or slip amount, extrusion occurs, causing the surface to rise. After analyzing the research results of predecessors, considering the impact of surface elevation changes on subsidence results, a method of using the digital elevation model (DEM) of the mining area surface to correct the movement deformation value in flat ground mining is proposed. The basic steps of this method are: ① First, combined with the terrain data of the mining area, the average elevation within the predicted range is given, and the mining depth of the corner points of the working face to be predicted is given based on this average elevation; ② Use the search algorithm to find the grid to which each predicted point belongs in the digital elevation model, and then use the bilinear interpolation method to interpolate the predicted points according to the coordinates of the corner points of the grid, calculate the elevation of each predicted point, and obtain the maximum and minimum elevations within the predicted range; ③ According to the maximum and minimum elevations, the mining depth of the original corner points of the working face to be predicted is corrected, and subsidence is predicted on this basis; ④ Based on the predicted results after the correction of the maximum and minimum elevations, linear interpolation is used to finally obtain the subsidence prediction results that basically conform to the actual situation. The flow chart of the method is shown below. Figure 1 shown.

[0025] The main formula of the mountain correction model is as follows: (1) Calculation of the estimated point elevation, such as Figure 2 As shown: (1) Where: The elevations of the four corner points of the DEM grid; For the requested point elevation.

[0026] (2) Find the maximum and minimum elevations: (2) Where: is the maximum elevation within the expected range; is the minimum elevation within the expected range.

[0027] (3) Mining depth correction: (3) Where: is the maximum mining depth after correction; is the corrected minimum mining depth; The mining depth is set according to the average elevation; is the maximum elevation within the expected range; is the minimum elevation within the expected range; It is the average elevation within the set expected range.

[0028] (4) Subsidence prediction: Since the mining subsidence prediction model adopts the probability integration method, the subsidence of an arbitrary point on the surface is only taken as an example to illustrate.

[0029] W( x , y )=W0·C( x )·C( y ) (4) Where: and It is called the subsidence distribution coefficient within the main section. x , y are the horizontal and vertical coordinates of each subsidence prediction point, respectively, W( x , y ) is the settlement amount of each estimated settlement point, and W0 is the settlement amount of the coal seam roof.

[0030] (5) Correction of the estimated results in mountainous areas: (5) Where: is the corrected sinking value; is the expected subsidence value for the minimum mining depth; is the expected subsidence value at the maximum mining depth; is the maximum elevation within the expected range; is the minimum elevation within the expected range; Estimated point elevation.

[0031] An embodiment of the present invention provides a system for estimating coal seam mining subsidence, comprising: The data acquisition module is used to obtain the mining depth of the corner points of the mining face in the coal seam mining working area.

[0032] The mining depth correction module is used to use a search algorithm to obtain the grid to which each estimated subsidence point on the surface of the mining work area belongs in the digital elevation model; according to the corner point coordinates of the grid to which each estimated subsidence point belongs, the bilinear interpolation method is used to interpolate the grid to which each estimated subsidence point belongs; according to the interpolation result, the elevation of each estimated subsidence point on the surface of the mining work area is obtained; the maximum elevation and the minimum elevation are selected from the elevations of all estimated points on the surface of the mining work area; the maximum elevation and the minimum elevation are respectively input into the mining depth correction model to correct the mining depth of the corner points of the mining working face, and the maximum value corrected corner point mining depth and the minimum value corrected corner point mining depth are obtained.

[0033] 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 result of each subsidence prediction point of the maximum value corrected corner point mining depth estimation and the subsidence prediction result of each subsidence prediction point of the minimum value corrected corner point mining depth estimation; according to the difference between the subsidence prediction result of each subsidence prediction point of the maximum value corrected corner point mining depth estimation and the subsidence prediction result of each subsidence prediction point of the minimum value corrected corner point mining depth estimation, as well as the difference between the maximum elevation and the minimum elevation, the settlement value of each subsidence prediction point is corrected using the linear interpolation method to estimate the settlement value that conforms to the actual situation.

[0034] A specific embodiment is as follows: 1. Design a software for predicting subsidence in mountainous areas.

[0035] 1.1 System introduction and overall architecture.

[0036] The system is based on the mining subsidence prediction model in the "Regulations on Coal Pillar Retention and Coal Compressing Mining for Buildings, Water Bodies, Railways and Main Wells and Lanes" and the self-designed mountain area correction model. Drawing on the advantages of existing mining subsidence prediction software, the system designs and implements a mining subsidence prediction system suitable for a certain mining area. The system strives to be easy to operate, reasonable in structure, good in visualization, and of practical value. It mainly realizes steady-state and dynamic mining subsidence prediction, contour drawing, profile drawing, subsidence area volume analysis, building damage zoning, waterlogging area analysis, protection of coal pillars, and parameter calculation.

[0037] Mining subsidence is an interdisciplinary subject, in which computer technology plays an important role. 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:

[0038] (1) This software system is a further improvement on the previous one. It adopts the "platform + plug-in" development model, is based on the AutoCAD platform, and uses .NET secondary development technology to realize mining subsidence prediction and analysis in mining areas. The system has good compatibility.

[0039] (2) The software has high integration and functional integration. You only need to use this software to complete data calculation, engineering drawing generation and other functions, without the need to use other tools to post-process the estimated data.

[0040] (3) High degree of data visualization. This system combines mining subsidence prediction theory and the principle of automatic generation of contour lines, as well as computer graphics technology, and integrates the automatic generation function of surface deformation contour lines, which can predict and output Figure 1 Integrated, easy to use.

[0041] The overall structure of a mining subsidence prediction system in a mining area is as follows: Figure 3 shown.

[0042] 1.2 Implementation and cases of predicted subsidence in mountainous areas.

[0043] <1> , Software Implementation Process (1) Establishing a project Click the menu command "Mountain Area Forecast / Project Information" to open the "Create Project" dialog box.

[0044] a) Enter the project name in the "Project Name" text box and press Enter to get the saved project path. If the user wants to re-specify the project path, click the "Select" button.

[0045] b) Click the "OK" button to save the data into the database.

[0046] c) Click the "Record Management" button to browse the currently created project information.

[0047] d) Click the "Exit" button or click the "X" in the upper right corner to exit the current window.

[0048] (2) Working face information Select the "Mountain Area Estimation / Working Face Information" command to open the "Working Face Information" dialog box.

[0049] a) In the "Project Name" list box, select the project name that contains the current work surface.

[0050] b) Enter the work surface number in the "Work surface name" text box.

[0051] c) Enter the sample thickness in millimeters in the “Sample Thickness” text box.

[0052] d) Mining start time: Enter the start time of mining on the working face.

[0053] e) Mining end time: Enter the start time of mining on the working face.

[0054] f) Estimated time: Select the estimated time for the work surface.

[0055] g) Mining speed: The mining speed is input according to the average mining speed, that is, mining speed = working face strike length / (mining end time-mining start time). When the mining speed is "+", the working face advances in the same direction as the strike, and when the mining speed is "-", the working face advances in the opposite direction as the strike.

[0056] h) The estimated parameters are: q is the sinking coefficient, tanβ is the tangent of the main influencing angle, b is the horizontal movement coefficient, θ is the maximum sinking angle, S1 is the left turning point offset, S2 is the right turning point offset, S3 is the upper turning point offset, and S4 is the lower turning point offset.

[0057] Note: The definitions of the inflection points Sleft, Sright, Sup, and Sdown are as follows: Figure 4 As shown, the inner side towards the goaf is "+", and the opposite side is "-".

[0058] i) Click the "Get Coal Seam Dip Azimuth" button to pick up the coal seam dip azimuth. The azimuth is picked using two points to determine the direction, that is, first pick up the starting point (the direction of the coal seam going uphill), then pick up an end point (the direction of the coal seam going downhill), and the starting point to the end point is the coal seam dip azimuth (you can see the prompt of the CAD command line); you can also enter it directly in the text box next to it.

[0059] j) Click the "Pick Corner Point Coordinates" button to pick the coordinates of the corner points of the work surface. Click the corner point on the CAD drawing, then enter the depth value of the corner point, press the Enter key, and the data will be saved. Pick up to 8 corner point coordinates in a counterclockwise direction. If there are less than 8 points, use the "Esc" key to exit. Note: The software only supports the estimation of convex polygon work surfaces. If the work surface is a concave polygon, the user needs to split it into several convex polygons.

[0060] k) Save.

[0061] 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. (can be modified or not) to increase the input speed.

[0062] m) You can click "Record Management" to open the "Working Surface Attribute Information Browsing" dialog box, in which you can click the "Delete Row" button to delete the current record, or click "Browse Working Surface Corner Point Coordinates" to browse the corner point coordinates of the currently selected working surface.

[0063] n) Click the "Exit" button or click the "X" in the upper right corner to exit the current window.

[0064] (3) Acquisition of expected surface targets Grid picking: Clicking the "Mountain area prediction / surface prediction point information picking" menu command will pop up the "Grid prediction target picking" dialog box (the text boxes in the dialog box are empty initially).

[0065] a) Click the "Pick" button to pick up the grid data. The grid is a rectangle covering the entire expected working surface (considering that the expected number of points should not be too many, you should pay attention 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 you will be prompted to pick the expected direction. The expected direction is determined by the starting point and the end point, that is, pick 2 points (you can see the prompt of the CAD command line). In the pop-up dialog box, select the name of the project and enter the division spacing, and then click the "Save" button to store the current data. Note: If you need to draw contour lines and other operations later, you can only execute the "Mesh Feature Pick" command.

[0066] b) If you want to browse or delete the created data, you can click the "Data Browse" button.

[0067] c) Finally exit.

[0068] (4) File generation After entering the working surface parameters, click the "Mountain Area Prediction / Generate Prediction Data File" menu command to open the "Generate Prediction Data File" dialog box. Select a project in the list box on the left, and the list box on the right will display the working surfaces it contains. Select the required working surface, then enter the average ground elevation, and finally click the "Generate Data File" button to generate the prediction file and store it in the project directory set by the user.

[0069] Click the "Edit Data File" button to open the file just generated.

[0070] Finally exit.

[0071] (5) Estimated calculation The mountain area correction model corrects the predicted results based on the digital elevation model (DEM) of the predicted area. DEM can be obtained by manual digitization or image acquisition. Its data format is (X, Y, H) and is saved as a .dat format file, such as Figure 5 shown.

[0072] Click the "Mountain Area Prediction / Prediction Calculation" menu command. This function is divided into four steps: DEM and predicted point elevation fitting, maximum elevation mining depth prediction, minimum elevation mining depth prediction, and data integration processing.

[0073] Step 1: Fitting DEM with the estimated point elevation will pop up the following interface.

[0074] Select the DEM data file and open it. After the first step is completed, the maximum elevation depth estimation is directly carried out. Due to the large amount of data to be processed, it may take a 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.

[0075] After storing the file, click "Compute End - Exit". (It should be noted that this process is performed twice, and you can follow the prompts).

[0076] After the second step is completed, the third step of minimum elevation mining depth estimation will be directly carried out. The operation is similar to the second step. After the third step is completed, the software will automatically carry out the fourth step of data integration processing. Since the amount of data to be processed may be very large, users are requested to wait patiently during the execution process.

[0077] <2> , Specific case implementation Specific test examples include Figure 6 and Figure 7 shown.

[0078] 1.3 Analysis of prediction error and effect.

[0079] The subsidence prediction errors of a mining subsidence prediction system in a certain mining area mainly include: ① model error, ② parameter error, and ③ mining thickness error.

[0080] 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, in order to ensure the accuracy and reliability of the predicted results, the mining subsidence prediction model of the software adopts the probability integral method model recommended in the "Regulations for the Retention and Compressed Coal Mining of Coal Pillars in Buildings, Water Bodies, Railways and Main Wells and Lanes". This model has been well applied in many mining areas, but the model still has certain errors in the prediction of mining subsidence in steeply inclined coal seams, extremely insufficient mining, mountainous areas, etc. For the prediction of surface subsidence in mountainous areas, a mountain correction model based on DEM was established to eliminate the influence of surface elevation changes on subsidence results; for the prediction of subsidence in extremely insufficient mining, the method of correcting subsidence prediction parameters was used to improve the prediction accuracy. Overall, the prediction accuracy of the subsidence prediction model for mining subsidence in gently inclined coal seams, near sufficient mining, and flat surface areas is relatively high, while the prediction accuracy of mining subsidence in steeply inclined coal seams, extremely insufficient mining, mountainous areas, etc. is slightly lower.

[0081] ②Parameter 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 predicted 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 made according to the parameters of the 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 influence of the mined working face on the expected working face should be considered when selecting parameters, otherwise the prediction result will be too small.

[0082] ③ Error in mining thickness: Due to the good coal seam conditions in a certain mining area, most of the mining is done by top coal caving. The mining thickness will directly affect the accuracy of the predicted results of mining subsidence. 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.

[0083] 1.4 Concluding results.

[0084] (1) Through in-depth analysis of the data and comparison of the applicability of existing mining subsidence prediction models in a certain mining area, the probability integration method model recommended in the "Regulations on Coal Pillar Retention and Compressed Coal Mining in Buildings, Water Bodies, Railways and Main Shafts and Tunnels" (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.

[0085] (2) Combine the existing surface movement observation station data to obtain the mining subsidence prediction parameters, and use 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.

[0086] (3) The mountain mining subsidence prediction and mapping software system has been promoted and used in ten mining areas of a coal industry group. Combined with the actual mining working faces of multiple coal mines, the mining subsidence prediction software of a mining area was used to perform subsidence prediction calculations. Prediction and analysis were performed from the perspectives of insufficient mining of a single working face and sufficient mining of multiple working faces. The prediction calculation results basically conform to the actual conditions of each mine. From the ongoing rock movement observation of a coal mine, it can be seen that the surface movement deformation value predicted by the software is basically consistent with the rock movement observation results, which is in line with the actual situation.

[0087] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for estimating coal seam mining subsidence, characterized in that: The following steps are involved: Obtaining the mining depth of the corner points of the mining working face in the coal seam mining working area; wherein the coal seam mining working area includes the mining working face and the ground surface above the mining working face, and the mining depth of the corner points is the vertical depth of the points at each corner of the mining working face relative to the ground surface; The search algorithm is used to obtain the grid to which each estimated subsidence point on the surface of the mining work area belongs in the digital elevation model; the bilinear interpolation method is used to interpolate the grid to which each estimated subsidence point belongs according to the coordinates of the corner points of the grid to which each estimated subsidence point belongs; the elevation of each estimated subsidence point on the surface of the mining work area is obtained according to the interpolation result; the maximum elevation and the minimum elevation are selected from the elevations of all estimated points on the surface of the mining work area; The maximum elevation and the minimum elevation are respectively input into the mining depth correction model to correct the mining depth of the corner points of the mining working face, and the maximum value correction corner point mining depth and the minimum value correction corner point mining depth are obtained; 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 result of each subsidence prediction point of the maximum value corrected corner point mining depth estimation and the subsidence prediction result of each subsidence prediction point of the minimum value corrected corner point mining depth estimation; According to the difference between the settlement estimation result of each settlement estimation point of the maximum value corrected corner point mining depth estimation and the settlement estimation result of each settlement estimation point of the minimum value corrected corner point mining depth estimation, as well as the difference between the maximum elevation and the minimum elevation, the settlement value of each settlement estimation point is corrected using the linear interpolation method to estimate the settlement value that conforms to the actual situation.

2. A method for estimating coal seam mining subsidence as claimed in claim 1, characterized in that: The step of obtaining the elevation of each estimated subsidence point on the surface of the mining area comprises: The bilinear interpolation method is used to interpolate the coordinates of the corner points of the grid to which each subsidence estimation point belongs; in, are the elevation coordinates of the four corner points of a grid in the digital elevation model. For each subsidence estimation point on the surface of the mining work area P The elevation of x For each settlement estimation point P The horizontal axis value of x 1 for H 1 and H The horizontal coordinate value of 3, x 2 for H 2 and H The horizontal axis value of 4, y For each settlement estimation point P The vertical coordinate value of y 1 for H 1 and H The vertical coordinate value of 2, y 2 for H 3 and H The vertical coordinate value of 4.

3. A method for estimating coal seam mining subsidence as claimed in claim 1, characterized in that: The specific steps of obtaining the maximum value correction corner point mining depth and the minimum value correction corner point mining depth include: Input the maximum elevation into the mining depth correction model to obtain the maximum value correction corner mining depth : Input the minimum elevation into the mining depth correction model to obtain the minimum corrected corner mining depth : in, The mining depth of the corner points of the mining face is corrected to the maximum value. The depth of the corner point of the mining face is corrected to the minimum value. The mining depth of the corner points of the mining face is is the maximum elevation, is the minimum elevation, is the average elevation.

4. A method for estimating coal seam mining subsidence as claimed in claim 1, characterized in that: The specific steps of obtaining the subsidence estimation result of each subsidence estimation point of the maximum value correction corner point mining depth estimation and the subsidence estimation result of each subsidence estimation point of the minimum value correction corner point mining depth estimation include: The maximum value of the corner point of the mining face is corrected by the corner point mining depth, and the minimum value of the corner point of the mining face is corrected by the corner point mining depth, which are input into the mining subsidence prediction model: W( x , y )=W0·C( x )·C( y ) in, and is the subsidence distribution coefficient within the main section; x , y are the horizontal and vertical coordinates of each subsidence prediction point, respectively, W( x , y ) is the settlement amount of each estimated settlement point, and W0 is the settlement amount of the coal seam roof; The subsidence estimation result of each subsidence estimation point estimated by the maximum value-corrected corner point mining depth and the subsidence estimation result of each subsidence estimation point estimated by the minimum value-corrected corner point mining depth are obtained.

5. A method for estimating coal seam mining subsidence as claimed in claim 1, characterized in that: The linear interpolation method is used to correct the settlement value of each settlement estimation point, and the specific steps include: Obtain a ratio of the first difference to the second difference based on a first difference between a subsidence estimation result of each subsidence estimation point of the maximum value-corrected corner point mining depth estimation and a subsidence estimation result of each subsidence estimation point of the minimum value-corrected corner point mining depth estimation, and a second difference between the maximum elevation and the minimum elevation; According to the ratio of the first difference to the second difference and the elevation of the subsidence estimation point, the subsidence value of the subsidence estimation point is obtained using linear interpolation. : in, is the corrected subsidence value, is the minimum settlement prediction result, is the maximum settlement prediction result, is the maximum elevation, is the minimum elevation, Estimated point elevation.

6. A method for estimating coal seam mining subsidence as claimed in claim 1, characterized in that: The specific steps of obtaining the mining depth of the corner point of the mining working face in the coal seam mining working area include: Use digital elevation model to obtain the elevation data of the ground surface in the mining working area of ​​the coal seam; The average elevation is obtained based on the elevation data of the surface of the mining work area, and the mining depth of the corner point of the mining work face is obtained based on the difference between the average elevation and the vertical coordinates of the corner point of the mining work face.

7. A coal seam mining subsidence prediction system, characterized in that: include: A data acquisition module is used to obtain the mining depth of the corner points of the mining face in the coal seam mining working area; The mining depth correction module is used to obtain the grid to which each estimated subsidence point on the surface of the mining work area belongs in the digital elevation model by using a search algorithm; According to the coordinates of the corner points of the grid to which each subsidence estimation point belongs, the grid to which each subsidence estimation point belongs is interpolated by using the bilinear interpolation method; according to the interpolation result, the elevation of each subsidence estimation point on the surface of the mining work area is obtained; the maximum elevation and the minimum elevation are selected from the elevations of all the estimation points on the surface of the mining work area; The maximum elevation and the minimum elevation are respectively input into the mining depth correction model to correct the mining depth of the corner points of the mining working face, and the maximum value correction corner point mining depth and the minimum value correction corner point mining depth are obtained; 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 result of each subsidence prediction point of the maximum value corrected corner point mining depth estimation and the subsidence prediction result of each subsidence prediction point of the minimum value corrected corner point mining depth estimation; According to the difference between the settlement estimation result of each settlement estimation point of the maximum value corrected corner point mining depth estimation and the settlement estimation result of each settlement estimation point of the minimum value corrected corner point mining depth estimation, as well as the difference between the maximum elevation and the minimum elevation, the settlement value of each settlement estimation point is corrected using the linear interpolation method to estimate the settlement value that conforms to the actual situation.

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