Any-shape multi-working-face mining surface subsidence prediction analysis method and device

Abstract

The invention relates to a method and device for predicting and analyzing mining surface subsidence of multiple working faces in any shape, and the method comprises the steps: collecting working face mining subsidence basic data, mining subsidence prediction parameters and predicted point basic data of a plurality of working faces in any shape, and constructing a working face data file and a predicted point data file; based on a probability integral method, performing surface subsidence prediction calculation operation on the plurality of working surfaces in any shape to obtain three-dimensional surface movement deformation data; and performing gridding operation on the three-dimensional surface movement deformation data to obtain gridding data, and performing statistical analysis and data visualization processing on the gridding data to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data. Therefore, the problems that according to an existing mining subsidence prediction technology, prediction results and coal mine graphs and maps are difficult to combine, a unified data management system cannot be formed, the calculation precision and the calculation amount are difficult to balance, and the post-processing function is insufficient are solved.

Description

Technical Field

[0001] The present application relates to the technical field of surface subsidence prediction, and in particular to a method and device for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes. Background Art

[0002] At present, the probability integration method is mainly used in the prediction and forecast of mining subsidence in my country. Most mining subsidence prediction programs are compiled in BASIC, C language or Matlab, so as to complete the prediction and calculation of surface subsidence after mining of a single rectangular working face. The mathematical models of prediction and calculation mostly use the prediction calculation formula derived under the limited mining conditions of the rectangular working face. For the case of multiple working faces, the existing technology can divide the working face into multiple rectangular working faces in the form of rectangular segmentation for superimposed calculation.

[0003] However, the existing mining subsidence prediction programs have the following shortcomings:

[0004] (1) The coordinates use an independent estimated calculation coordinate system, which makes it difficult to combine the estimated results with coal mine graphics and drawings;

[0005] (2) Most mining subsidence prediction software uses data files to manage data, which cannot form a unified data management system. In addition, most prediction software requires manual preparation of data files, which is not only prone to errors, but also increases the workload of data preparation.

[0006] (3) For work surfaces of arbitrary shapes, the estimated calculation usually adopts the form of rectangular subdivision.

[0007] The irregular shape is divided into multiple rectangular working surfaces according to certain rules, and the calculation results of multiple working surfaces are superimposed; the rectangular subdivision form often uses approximate division processing for multiple deformation boundaries. The calculation accuracy of this method is related to the size of the subdivided rectangle; when the subdivided rectangle is large, it will cause a large error in the expected result; when the subdivided rectangle is small, the calculation amount will increase, making the calculation time longer, and the expected results are all approximate values;

[0008] (4) Insufficient post-processing function.

[0009] At present, the domestic mining subsidence prediction system does not have the function of drawing movement deformation curves and related contour lines, and is unable to visualize the prediction results and conduct relevant analysis.

[0010] In summary, the existing mining subsidence prediction technology is difficult to combine the predicted results with coal mine graphics and drawings, cannot form a unified data management system, and is difficult to balance the calculation accuracy and calculation amount, and the post-processing function is insufficient, which needs to be solved urgently. Summary of the invention

[0011] The present application provides a method and device for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes, so as to solve the problems that the existing mining subsidence prediction technology is difficult to combine the predicted results with coal mine graphics and drawings, cannot form a unified data management system, is difficult to balance the calculation accuracy and calculation amount, and has insufficient post-processing functions.

[0012] The first aspect of the present application provides a method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes, comprising the following steps: collecting basic data on mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in a target mining area, and constructing a working face data file and a predicted point data file through the basic data on mining subsidence of the working faces, the mining subsidence prediction parameters and the basic data of predicted points; performing surface subsidence prediction calculation operations on the multiple working faces of arbitrary shapes based on the working face data file, the predicted point data file and a preset probability integral method to obtain three-dimensional surface movement deformation data corresponding to the multiple working faces of arbitrary shapes; performing gridding operations on the three-dimensional surface movement deformation data to obtain gridded data corresponding to the three-dimensional surface movement deformation data, and performing statistical analysis and data visualization processing on the gridded data to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data.

[0013] Optionally, in one embodiment of the present application, the basic data of mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in the target mining area are collected, and working face data files and predicted point data files are constructed through the basic data of mining subsidence, the mining subsidence prediction parameters and the basic data of predicted points, including: collecting the basic data of mining subsidence and the mining subsidence prediction parameters of multiple working faces of arbitrary shapes in the target mining area, wherein the basic data of mining subsidence of the working faces include mining depth information, mining thickness information, working face position information, working face corner point coordinate information, coal seam inclination information and strike azimuth information corresponding to the multiple working faces of arbitrary shapes, and the mining subsidence prediction parameters include subsidence coefficient, horizontal movement coefficient, main influence angle tangent, inflection point offset and mining influence propagation angle; based on the basic data of mining subsidence of the working faces and the mining subsidence prediction parameters, establish the working face data file; select the target corner point in the target mine map of the target mining area, and generate the target rectangular prediction area through the target corner point, so as to determine the surface area prediction range in the basic data of the prediction point according to the target rectangular prediction area; determine the starting point coordinates, end point coordinates and line segment point spacing of the target line segment in the target mine map, and generate the surface linear prediction point information in the basic data of the prediction point through the starting point coordinates, the end point coordinates and the line segment point spacing; determine the target prediction direction in the target mine map, so as to generate any discrete point in the target mine map according to the target prediction direction, and obtain the surface arbitrary prediction point information in the basic data of the prediction point through the arbitrary discrete point; generate the prediction point data file based on the surface arbitrary prediction point information, the surface linear prediction point information and the surface area prediction range.

[0014] Optionally, in one embodiment of the present application, the surface subsidence prediction and calculation operation is performed on the multiple arbitrarily shaped working surfaces based on the working surface data file, the predicted point data file and the preset probability integral method to obtain three-dimensional surface movement and deformation data corresponding to the multiple arbitrarily shaped working surfaces, including: based on a preset concave and convex vertex judgment strategy, and in combination with the working surface data file and the predicted point data file, a triangulation operation is performed on each of the multiple arbitrarily shaped working surfaces to obtain a plurality of triangular areas corresponding to each arbitrarily shaped working surface; according to the probability integral method, two Romberg integral operations are performed on the multiple triangular areas to obtain an integral calculation result corresponding to each of the multiple triangular areas, and the integral calculation results corresponding to each triangular area are accumulated to obtain the three-dimensional surface movement and deformation data corresponding to each arbitrarily shaped working surface, wherein the three-dimensional surface movement and deformation data include surface subsidence information and the target prediction direction and the inclination information, curvature information, horizontal movement information and horizontal deformation information perpendicular to the target prediction direction.

[0015] Optionally, in one embodiment of the present application, the method of triangulating each of the plurality of arbitrarily shaped working surfaces based on a preset convex and concave vertex determination strategy and in combination with the working surface data file and the predicted point data file to obtain a plurality of triangular regions corresponding to each of the arbitrarily shaped working surfaces comprises: reading each corner point information in each arbitrarily shaped working surface in turn in a counterclockwise direction, and establishing a bidirectional circular linked list according to each corner point information; determining the concavity and convexity of each node in the bidirectional circular linked list to obtain a plurality of convex nodes in the bidirectional circular linked list according to the concavity and convexity; combining each of the plurality of convex nodes with the front and rear nodes to obtain a triangle corresponding to each convex node, and judging whether there are other vertices in the triangle corresponding to each convex node, wherein if the plurality of convex nodes are If there is at least one target convex node corresponding to the triangle that does not have the other vertices, then calculate the triangle weight of the target triangle corresponding to each target convex node in the at least one target convex node; compare the triangle weights corresponding to each target convex node to obtain the maximum triangle weight, and save the target triangle corresponding to the maximum triangle weight in the bidirectional circular linked list, and delete the middle convex nodes that constitute the target triangle corresponding to the maximum triangle weight, and at the same time determine whether the number of nodes in the bidirectional circular linked list is greater than 3; if the number of nodes in the bidirectional circular linked list is greater than 3, re-determine the concavity and convexity of each node in the bidirectional circular linked list, otherwise, form a triangle with the last three nodes in the bidirectional circular linked list, and delete the last three nodes in the bidirectional circular linked list to obtain multiple triangular areas corresponding to each arbitrary shape working surface.

[0016] Optionally, in one embodiment of the present application, the gridding operation is performed on the three-dimensional surface movement and deformation data to obtain gridded data corresponding to the three-dimensional surface movement and deformation data, and the gridded data is statistically analyzed and data visualized to generate and display statistical image data corresponding to the three-dimensional surface movement and deformation data, including: gridding the three-dimensional surface movement and deformation data through preset SURFER software to generate the gridded data; statistically analyzing the gridded data to obtain area and volume statistical data, terrain factor analysis data, profile analysis results and geostatistical analysis results in the statistical image data; data visualization processing is performed on the gridded data to obtain contour maps, profile maps, three-dimensional surface maps and movement and deformation vector maps in the statistical image data; and the working surface base maps of the multiple arbitrarily shaped working surfaces are obtained, and layer overlay operations are performed on the contour maps, the profile maps, the three-dimensional surface maps, the movement and deformation vector maps and the working surface base map to generate overlay maps corresponding to the multiple arbitrarily shaped working surfaces in the statistical image data.

[0017] The second aspect of the present application provides a device for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes, including: an acquisition module, used to collect basic data on mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in a target mining area, and to construct working face data files and predicted point data files through the basic data on mining subsidence of the working faces, the mining subsidence prediction parameters and the basic data of predicted points; a prediction module, used to perform surface subsidence prediction calculation operations on the multiple working faces of arbitrary shapes based on the working face data files, the predicted point data files and a preset probability integral method, so as to obtain three-dimensional surface movement and deformation data corresponding to the multiple working faces of arbitrary shapes; an analysis module, used to perform gridding operations on the three-dimensional surface movement and deformation data, so as to obtain gridded data corresponding to the three-dimensional surface movement and deformation data, and to perform statistical analysis and data visualization processing on the gridded data, so as to generate and display statistical image data corresponding to the three-dimensional surface movement and deformation data.

[0018] Optionally, in one embodiment of the present application, the acquisition module includes: an acquisition unit, used to collect the basic data of mining subsidence of the working faces and the mining subsidence prediction parameters of multiple arbitrarily shaped working faces in the target mining area, wherein the basic data of mining subsidence of the working faces include mining depth information, mining thickness information, working face position information, working face corner point coordinate information, coal seam inclination information and strike azimuth information corresponding to the multiple arbitrarily shaped working faces, and the mining subsidence prediction parameters include subsidence coefficient, horizontal movement coefficient, main influence angle tangent, inflection point offset distance and mining influence propagation angle; an establishment unit, used to establish the working face data file based on the basic data of mining subsidence of the working faces and the mining subsidence prediction parameters; a determination unit, used to select target corner points in the target mine map of the target mining area, and through the target angle A target rectangular prediction area is generated by the point, so as to determine the surface area prediction range in the basic data of the predicted point according to the target rectangular prediction area; a first generating unit is used to determine the starting point coordinates, the ending point coordinates and the line segment point spacing of the target line segment in the target mineral map, and generate the surface linear predicted point information in the basic data of the predicted point through the starting point coordinates, the ending point coordinates and the line segment point spacing; a second generating unit is used to determine the target prediction direction in the target mineral map, so as to generate any discrete point in the target mineral map according to the target prediction direction, and obtain the surface arbitrary predicted point information in the basic data of the predicted point through the arbitrary discrete point; a third generating unit is used to generate the predicted point data file based on the surface arbitrary predicted point information, the surface linear predicted point information and the surface area prediction range.

[0019] Optionally, in one embodiment of the present application, the prediction module includes: a triangulation unit, which is used to perform a triangulation operation on each of the multiple arbitrarily shaped working surfaces based on a preset concave and convex vertex determination strategy and in combination with the working surface data file and the predicted point data file to obtain a plurality of triangular areas corresponding to each of the arbitrarily shaped working surfaces; an integral calculation unit, which is used to perform two Romberg integral operations on the multiple triangular areas according to the probability integral method to obtain an integral calculation result corresponding to each of the multiple triangular areas, and accumulate the integral calculation results corresponding to each triangular area to obtain three-dimensional surface movement deformation data corresponding to each arbitrarily shaped working surface, wherein the three-dimensional surface movement deformation data includes surface subsidence information and the target prediction direction and inclination information, curvature information, horizontal movement information and horizontal deformation information perpendicular to the target prediction direction.

[0020] Optionally, in one embodiment of the present application, the prediction module further includes: a reading unit, which is used to read each corner point information in each arbitrary shape working surface in turn based on the counterclockwise direction, and establish a bidirectional circular linked list according to each corner point information; a concavity and convexity detection unit, which is used to determine the concavity and convexity of each node in the bidirectional circular linked list, so as to obtain multiple convex nodes in the bidirectional circular linked list according to the concavity and convexity; a combination unit, which is used to combine each convex node in the multiple convex nodes with the front and rear nodes to obtain the triangle corresponding to each convex node, and determine whether there are other vertices in the triangle corresponding to each convex node, wherein if there is at least one target convex node in the multiple convex nodes and the triangle corresponding to the convex node does not have the other vertices, then the at least one target convex node is calculated. The triangle weight of the target triangle corresponding to each target convex node in the convex nodes; a comparison unit, used to compare the triangle weights corresponding to each target convex node to obtain the maximum triangle weight, and save the target triangle corresponding to the maximum triangle weight in the bidirectional circular linked list, and delete the middle convex nodes that constitute the target triangle corresponding to the maximum triangle weight, and at the same time determine whether the number of nodes in the bidirectional circular linked list is greater than 3; a deletion unit, used to redetermine the convexity of each node in the bidirectional circular linked list if the number of nodes in the bidirectional circular linked list is greater than 3, otherwise the last three nodes in the bidirectional circular linked list are combined into a triangle, and the last three nodes in the bidirectional circular linked list are deleted to obtain multiple triangle areas corresponding to each arbitrary shape working surface.

[0021] Optionally, in one embodiment of the present application, the analysis module includes: a gridding unit, used to perform a gridding operation on the three-dimensional surface movement and deformation data through a preset SURFER software to generate the gridded data; a statistical analysis unit, used to perform a statistical analysis on the gridded data to obtain area and volume statistical data, terrain factor analysis data, profile analysis results and geostatistical analysis results in the statistical image data; a visualization unit, used to perform data visualization processing on the gridded data to obtain contour maps, profile maps, three-dimensional surface maps and movement and deformation vector maps in the statistical image data; a fourth generation unit, used to obtain the working surface base maps of the multiple arbitrarily shaped working surfaces, and perform a layer overlay operation on the contour maps, the profile maps, the three-dimensional surface maps, the movement and deformation vector maps and the working surface base maps to generate overlay maps corresponding to the multiple arbitrarily shaped working surfaces in the statistical image data.

[0022] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for predicting and analyzing surface subsidence in multi-working face mining of arbitrary shapes as described in the above embodiments.

[0023] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for predicting and analyzing surface subsidence in multi-working face mining of arbitrary shapes.

[0024] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned method for predicting and analyzing surface subsidence in multi-working face mining of arbitrary shapes.

[0025] Therefore, the embodiments of the present application have the following beneficial effects:

[0026] The embodiment of the present application can collect the basic data of mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in the target mining area, and construct the working face data file and the predicted point data file through the basic data of mining subsidence, mining subsidence prediction parameters and basic data of predicted points; based on the working face data file, the predicted point data file and the preset probability integral method, perform surface subsidence prediction calculation operations on multiple working faces of arbitrary shapes to obtain three-dimensional surface movement deformation data corresponding to multiple working faces of arbitrary shapes; perform gridding operations on the three-dimensional surface movement deformation data to obtain gridded data corresponding to the three-dimensional surface movement deformation data, and perform statistical analysis and data visualization processing on the gridded data to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data. The present application manages the working face and mining subsidence prediction parameters by adopting the database concept, reduces the workload of working face data input and management, and the mining boundary error caused by the rectangular subdivision of the working face, improves the prediction accuracy of mining subsidence, realizes the automation and visual expression of mining subsidence prediction analysis map production, and improves the work efficiency of mining subsidence prediction. This solves the problems of existing mining subsidence prediction technology, such as difficulty in combining predicted results with coal mine graphics and drawings, inability to form a unified data management system, difficulty in balancing calculation accuracy and amount, and insufficient post-processing functions.

[0027] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0029] Figure 1 A flowchart of a method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes provided according to an embodiment of the present application;

[0030] Figure 2 A schematic diagram of a basic interface for work surface data management provided by an embodiment of the present application;

[0031] Figure 3 A schematic diagram of a basic interface for management of mining subsidence prediction parameters in a mining area provided by an embodiment of the present application;

[0032] Figure 4 A schematic diagram of a basic operation interface for preparing a working surface data file provided by an embodiment of the present application;

[0033] Figure 5 A schematic diagram of a basic operation interface for preparing a regional estimated range data file provided by an embodiment of the present application;

[0034] Figure 6 A schematic diagram of a basic operation interface for generating a linear predicted point data file provided by an embodiment of the present application;

[0035] Figure 7 A schematic diagram of a basic operation interface for generating an optional point data file provided by an embodiment of the present application;

[0036] Figure 8 A schematic diagram of a working surface data file provided for an embodiment of the present application;

[0037] Fig. 9 A schematic diagram of a predicted point data file provided for an embodiment of the present application;

[0038] Fig.10 A schematic diagram of a surface mining subsidence prediction process provided for an embodiment of the present application;

[0039] Fig.11 A schematic diagram of a basic operation interface for estimated calculation provided by an embodiment of the present application;

[0040] Fig.12 A schematic diagram of an execution flow of dividing a work surface of any shape into triangular areas provided in one embodiment of the present application;

[0041] Fig.13 A schematic diagram of an execution flow of determining whether a triangle contains other vertices provided in one embodiment of the present application;

[0042] Fig.14 A schematic diagram of a mining subsidence analysis process provided for an embodiment of the present application;

[0043] Fig.15 A schematic diagram of a main menu of a mining subsidence prediction part provided for an embodiment of the present application;

[0044] Fig.16 A schematic diagram of the execution logic of a method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes provided in one embodiment of the present application;

[0045] Fig.17 This is an example diagram of a device for predicting and analyzing ground subsidence in mining with multiple working faces of arbitrary shapes according to an embodiment of the present application;

[0046] Fig.18 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0047] Among them, 10-arbitrary shape multi-working face mining surface subsidence prediction and analysis device; 100-acquisition module, 200-prediction module, 300-analysis module; 1801-memory, 1802-processor, 1803-communication interface. DETAILED DESCRIPTION

[0048] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0049] The following describes the method and device for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes according to the embodiment of the present application with reference to the accompanying drawings. In view of the problems mentioned in the above background technology, the present application provides a method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes, in which basic data of mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in the target mining area are collected, and working face data files and predicted point data files are constructed through the basic data of mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces; based on the working face data files, predicted point data files and the preset probability integral method, surface subsidence prediction calculation operations are performed on multiple working faces of arbitrary shapes to obtain three-dimensional surface movement deformation data corresponding to multiple working faces of arbitrary shapes; the three-dimensional surface movement deformation data is gridded to obtain gridded data corresponding to the three-dimensional surface movement deformation data, and the gridded data is statistically analyzed and data visualized to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data. This application adopts the database concept to manage the working face and mining subsidence prediction parameters, reduces the workload of working face data input and management, and the mining boundary error caused by the rectangular subdivision of the working face, improves the prediction accuracy of mining subsidence, realizes the automation and visual expression of mining subsidence prediction analysis map production, and improves the work efficiency of mining subsidence prediction. Thus, it solves the problems that the existing mining subsidence prediction technology is difficult to combine the predicted results with coal mine graphics, cannot form a unified data management system, and is difficult to balance the calculation accuracy and calculation amount, and the post-processing function is insufficient.

[0050] Specifically, Figure 1 A flow chart of a method for predicting and analyzing surface subsidence in multi-working face mining of arbitrary shapes provided in an embodiment of the present application.

[0051] like Figure 1 As shown, the surface subsidence prediction and analysis method for mining with multiple working faces of arbitrary shapes includes the following steps:

[0052] In step S101, basic data of mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in the target mining area are collected, and working face data files and predicted point data files are constructed through the basic data of mining subsidence, mining subsidence prediction parameters and basic data of predicted points.

[0053] The embodiments of the present application can first collect basic data on mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in the target mining area through a mining face data management program and a mining subsidence prediction parameter management program.

[0054] Among them, the mining working face data management program is a management program for establishing a mining working face database. By operating this program, it is possible to add, delete, and modify the working face number, name, start date, end date, strike azimuth, mining thickness, coal seam inclination, corner point coordinates indicating the working face position, and mining depth basic information; in addition, the embodiment of the present application can also use this program to directly extract the basic information of the working face from the mining area AutoCAD graphics, thereby realizing the acquisition and management of the basic data of mining subsidence of the mining area working face.

[0055] The mining subsidence prediction parameter management program for mining areas is a management program for establishing a mining subsidence prediction parameter database for mining areas. By operating the program, the numbering, subsidence coefficient, horizontal movement coefficient, main influence angle tangent, mining influence propagation angle, left turning point offset distance, right turning point offset distance, upper turning point offset distance, and lower turning point offset distance of mining subsidence prediction parameters in different areas of the mining area can be realized. Addition, deletion, and modification. Therefore, the embodiments of the present application can manage the mining subsidence parameter results of the mining area through the program to achieve effective management of mining subsidence prediction parameters in the mining area.

[0056] Optionally, in one embodiment of the present application, basic data on mining subsidence of working faces of multiple arbitrarily shaped working faces in the target mining area, mining subsidence prediction parameters and basic data on predicted points are collected, and working face data files and predicted point data files are constructed through the basic data on mining subsidence of working faces, mining subsidence prediction parameters and basic data on predicted points, including: collecting basic data on mining subsidence of working faces of multiple arbitrarily shaped working faces in the target mining area and mining subsidence prediction parameters, wherein the basic data on mining subsidence of working faces include mining depth information, mining thickness information, working face position information, working face corner point coordinate information, coal seam inclination information and strike azimuth information corresponding to multiple arbitrarily shaped working faces, and mining subsidence prediction parameters include subsidence coefficient, horizontal movement coefficient, main influence angle tangent, inflection point offset and mining influence propagation angle; based on working The basic data of surface mining subsidence and mining subsidence prediction parameters are used to establish a working face data file; select target corner points in the target mine map of the target mining area, and generate a target rectangular prediction area through the target corner points, so as to determine the surface area prediction range in the basic data of the prediction point according to the target rectangular prediction area; determine the starting point coordinates, end point coordinates and segment point spacing of the target line segment in the target mine map, and generate the surface linear prediction point information in the basic data of the prediction point through the starting point coordinates, end point coordinates and segment point spacing; determine the target prediction direction in the target mine map, so as to generate any discrete point in the target mine map according to the target prediction direction, and obtain the surface arbitrary prediction point information in the basic data of the prediction point through any discrete point; generate a prediction point data file based on the surface arbitrary prediction point information, surface linear prediction point information and surface area prediction range.

[0057] It should be noted that the basic data management generation part in the embodiment of the present application can adopt the database concept to manage the mining working face and mining subsidence prediction parameters, and provide basic data for subsidence prediction. The innovation of this process is mainly reflected in the database management of working face data and mining subsidence prediction parameters.

[0058] Before conducting a mining subsidence prediction, the embodiments of the present application need to input the basic parameters required for the prediction, such as the basic working face parameters (mining depth, mining thickness, working face position, working face corner coordinates, coal seam inclination, strike azimuth), mining area subsidence prediction parameters (sinking coefficient, horizontal movement coefficient, main influencing angle tangent, inflection point offset distance, mining influence propagation angle) and surface predicted range, into a preset surface subsidence prediction and analysis system.

[0059] like Figure 2 As shown, the embodiment of the present application can realize the addition, deletion and modification of the basic data of the working face, and can directly select the working face position coordinates from the mine area AUTOCAD drawing, thereby reducing the manual input workload and input errors; Figure 3 As shown, the embodiment of the present application can realize the addition, deletion and modification of mining subsidence prediction parameters in the mining area.

[0060] Based on the editing of the above data, Figure 4 As shown, the embodiment of the present application can select multiple working faces participating in the prediction and their corresponding mining subsidence prediction parameters from all working faces in the mining area, and then generate a working face data file for mining subsidence prediction.

[0061] In the actual implementation process, the selection of the estimated range of the surface area is achieved by directly selecting corner points on the mine map. Specifically, the embodiment of the present application can select the lower left corner point (the smallest X coordinate in the area and the smallest Y coordinate in the area) and the upper right corner point (the largest X coordinate in the area and the largest Y coordinate in the area) of the estimated area on the mine map data to determine a rectangular estimated area, such as Figure 5 As shown, the estimated file is automatically generated by specifying the X-direction interval, the Y-direction interval and the estimated direction.

[0062] Figure 6 The basic operation interface for generating linear estimated point data files is shown. Figure 6 As shown, the linear prediction data file can be generated by specifying the starting point of the line segment (starting point X coordinate and starting point Y coordinate), the end point (end point X coordinate and end point Y coordinate) and the point spacing along the line segment. The prediction direction can be set to three situations: along the straight line direction, perpendicular to the straight line direction and custom direction.

[0063] Figure 7 The basic operation interface for generating arbitrary point data files is shown. Figure 7As shown, any discrete point can generate a data file by directly selecting and specifying the expected direction (ie, the target prediction direction) on the mining area map, and the embodiment of the present application can delete the selected point.

[0064] After completing the basic data management and generation part, you can get the basic data of the working face (contained in the working face data file) and the basic data of the predicted points (contained in the predicted points data file) required for the prediction calculation. Both files are ASCII text files.

[0065] Among them, the working surface data file description:

[0066] (1) The first row of data: the number of working surfaces involved in the calculation;

[0067] (2) Enter the data of the working surface involved in the calculation starting from the second line.

[0068] 1) The first line includes eight data: working face number, working face name (separated by double quotes), working face start mining date (separated by double quotes), working face stop mining date (separated by double quotes), mining thickness and strike azimuth. The data are separated by commas or spaces.

[0069] 2) The number of corner points of the working surface;

[0070] 3) Coordinates of working face corner points: X coordinate, Y coordinate, mining depth.

[0071] 4) Probability integral method parameters used for prediction (8 data, input in two lines):

[0072] The first row contains 4 data: subsidence coefficient, horizontal movement data, main impact angle tangent, and mining impact propagation angle;

[0073] The second line contains the offset data of the four inflection points: left inflection point offset, right inflection point offset, upper inflection point offset, and lower inflection point offset.

[0074] For multiple working surfaces, after entering the data of the first working surface, you need to enter the contents of multiple corresponding working surfaces 1) to 4), such as Figure 8 shown.

[0075] Estimated point data file description:

[0076] (1) The first row, the number of estimated points;

[0077] (2) Enter the coordinates of the estimated point and the estimated direction starting from the second line, including the point number (integer) of the estimated point, the X coordinate of the estimated point, the Y coordinate of the estimated point, and the estimated direction.

[0078] Then list the data of the points to be estimated one by one. The point numbers should be incremented by 1 in order. The number of estimated points should be the same as the number of estimated points in the first row, such as Fig. 9 shown.

[0079] In the embodiment of the present application, the principle basis of surface mining subsidence prediction is the probability integral method based on random medium theory; according to the principle of probability integral method, the calculation formula of flat ground subsidence, horizontal movement, inclination, curvature and horizontal deformation of non-rectangular working surface is:

[0080] The mathematical expression for the flat sinking of a non-rectangular working surface is:

[0081]

[0082] The mathematical expression for the inclination of a non-rectangular working surface is:

[0083]

[0084] The mathematical expression for the curvature of a non-rectangular working surface is:

[0085]

[0086] The mathematical expression for the horizontal movement of a non-rectangular working surface is:

[0087]

[0088] The mathematical expression for the horizontal deformation of a non-rectangular working surface is:

[0089]

[0090] Among them, the maximum sinking value is W0=mqcosα, and the main impact radius is r=H / tanβ.

[0091] In the above formula, W0 represents the maximum subsidence value; q represents the subsidence coefficient; b represents the horizontal movement coefficient; m represents the mining thickness; r represents the main influence radius; tanβ represents the mining influence propagation angle; D is the calculated mining area, that is, the mining area after considering the inflection point offset distance, which generally appears as a polygon on the mining plane.

[0092] It should be noted that the above formula considers the case where the mining thickness is a constant. When the mining thickness changes in the mining area, a triple integral is required, that is, the mining thickness M(s,t) is integrated as a function of the variables s and t. Generally speaking, the change in mining thickness within the mining range of the same mining face will not be too large, so it can be regarded as a constant.

[0093] In step S102, based on the working surface data file, the predicted point data file and the preset probability integral method, surface subsidence prediction calculation operations are performed on multiple arbitrarily shaped working surfaces to obtain three-dimensional surface movement deformation data corresponding to the multiple arbitrarily shaped working surfaces.

[0094] Furthermore, the embodiments of the present application also need to be based on a mining subsidence prediction program, and predict and calculate the surface movement and deformation caused by mining of multiple working faces of arbitrary shapes according to the basic principles of the probability integral method. The program can realize the calculation of the surface subsidence, inclination, curvature, horizontal movement, and horizontal deformation movement deformation values ​​caused by mining, and output the prediction and calculation results to a data file, thereby realizing the prediction and calculation of surface movement and deformation caused by mining of arbitrary shapes of working faces and multiple working faces in the mining area, so as to obtain three-dimensional surface movement deformation data corresponding to multiple working faces of arbitrary shapes.

[0095] Optionally, in one embodiment of the present application, based on the working surface data file, the predicted point data file and the preset probability integral method, a surface subsidence prediction calculation operation is performed on multiple arbitrarily shaped working surfaces to obtain three-dimensional surface movement and deformation data corresponding to the multiple arbitrarily shaped working surfaces, including: based on the preset concave and convex vertex judgment strategy, and in combination with the working surface data file and the predicted point data file, a triangulation operation is performed on each of the multiple arbitrarily shaped working surfaces to obtain multiple triangular areas corresponding to each arbitrarily shaped working surface; according to the probability integral method, two Romberg integral operations are performed on the multiple triangular areas to obtain an integral calculation result corresponding to each of the multiple triangular areas, and the integral calculation results corresponding to each triangular area are accumulated to obtain the three-dimensional surface movement and deformation data corresponding to each arbitrarily shaped working surface, wherein the three-dimensional surface movement and deformation data include surface subsidence information and inclination information, curvature information, horizontal movement information and horizontal deformation information in the target prediction direction and perpendicular to the target prediction direction.

[0096] It should be noted that the embodiments of the present application can use the direct double integral method to realize the prediction calculation of surface movement deformation for working surfaces of arbitrary shapes.

[0097] Specifically, Fig.10 As shown, the embodiment of the present application can firstly extract the basic parameter data of the prediction calculation according to the working surface data file and the predicted point data file, and based on the concave and convex vertex judgment strategy, triangulate each arbitrarily shaped working surface in combination with the basic parameter data to obtain the corresponding multiple triangular areas; secondly, the embodiment of the present application can perform two Romberg integration operations on multiple triangular areas according to the probability integral method to obtain the integral calculation result corresponding to each triangular area, and accumulate the integral calculation results corresponding to each triangular area to obtain the three-dimensional surface movement deformation data corresponding to each arbitrarily shaped working surface.

[0098] Therefore, the embodiment of the present application performs mining subsidence prediction based on the probability integral method according to the program. Fig.11 As shown, based on different predicted contents, the embodiment of the present application can output nine surface movement and deformation data, namely, surface subsidence, inclination along the predicted direction, curvature along the predicted direction, horizontal movement along the predicted direction, horizontal deformation along the predicted direction, inclination perpendicular to the predicted direction, curvature perpendicular to the predicted direction, horizontal movement perpendicular to the predicted direction, and horizontal deformation perpendicular to the predicted direction.

[0099] Optionally, in one embodiment of the present application, based on a preset concave-convex vertex determination strategy, and in combination with a work surface data file and a predicted point data file, a triangulation operation is performed on each of a plurality of arbitrarily shaped work surfaces to obtain a plurality of triangular regions corresponding to each arbitrarily shaped work surface, including: reading each corner point information in each arbitrarily shaped work surface in turn in a counterclockwise direction, and establishing a bidirectional circular linked list according to each corner point information; determining the concave-convexity of each node in the bidirectional circular linked list to obtain a plurality of convex nodes in the bidirectional circular linked list according to the concave-convexity; combining each convex node in the plurality of convex nodes with the front and rear nodes to obtain a triangle corresponding to each convex node, and judging whether there are other vertices in the triangle corresponding to each convex node, wherein if there are other vertices in the plurality of convex nodes, If there are no other vertices in the triangle corresponding to at least one target convex node, the triangle weight of the target triangle corresponding to each target convex node in at least one target convex node is calculated; the triangle weight corresponding to each target convex node is compared to obtain the maximum triangle weight, and the target triangle corresponding to the maximum triangle weight is saved in a bidirectional circular linked list, and the middle convex nodes that constitute the target triangle corresponding to the maximum triangle weight are deleted, and at the same time, it is determined whether the number of nodes in the bidirectional circular linked list is greater than 3; if the number of nodes in the bidirectional circular linked list is greater than 3, the concavity and convexity of each node in the bidirectional circular linked list is re-determined, otherwise the last three nodes in the bidirectional circular linked list are formed into a triangle, and the last three nodes in the bidirectional circular linked list are deleted to obtain multiple triangular areas corresponding to each arbitrary shape working surface.

[0100] In the specific implementation process, the embodiment of the present application can use simple polygon triangulation based on concave and convex vertex determination to divide the working surface of any shape, such as Fig.12 As shown, the steps for dividing a working surface of any shape into triangles in the embodiment of the present application are as follows:

[0101] Step 1: Read the corner points of the working surface in counterclockwise order and establish a bidirectional circular linked list;

[0102] Step 2: Calculate the concavity and convexity of each node in the bidirectional circular linked list;

[0103] Step 3: For each convex node Q in the bidirectional circular linked list, let the triangle formed by its previous and next nodes P and Q be △PQR. If △PQR does not contain other vertices on the polygon, then find the weight of the triangle. Find the triangle with the largest weight from these triangles, let it be △ABC, save the vertex sequence number of △ABC in the TML table, and delete node B from the linked list;

[0104] Step 4: If there are more than 3 nodes in the linked list, go to step 2, otherwise go to step 5;

[0105] Step 5: Use the linked list to summarize the polygon vertices corresponding to the last three points to form a triangle, and delete the last three nodes in the linked list.

[0106] It should be noted that the embodiments of the present application can be based on Fig.13 The analysis strategy shown determines whether △PQR in step 3 contains other vertices.

[0107] It should be noted that the innovation of the probability integral method in the embodiment of the present application is also reflected in the implementation of the probability integral method. The embodiment of the present application can adopt a direct integration method to implement the integral calculation of the probability integral method by using two Romberg integrals. The specific implementation steps are as follows:

[0108] 1. The integral value is calculated by dividing the interval into halves successively, so that the function value obtained in the previous division can still be used after the division into halves, and it is easy to implement by programming;

[0109] 2. For any certain integral For the interval [a, b], let h = b-a to construct a trapezoidal sequence {T 2K}.

[0110] T1=h[f(a)+f(b)] / 2

[0111] 3. Divide the interval into two equal parts. The length of each interval is h / 2 = (b-a) / 2, so:

[0112] T2=T1 / 2+[h / 2]f(a+h / 2)

[0113] 4. Divide the interval 22 into equal parts. The length of each small interval is h / 22 = (b-a) / 4. So we have:

[0114] T4=T2 / 2+[h / 22][f(a+h / 4)+f(a+3h / 4]

[0115] 5. Divide [a,b]2K into equal parts, dividing point X i =a+(ba) / 2K﹡i(i=1,2,3,…2k)The length of each small interval is (b-a) / 2k. By induction, we can get:

[0116]

[0117] 5. Calculate this time by time. and The calculation is exited when the difference between the values ​​of is small and the calculation accuracy is met.

[0118] Therefore, when the embodiment of the present application divides the interval successively, only the newly added function value needs to be calculated, avoiding the repeated calculation of the function value at the old dividing point, which can save nearly half of the calculation amount. In addition, the implementation process of the double integral in the embodiment of the present application is: first perform the Romberg integration on X, and then use the Romberg method to integrate Y again.

[0119] In step S103, the three-dimensional surface movement deformation data is gridded to obtain gridded data corresponding to the three-dimensional surface movement deformation data, and the gridded data is statistically analyzed and visualized to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data.

[0120] Furthermore, the embodiment of the present application also needs to adopt SURFER's Active X Automation technology to carry out secondary development to complete the subsidence analysis subroutine, so as to automatically visualize the data obtained by the prediction calculation, make drawings and analyze data, so as to automatically generate the working surface base map, moving deformation contour map, profile map, three-dimensional surface map and related overlay map; in addition, the embodiment of the present application can also realize the functions of area and volume statistics, terrain factor analysis, profile line analysis, geostatistical analysis and so on.

[0121] The embodiment of the present application takes the probability integral method based on random medium theory as the basic principle to realize the data management of mining areas, especially coal mining working faces, the management of mining area subsidence prediction parameters, and the automatic generation and production of mining subsidence prediction calculation, analysis and related drawings of multiple working faces of arbitrary shapes.

[0122] Therefore, the embodiment of the present application is completed by adopting the idea of ​​database in combination with AutoCAD and SURFER for secondary development to overcome the boundary subdivision error caused by the rectangular subdivision of the polygonal working surface, reduce the mining subsidence prediction error caused by inaccurate working surface subdivision, and realize the accurate subdivision of the polygonal working surface by adopting the idea of ​​triangular subdivision; in addition, the embodiment of the present application combines AutoCAD and SURFER software to realize the visual expression of mining subsidence analysis.

[0123] Optionally, in one embodiment of the present application, a gridding operation is performed on the three-dimensional surface movement and deformation data to obtain gridded data corresponding to the three-dimensional surface movement and deformation data, and statistical analysis and data visualization processing are performed on the gridded data to generate and display statistical image data corresponding to the three-dimensional surface movement and deformation data, including: gridding the three-dimensional surface movement and deformation data through the preset SURFER software to generate gridded data; statistical analysis is performed on the gridded data to obtain area and volume statistical data, terrain factor analysis data, profile analysis results and geostatistical analysis results in the statistical image data; data visualization processing is performed on the gridded data to obtain contour maps, profile maps, three-dimensional surface maps and movement deformation vector maps in the statistical image data; working surface base maps of multiple arbitrarily shaped working surfaces are obtained, and layer overlay operations are performed on the contour maps, profile maps, three-dimensional surface maps, movement deformation vector maps and working surface base maps to generate overlay maps corresponding to multiple arbitrarily shaped working surfaces in the statistical image data.

[0124] Specifically, the embodiment of the present application can use Active X Automation technology and the programming interface provided by ActiveXAutomation in SURFER to customize SURFER, thereby conveniently and quickly drawing contour maps, pasted maps, image maps, vector maps and other drawings, and can also perform trend surface analysis, volume and area calculation, slope analysis, profile analysis and other three-dimensional space analysis on the drawings; SURFER provides more than 60 different types of Active objects for external applications to call, and basically all the functions of SURFER can be completed through high-level language customization.

[0125] As a method that can be implemented, the main program segments of the embodiment of the present application using VB language to connect the SURFER ACTIVE object are as follows:

[0126] 'Declare SuferApp as an object variable

[0127] Dim SurferApp As Object

[0128] 'Create an instance of SURFER Application

[0129] Set SurferApp=CreateObject("Surfer.Application")

[0130] 'Declare a Plot object

[0131] Dim Plot As Object

[0132] 'Add a Plot graphics object to the SurferApp object

[0133] Set Plot = SurferApp.Documents.Add (1)

[0134] Therefore, the embodiment of the present application establishes a connection between a VB program and a SURFER object, and then SURFER can be manipulated through VB programming to complete various complex calculation functions.

[0135] It should be noted that if Fig.14 As shown, the specific steps of the embodiment of the present application for completing the production of various maps and data analysis based on the surface movement deformation data and the SURFE built-in function to generate and display the statistical image data corresponding to the three-dimensional surface movement deformation data are as follows:

[0136] Step 1: perform surface subsidence prediction and calculation operations after the working face is mined to obtain three-dimensional surface movement and deformation data, which includes point coordinates and the magnitude of various deformation values;

[0137] Step 2: Connect SURFER and send the obtained data to SURFER kernel data processing for data gridding;

[0138] Step 3: Use SURFER's graphic production and expression function to produce various drawing materials, and perform overlay analysis with the working surface base map to generate an overlay map;

[0139] Step 4: Use the data analysis function of SURFER to perform various statistics and data analysis and processing. The main menu of the mining subsidence prediction part in the embodiment of this application is as follows: Fig.15 shown.

[0140] In summary, if Fig.16 As shown, the embodiment of the present application performs prediction and analysis of mining surface subsidence of multiple working faces of arbitrary shapes through three parts: basic data management and generation part, mining subsidence prediction calculation part and prediction result statistical analysis visualization expression part. Specifically, when predicting mining subsidence, the working face can be firstly divided by a simple polygon triangulation method based on concave and convex fixed point judgment, and then the probability integral method can be used for direct integration to obtain the surface movement deformation value caused by mining of each triangular unit, and then the prediction and forecast of mining subsidence of the working face can be realized by superposition; the secondary development is combined with AutoCAD and SURFER software to realize the visualization analysis of the prediction results; the secondary development is carried out by using SURFER's ActiveX Automation technology, and the SURFER kernel program is manipulated by VB language to realize the production of various movement deformation contour lines, three-dimensional surface maps and profiles of mining subsidence and the automation of data analysis.

[0141] Therefore, the embodiments of the present application adopt the concept of structured programming, with a clear and simple operation process. It can predict and calculate the surface mining subsidence caused by mining of any shape and any number of working faces in a mining area, and conduct relevant analysis and produce relevant drawings. In addition, the application of the embodiments of the present application can reduce the workload of preparing mining subsidence prediction data, improve the accuracy of mining subsidence prediction and analysis, and provide a scientific basis for mining subsidence prediction and safe coal mining in mining areas.

[0142] According to the method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes proposed in the embodiment of the present application, basic data on mining subsidence of multiple working faces of arbitrary shapes in the target mining area, mining subsidence prediction parameters and basic data of predicted points are collected, and working face data files and predicted point data files are constructed through the basic data on mining subsidence of the working faces, mining subsidence prediction parameters and basic data of predicted points; based on the working face data files, predicted point data files and a preset probability integral method, surface subsidence prediction calculation operations are performed on multiple working faces of arbitrary shapes to obtain three-dimensional surface movement deformation data corresponding to the multiple working faces of arbitrary shapes; gridding operations are performed on the three-dimensional surface movement deformation data to obtain gridded data corresponding to the three-dimensional surface movement deformation data, and statistical analysis and data visualization processing are performed on the gridded data to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data. This application adopts the database concept to manage the working face and mining subsidence prediction parameters, thereby reducing the workload of working face data input and management, as well as the mining boundary error caused by the rectangular subdivision of the working face, improving the prediction accuracy of mining subsidence, realizing the automation and visual expression of the production of mining subsidence prediction analysis drawings, and improving the work efficiency of mining subsidence prediction.

[0143] Secondly, the surface subsidence prediction and analysis device for mining with multiple working faces of arbitrary shapes proposed in accordance with the embodiment of the present application is described with reference to the accompanying drawings.

[0144] Fig.17 It is a block diagram of a surface subsidence prediction and analysis device for mining with multiple working faces of arbitrary shapes according to an embodiment of the present application.

[0145] like Fig.17 As shown, the arbitrary-shaped multi-working-face mining surface subsidence prediction and analysis device 10 includes: a collection module 100 , a prediction module 200 and an analysis module 300 .

[0146] Among them, the acquisition module 100 is used to collect basic data on mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in the target mining area, and to construct working face data files and predicted point data files through the basic data on mining subsidence, mining subsidence prediction parameters and basic data of predicted points.

[0147] The prediction module 200 is used to perform surface subsidence prediction calculation operations on multiple arbitrarily shaped working surfaces based on the working surface data file, the estimated point data file and the preset probability integration method to obtain three-dimensional surface movement deformation data corresponding to the multiple arbitrarily shaped working surfaces.

[0148] The analysis module 300 is used to perform gridding operations on the three-dimensional surface movement deformation data to obtain gridded data corresponding to the three-dimensional surface movement deformation data, and perform statistical analysis and data visualization processing on the gridded data to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data.

[0149] Optionally, in one embodiment of the present application, the acquisition module 100 includes: an acquisition unit, an establishment unit, a determination unit, a first generation unit, a second generation unit and a third generation unit.

[0150] Among them, the acquisition unit is used to collect basic data and mining subsidence prediction parameters of working faces of multiple arbitrary shapes in the target mining area, wherein the basic data of working face mining subsidence includes mining depth information, mining thickness information, working face position information, working face corner point coordinate information, coal seam inclination information and strike azimuth information corresponding to multiple arbitrary shapes of working faces, and the mining subsidence prediction parameters include sinking coefficient, horizontal movement coefficient, main influence angle tangent, inflection point offset distance and mining influence propagation angle.

[0151] A unit is established to create a working face data file based on basic data of mining subsidence and mining subsidence prediction parameters of the working face.

[0152] The determination unit is used to select target corner points in the target mine map of the target mining area, and generate a target rectangular prediction area through the target corner points to determine the surface area prediction range in the basic data of the prediction point according to the target rectangular prediction area.

[0153] The first generating unit is used to determine the starting point coordinates, end point coordinates and segment point spacing of the target line segment in the target mine map, and generate the surface linear predicted point information in the predicted point basic data through the starting point coordinates, end point coordinates and segment point spacing.

[0154] The second generating unit is used to determine the target prediction direction in the target mine map, to generate any discrete point in the target mine map according to the target prediction direction, and to obtain any surface prediction point information in the prediction point basic data through the any discrete point.

[0155] The third generating unit is used to generate a predicted point data file based on any predicted point information on the surface, the predicted linear point information on the surface and the predicted range of the surface area.

[0156] Optionally, in one embodiment of the present application, the prediction module 200 includes: a triangulation unit and an integral calculation unit.

[0157] Among them, the triangulation unit is used to perform triangulation operations on each of multiple arbitrarily shaped working surfaces based on a preset concave and convex vertex judgment strategy and in combination with the working surface data file and the expected point data file to obtain multiple triangular areas corresponding to each arbitrarily shaped working surface.

[0158] An integral calculation unit is used to perform two Romberg integral operations on multiple triangular areas according to the probability integral method to obtain an integral calculation result corresponding to each of the multiple triangular areas, and accumulate the integral calculation results corresponding to each triangular area to obtain three-dimensional surface movement deformation data corresponding to each arbitrary shape working surface, wherein the three-dimensional surface movement deformation data includes surface subsidence information and inclination information, curvature information, horizontal movement information and horizontal deformation information in a target prediction direction and perpendicular to the target prediction direction.

[0159] Optionally, in one embodiment of the present application, the prediction module 200 further includes: a reading unit, a concave-convexity detection unit, a combining unit, a comparing unit and a deleting unit.

[0160] The reading unit is used to read the information of each corner point in each work surface of arbitrary shape in turn based on the counterclockwise direction, and establish a bidirectional circular linked list according to the information of each corner point.

[0161] The concavity and convexity detection unit is used to determine the concavity and convexity of each node in the bidirectional circular linked list, so as to obtain a plurality of convex nodes in the bidirectional circular linked list according to the concavity and convexity.

[0162] A combining unit is used to combine each convex node among the multiple convex nodes with the previous and next nodes to obtain a triangle corresponding to each convex node, and to determine whether there are other vertices in the triangle corresponding to each convex node, wherein if there is a triangle corresponding to at least one target convex node among the multiple convex nodes and no other vertices exist, then the triangle weight of the target triangle corresponding to each target convex node among the at least one target convex node is calculated.

[0163] A comparison unit is used to compare the triangle weights corresponding to each target convex node to obtain the maximum triangle weight, and save the target triangle corresponding to the maximum triangle weight in a bidirectional circular linked list, and delete the middle convex nodes that constitute the target triangle corresponding to the maximum triangle weight, and at the same time determine whether the number of nodes in the bidirectional circular linked list is greater than 3.

[0164] The deletion unit is used to redetermine the concavity and convexity of each node in the bidirectional circular linked list if the number of nodes in the bidirectional circular linked list is greater than 3, otherwise the last three nodes in the bidirectional circular linked list are formed into a triangle and the last three nodes in the bidirectional circular linked list are deleted to obtain multiple triangular areas corresponding to each arbitrary shape working surface.

[0165] Optionally, in one embodiment of the present application, the analysis module 300 includes: a gridding unit, a statistical analysis unit, a visualization unit and a fourth generation unit.

[0166] Among them, the gridding unit is used to perform gridding operations on the three-dimensional surface movement deformation data through the preset SURFER software to generate gridding data.

[0167] The statistical analysis unit is used to perform statistical analysis on the gridded data to obtain area and volume statistical data, terrain factor analysis data, profile analysis results and geostatistical analysis results in the statistical image data.

[0168] The visualization unit is used to perform data visualization processing on the grid data to obtain contour maps, profile maps, three-dimensional surface maps and moving deformation vector maps in the statistical image data.

[0169] The fourth generating unit is used to obtain the working surface base surface images of multiple working surfaces of arbitrary shapes, and to perform layer overlay operations on the contour map, profile map, three-dimensional surface map, moving deformation vector map and working surface base surface map to generate overlay maps corresponding to multiple working surfaces of arbitrary shapes in the statistical image data.

[0170] It should be noted that the aforementioned explanation of the embodiment of the method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes is also applicable to the device for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes of this embodiment, and will not be repeated here.

[0171] The device for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes proposed in the embodiment of the present application includes a collection module for collecting basic data on mining subsidence of working faces of arbitrary shapes in a target mining area, mining subsidence prediction parameters and basic data of predicted points, and constructing working face data files and predicted point data files through the basic data on mining subsidence of working faces, mining subsidence prediction parameters and basic data of predicted points; a prediction module for performing surface subsidence prediction calculation operations on multiple working faces of arbitrary shapes based on the working face data files, predicted point data files and a preset probability integral method to obtain three-dimensional surface movement deformation data corresponding to the multiple working faces of arbitrary shapes; an analysis module for performing gridding operations on the three-dimensional surface movement deformation data to obtain gridded data corresponding to the three-dimensional surface movement deformation data, and performing statistical analysis and data visualization processing on the gridded data to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data. This application adopts the database concept to manage the working face and mining subsidence prediction parameters, thereby reducing the workload of working face data input and management, as well as the mining boundary error caused by the rectangular subdivision of the working face, improving the prediction accuracy of mining subsidence, realizing the automation and visual expression of the production of mining subsidence prediction analysis drawings, and improving the work efficiency of mining subsidence prediction.

[0172] Fig.18 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0173] A memory 1801 , a processor 1802 , and a computer program stored in the memory 1801 and executable on the processor 1802 .

[0174] When the processor 1802 executes the program, the surface subsidence prediction and analysis method for mining with multiple working faces of arbitrary shapes provided in the above-mentioned embodiment is implemented.

[0175] Furthermore, the electronic device further comprises:

[0176] The communication interface 1803 is used for communication between the memory 1801 and the processor 1802 .

[0177] The memory 1801 is used to store computer programs that can be executed on the processor 1802 .

[0178] The memory 1801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0179] If the memory 1801, the processor 1802 and the communication interface 1803 are implemented independently, the communication interface 1803, the memory 1801 and the processor 1802 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.18 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0180] Optionally, in a specific implementation, if the memory 1801, the processor 1802 and the communication interface 1803 are integrated on a chip, the memory 1801, the processor 1802 and the communication interface 1803 can communicate with each other through an internal interface.

[0181] The processor 1802 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0182] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes.

[0183] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes.

[0184] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0185] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0186] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0187] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0188] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0189] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0190] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0191] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes, characterized in that: The following steps are involved: Collecting basic data of mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in the target mining area, and constructing working face data files and predicted point data files through the basic data of mining subsidence, the mining subsidence prediction parameters and the basic data of predicted points; Based on the working surface data file, the predicted point data file and a preset probability integral method, a surface subsidence prediction calculation operation is performed on the plurality of working surfaces of arbitrary shapes to obtain three-dimensional surface movement deformation data corresponding to the plurality of working surfaces of arbitrary shapes; A gridding operation is performed on the three-dimensional surface movement deformation data to obtain gridded data corresponding to the three-dimensional surface movement deformation data, and statistical analysis and data visualization processing are performed on the gridded data to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data.

2. The method according to claim 1, characterized in that The method collects basic data of mining subsidence, mining subsidence prediction parameters and basic data of estimated points of multiple working faces of arbitrary shapes in the target mining area, and constructs working face data files and estimated point data files through the basic data of mining subsidence, the mining subsidence prediction parameters and the basic data of estimated points, including: Collecting the basic data of mining subsidence of the working faces and the mining subsidence prediction parameters of a plurality of working faces of arbitrary shapes in the target mining area, wherein the basic data of mining subsidence of the working faces include mining depth information, mining thickness information, working face position information, working face corner point coordinate information, coal seam inclination information and strike azimuth information corresponding to the plurality of working faces of arbitrary shapes, and the mining subsidence prediction parameters include sinking coefficient, horizontal movement coefficient, main influence angle tangent, inflection point offset distance and mining influence propagation angle; Based on the basic data of mining subsidence of the working face and the mining subsidence prediction parameters, establishing the working face data file; Selecting a target corner point in a target mine map of the target mine area, and generating a target rectangular prediction area through the target corner point, so as to determine a surface area prediction range in the basic data of the prediction point according to the target rectangular prediction area; Determine the starting point coordinates, the end point coordinates and the line segment point spacing of the target line segment in the target mine map, and generate the surface linear predicted point information in the predicted point basic data through the starting point coordinates, the end point coordinates and the line segment point spacing; Determine the target prediction direction in the target mine map, generate any discrete point in the target mine map according to the target prediction direction, and obtain the surface arbitrary prediction point information in the prediction point basic data through the arbitrary discrete point; The predicted point data file is generated based on the predicted arbitrary point information on the ground surface, the predicted linear point information on the ground surface and the predicted range of the ground surface area.

3. The method according to claim 2, characterized in that The method of performing surface subsidence prediction calculation operations on the plurality of arbitrarily shaped working surfaces based on the working surface data file, the predicted point data file and a preset probability integral method to obtain three-dimensional surface movement deformation data corresponding to the plurality of arbitrarily shaped working surfaces includes: Based on a preset concave-convex vertex determination strategy, and in combination with the working surface data file and the predicted point data file, a triangulation operation is performed on each of the plurality of arbitrarily shaped working surfaces to obtain a plurality of triangular regions corresponding to each of the arbitrarily shaped working surfaces; According to the probability integral method, two Romberg integration operations are performed on the multiple triangular areas to obtain an integral calculation result corresponding to each of the multiple triangular areas, and the integral calculation results corresponding to each triangular area are accumulated to obtain three-dimensional surface movement and deformation data corresponding to each arbitrary shape working surface, wherein the three-dimensional surface movement and deformation data include surface subsidence information and the target prediction direction and the inclination information perpendicular to the target prediction direction, curvature information, horizontal movement information and horizontal deformation information.

4. The method according to claim 3, characterized in that The method of performing a triangulation operation on each of the plurality of arbitrarily shaped working surfaces based on a preset concave-convex vertex determination strategy and in combination with the working surface data file and the predicted point data file to obtain a plurality of triangular regions corresponding to each of the arbitrarily shaped working surfaces comprises: Based on the counterclockwise direction, read the information of each corner point in each arbitrary shape working surface in turn, and establish a bidirectional circular linked list according to the information of each corner point; Determine the concavity and convexity of each node in the bidirectional circular linked list to obtain a plurality of convex nodes in the bidirectional circular linked list according to the concavity and convexity; Combine each convex node among the multiple convex nodes with the previous and next nodes to obtain a triangle corresponding to each convex node, and determine whether there are other vertices in the triangle corresponding to each convex node, wherein if there is at least one target convex node among the multiple convex nodes and the triangle corresponding to the target convex node does not have the other vertices, calculate the triangle weight of the target triangle corresponding to each target convex node among the at least one target convex node; Compare the triangle weights corresponding to each target convex node to obtain the maximum triangle weight, and save the target triangle corresponding to the maximum triangle weight in the bidirectional circular linked list, and delete the middle convex nodes constituting the target triangle corresponding to the maximum triangle weight, and determine whether the number of nodes in the bidirectional circular linked list is greater than 3; If the number of nodes in the bidirectional circular linked list is greater than 3, the concavity and convexity of each node in the bidirectional circular linked list are re-determined; otherwise, the last three nodes in the bidirectional circular linked list are formed into a triangle, and the last three nodes in the bidirectional circular linked list are deleted to obtain multiple triangular areas corresponding to each arbitrary shape working surface.

5. The method according to claim 1, characterized in that The gridding operation is performed on the three-dimensional surface movement deformation data to obtain gridded data corresponding to the three-dimensional surface movement deformation data, and statistical analysis and data visualization processing are performed on the gridded data to generate and display statistical image data corresponding to the three-dimensional surface movement deformation data, including: Performing a gridding operation on the three-dimensional surface movement deformation data by using a preset SURFER software to generate the gridded data; Performing statistical analysis on the gridded data to obtain area and volume statistical data, terrain factor analysis data, profile analysis results and geostatistical analysis results in the statistical image data; Performing data visualization processing on the gridded data to obtain contour maps, profile maps, three-dimensional surface maps and moving deformation vector maps in the statistical image data; Obtain the working surface base surface images of the multiple arbitrarily shaped working surfaces, and perform layer overlay operations on the contour map, the cross-sectional map, the three-dimensional surface map, the moving deformation vector map and the working surface base surface image to generate an overlay map corresponding to the multiple arbitrarily shaped working surfaces in the statistical image data.

6. A surface subsidence prediction and analysis device for mining with multiple working faces of arbitrary shapes, characterized in that: include: A collection module, used for collecting basic data of mining subsidence, mining subsidence prediction parameters and basic data of predicted points of multiple working faces of arbitrary shapes in the target mining area, and constructing working face data files and predicted point data files through the basic data of mining subsidence, the mining subsidence prediction parameters and the basic data of predicted points; A prediction module, configured to perform surface subsidence prediction calculation operations on the plurality of arbitrarily shaped working surfaces based on the working surface data file, the predicted point data file and a preset probability integral method, so as to obtain three-dimensional surface movement deformation data corresponding to the plurality of arbitrarily shaped working surfaces; The analysis module is used to perform gridding operations on the three-dimensional surface movement and deformation data to obtain gridded data corresponding to the three-dimensional surface movement and deformation data, and to perform statistical analysis and data visualization processing on the gridded data to generate and display statistical image data corresponding to the three-dimensional surface movement and deformation data.

7. The device according to claim 6, characterized in that The acquisition module comprises: An acquisition unit is used to collect the basic data of mining subsidence of the working faces and the mining subsidence prediction parameters of a plurality of working faces of arbitrary shapes in the target mining area, wherein the basic data of mining subsidence of the working faces include mining depth information, mining thickness information, working face position information, working face corner point coordinate information, coal seam inclination information and strike azimuth information corresponding to the plurality of working faces of arbitrary shapes, and the mining subsidence prediction parameters include sinking coefficient, horizontal movement coefficient, main influence angle tangent, inflection point offset distance and mining influence propagation angle; An establishing unit, used for establishing the working face data file based on the basic data of mining subsidence of the working face and the mining subsidence prediction parameters; A determination unit, configured to select a target corner point in a target mine map of the target mine area, and generate a target rectangular prediction area through the target corner point, so as to determine a surface area prediction range in the basic data of the prediction point according to the target rectangular prediction area; A first generating unit is used to determine the starting point coordinates, the end point coordinates and the line segment point spacing of the target line segment in the target mine map, and generate the surface linear predicted point information in the predicted point basic data through the starting point coordinates, the end point coordinates and the line segment point spacing; A second generating unit is used to determine a target prediction direction in the target mineral map, to generate any discrete point in the target mineral map according to the target prediction direction, and to obtain any surface prediction point information in the prediction point basic data through the arbitrary discrete point; The third generating unit is used to generate the predicted point data file based on the arbitrary predicted point information on the surface, the linear predicted point information on the surface and the predicted range of the surface area.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for predicting and analyzing surface subsidence in multi-working face mining of arbitrary shapes as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for predicting and analyzing surface subsidence in mining with multiple working faces of arbitrary shapes as described in any one of claims 1 to 5.

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