Simulation method and system for coal seam mining subsidence

Abstract

The invention discloses a coal seam mining subsidence simulation method and system, and relates to the field of coal mining subsidence, and the method comprises the steps: obtaining a surface subsidence curve formed by surface subsidence when a coal seam is mined to cause subsidence of the surface above a mining working face; segmenting the surface subsidence curve to obtain a basin curve in the middle of the surface subsidence curve and a subsidence curve above the middle of the surface subsidence curve; and respectively fitting the settlement curve and the basin curve by using a function model so as to construct a mining settlement prediction model which reflects the vertical settlement condition caused by surface subsidence in the trend of the mining working face. The mining subsidence condition can be accurately predicted.

Description

Technical Field

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

[0002] Subsidence caused by coal mining has led to the destruction of a large amount of land resources, seriously affecting the sustainable development of the region. The reclamation of damaged land has also become an urgent problem that needs to be solved. If relevant information about damaged land can be obtained in advance before coal mining, it will be of great significance for controlling ecological environmental degradation and formulating reclamation measures.

[0003] In the prior art, the prediction of mining subsidence uses the probability integral method. The subsidence caused by mining subsidence will produce a basin. The probability integral method is relatively accurate in predicting the area near the maximum subsidence value, but the predicted subsidence range at the two sides of the basin is inaccurate, resulting in inaccurate mining subsidence predicted by this method. Summary of the invention

[0004] The embodiment of the present invention provides a method and system for simulating coal seam mining subsidence, which can solve the problem of inaccurate prediction of mining subsidence in the prior art.

[0005] An embodiment of the present invention provides a method for simulating coal mining subsidence, comprising the following steps: obtaining a surface subsidence curve formed by surface subsidence, wherein the surface subsidence indicates subsidence occurring on the surface above the coal mining working face; segmenting the surface subsidence curve to obtain a basin curve in the middle of the surface subsidence curve, and a settlement curve above the middle of the surface subsidence curve; fitting the settlement curve using a logarithmic function model to simulate the subsidence of the surface above the middle of the surface subsidence curve along the direction of the mining working face; and fitting the basin curve using a parabolic function model to simulate the subsidence of the surface in the middle of the surface subsidence curve along the direction of the mining working face; and constructing a mining subsidence prediction model by fitting the logarithmic function model and the parabolic function model of the surface subsidence curve.

[0006] Furthermore, the step of fitting the sedimentation curve comprises:

[0007] Construct a coordinate system, the origin of which is the surface position corresponding to the midpoint in the direction of the coal seam mining working face; the x-axis of the coordinate system is parallel to the direction of the working face, and the y-axis is the vertical direction perpendicular to the surface;

[0008]

[0009] Among them, x is the coordinate value in the direction of the coal seam mining working face, y is the settlement value in the vertical direction perpendicular to the ground surface, r is the horizontal distance from the coordinate origin to the inflection point of the settlement curve, R is the horizontal distance between the coordinate origin and the outermost boundary of the settlement curve, and a and h are both constants related to the mining thickness of the working face.

[0010] Furthermore, the basin curve is fitted, and the specific steps include:

[0011] Design functions to simulate the basin curve in the middle of the surface subsidence curve according to simulation conditions, including:

[0012] The sedimentation curve is simulated by y1(x) and y3(x), and the basin curve is simulated by y2(x), as the first simulation condition;

[0013] set up is the second simulation condition;

[0014] Set y1′(r)=y2′(r), y2′(r′)=y3′(r′) as the third simulation condition;

[0015] Analyze the inflection point where the settlement curve connects with the basin curve to obtain:

[0016] y1(r)=a+h; y3(r′)=a′+h′;

[0017] Among them, r' is the horizontal distance between the coordinate origin on the other side of the coordinate origin and the inflection point of the settlement curve, R' is the horizontal distance between the coordinate origin on the other side of the coordinate origin and the outermost boundary of the settlement curve, and a' and h' are both constants related to the mining thickness of the working face on the other side of the coordinate origin;

[0018] Construct y2(x) to simulate the basin curve:

[0019] y2(x)=b1x 2 +b2x+b3

[0020] Among them, b1, b2 and b3 are all constants;

[0021] make but:

[0022]

[0023] Integrating y2'(x) yields:

[0024]

[0025] Where C represents a constant related to the maximum settlement;

[0026] The basin curve y2(x) in the middle of the simulated surface subsidence curve is obtained.

[0027] Furthermore, the construction of the mining subsidence prediction model specifically includes the following steps:

[0028] according to get:

[0029]

[0030] but:

[0031]

[0032] Substitute h and h' into the simulated settlement curve respectively, and together with the simulated basin curve, construct the mining subsidence prediction model:

[0033]

[0034] in,

[0035] When the coal seam to be mined is a horizontal coal seam, a=a′, r=-r′, R=R′, The case of piecewise functions is:

[0036]

[0037] An embodiment of the present invention provides a simulation system for coal mining subsidence, including: obtaining a surface subsidence curve formed by surface subsidence; a mining subsidence simulation module, which is used to segment the surface subsidence curve to obtain a basin curve in the middle of the surface subsidence curve, and a settlement curve above the middle of the surface subsidence curve; using a logarithmic function model to fit the settlement curve to simulate the subsidence of the surface above the middle of the surface subsidence curve along the direction of the mining working face; and using a parabolic function model to fit the basin curve to simulate the subsidence of the surface in the middle of the surface subsidence curve along the direction of the mining working face; by fitting the logarithmic function model and the parabolic function model of the surface subsidence curve, a mining subsidence prediction model is constructed.

[0038] The embodiment of the present invention provides a method and system for simulating coal seam mining subsidence. Compared with the prior art, the beneficial effects thereof are as follows:

[0039] The surface subsidence curve is divided into the basin curve in the middle of the surface subsidence curve and the settlement curve above the middle of the surface subsidence curve for segmented analysis. The settlement curve is fit using a logarithmic function model, and the basin curve is fit using a parabolic function model. Compared with using a single function simulation method, the subsidence conditions of different areas can be accurately distinguished in a segmented manner, thereby accurately simulating the mining subsidence of the coal seam. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A similar material model design diagram (10 degree model) provided for an embodiment of the present invention;

[0041] Figure 2 The overall moving vector diagram after the inclined coal seam mining is stabilized provided by the embodiment of the present invention;

[0042] Figure 3 A comparison diagram of the simulation data and the probability integral method model fitting provided by an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of a complete well in a confined aquifer provided by an embodiment of the present invention;

[0044] Figure 5 A similar material tilt model sinking curve fitting effect diagram provided in an embodiment of the present invention;

[0045] Figure 6 A morphological diagram of a parameter ζ variation curve provided by an embodiment of the present invention;

[0046] Figure 7 A morphological diagram of a tanβ variation curve in the uphill direction in the probability integral model provided in an embodiment of the present invention;

[0047] Figure 8 A morphological diagram of a parameter τ variation curve provided in an embodiment of the present invention;

[0048] Fig. 9 A morphological diagram of a tanβ variation curve in a downhill direction in a probability integral model provided by an embodiment of the present invention;

[0049] Fig.10 A morphological diagram of a parameter σ variation curve provided in an embodiment of the present invention;

[0050] Fig.11 A morphological diagram of a working surface oblique length variation curve in a probability integral model provided by an embodiment of the present invention;

[0051] Fig.12 A morphological diagram of a parameter ω variation curve provided in an embodiment of the present invention;

[0052] Fig.13 A morphological diagram of a curve of a change in inflection point offset in a probability integral model provided in an embodiment of the present invention;

[0053] Fig.14 The parameter K provided in the embodiment of the present invention w Change curve morphology diagram;

[0054] Fig.15 A morphological diagram of a sinking coefficient variation curve in a probability integral model provided by an embodiment of the present invention;

[0055] Fig.16 11118 observation station layout diagram provided for an embodiment of the present invention;

[0056] Fig.17 A comparison curve diagram of the probability integral method and the logarithmic function model provided in the embodiment of the present invention;

[0057] Fig.18 A flow chart of a method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0059] The embodiment of the present invention provides a method for simulating coal seam mining subsidence, comprising the following steps:

[0060] Step 1: Obtain a surface subsidence curve formed by surface subsidence, which refers to the subsidence of the surface above the coal mining working face.

[0061] Step 2: Divide the surface subsidence curve into segments to obtain the basin curve in the middle of the surface subsidence curve and the settlement curve above the middle of the surface subsidence curve; use the logarithmic function model to fit the settlement curve to simulate the settlement of the surface above the middle of the surface subsidence curve along the direction of the mining working face; and use the parabolic function model to fit the basin curve to simulate the settlement of the surface in the middle of the surface subsidence curve along the direction of the mining working face; by fitting the logarithmic function model and the parabolic function model of the surface subsidence curve, a mining subsidence prediction model is constructed.

[0062] The specific analysis process is as follows:

[0063] 1. Simulation experiment of similar materials.

[0064] (1) Similar material simulation.

[0065] Similar material simulation experiment belongs to physical simulation experiment. According to the principle of similarity, the rock prototype is reduced in a certain proportion and made into a model using similar materials. The coal seam in the model is mined according to the time ratio, and the displacement of the target point on the model is obtained by deformation monitoring means, and then converted into actual value. The transmission law of mining subsidence in the overburden is obtained through analysis.

[0066] The experiment used a 2.5-meter plane similar material model test bench and determined the scale to be 1:200.

[0067] The calculation of model materials must comply with the principles of geometric similarity, kinematic similarity and dynamic similarity. In this experiment, the geometric similarity coefficient is 1:200. The model ratio principle is:

[0068] 1) Design the thickness of each rock layer according to the drill column of a certain mining area.

[0069] 2) The height of the water-conducting fracture zone formed after model mining is basically consistent with the actual measurement.

[0070] 3) The maximum subsidence of the model surface is basically consistent with the actual measurement.

[0071] The model simulates mining in an inclined coal seam with an inclination angle of 10 degrees. The model result design diagram is shown in the figure below. Figure 1 shown.

[0072] In order to obtain high-precision and high-quality test data, this test intends to use a combination of a digital close-range photogrammetry system (XJTUDP) and a three-dimensional optical dense point cloud measurement system (XJTUOM) to obtain test data. These two systems are referred to as industrial measurement systems.

[0073] Before model observation, you first need to paste the necessary markers on the model. The markers can be divided into coded points and non-coded points. Both markers can be automatically identified in the XJTUDP software system and their three-dimensional coordinates can be solved in real time.

[0074] The industrial measurement system has high observation accuracy and can obtain high-precision model test data. Among them, the nominal single-point position accuracy of the XJTUDP photogrammetry system is 0.03mm to 0.1mm. The XJTUOM dense point cloud measurement system can measure 1 million to 6 million point clouds at a time, and the interval between each point is 0.08mm to 1mm. The measurement accuracy is 0.01 to 1mm, generally 0.03mm, depending on the single format size and camera pixels.

[0075] (2) Simulation results of similar materials.

[0076] The simulation test obtained a series of observation data, such as the overall movement vector diagram after the inclined coal seam mining stabilized, such as Figure 2 shown.

[0077] According to the simulation results of similar materials, the existing probability integral method prediction model was fitted and it was found that the fitting effect was relatively poor in the sections with subsidence values ​​less than 300 mm on both sides of the basin, such as Figure 3A large amount of measured data also proves that the probability integral method is more accurate in predicting the area near the maximum subsidence value, but converges faster at the basin boundary, and the predicted basin range is smaller than the actual basin range.

[0078] Therefore, in order to accurately grasp the amount of coal mining subsidence in this mining area, it is necessary to establish a mining subsidence prediction model suitable for the characteristics of this mining area.

[0079] 2. Construction of subsidence area prediction model—logarithmic function model.

[0080] (1) Model construction

[0081] Research has found that the principle of surface subsidence after coal mining is similar to the principle of groundwater infiltration. After coal seam mining, the upper rock gradually collapses and compacts downward, and then transfers upward. Due to the pumping of wells, groundwater gradually infiltrates to the lower water level, and the movement principle is somewhat similar. The surface subsidence caused by coal seam working face mining is a subsidence basin, like a subsidence funnel, which is very similar to the precipitation funnel formed by pumping water from pumping wells in the phreatic aquifer. Figure 4 As shown in the figure, the water level drop funnel formed by pumping water from the complete well of the confined aquifer on the cross section. The thickness of the homogeneous isotropic medium aquifer is M, the original water level of the aquifer is H0, and the water level in the well after the complete well pumping is stable is h w , the thickness of the saturated water layer is H, the permeability coefficient is K, and the wellbore radius is r w The water level range affected by water well pumping is expressed by the influence radius R (unit: m), which is calculated according to the Siechardt empirical formula:

[0082]

[0083] Where: S w is the water level drop in the well (m); K is the permeability coefficient (m / s).

[0084] According to the calculation and derivation of the seepage model, the wetted surface equation is finally obtained as follows:

[0085]

[0086] Inspired by the wetting surface equation derived from the above confined aquifer seepage model, the initial established subsidence prediction model on the main section of the working face mining subsidence is a logarithmic function model:

[0087]

[0088] In the formula, the origin of the coordinate system is the surface position corresponding to the midpoint in the strike direction of the mining face, the x-axis is parallel to the strike direction of the working face, and the y-axis is the vertical direction perpendicular to the surface. When x is negative, the corresponding function formula can be changed to:

[0089]

[0090] Preliminary tests of formulas (1-3) and (1-4) can yield the shape of the sinking curve of the upper boundary of the main section of the surface subsidence basin caused by mining subsidence. The middle part forms a particularly sharp and narrow funnel, which changes with the change of the value range of r and x in the formula (the value of x is not taken in a range close to 0, such as ). In order to adapt to the shape of the sinking curve of the main section of the working face mining strike, a function must be constructed to express the curve shape in the middle of the funnel. Assume that this function is y2(x). Since the constructed mathematical model is a prediction model applicable to all coal seam inclinations, it is assumed that the prediction function along the full-inclination main section on the uphill side is y1(x), and the prediction function along the full-inclination main section on the downhill side is y3(x). The piecewise function form is as follows:

[0091]

[0092] In the formula, a<0, a′<0, h>0, h′>0, r>0, r′<0, R>0, R′>0.

[0093] To construct the function y2(x) so that it conforms to the desired sinking curve shape, three conditions must be met at the same time:

[0094] (1) Basin curve shape of the y2(x) function plot.

[0095] (2)

[0096] (3)y1′(r)=y2′(r), y2′(r′)=y3′(r′).

[0097] According to: y1(r)=a+h

[0098]

[0099] y3(r′)=a′+h′

[0100]

[0101] Construct the y2(x) function as:

[0102] y2(x)=b1x 2 +b2x+b3 (1-6)

[0103] make Then the y2′(x) function can be constructed as:

[0104]

[0105] Integrating formula (1-7), we get:

[0106]

[0107] And by have to:

[0108]

[0109] but

[0110]

[0111]

[0112] Combining formulas (1-7) to (1-12), the preliminary piecewise function model is obtained:

[0113]

[0114] When the coal seam to be mined is a horizontal coal seam, then:

[0115] a=a′, r=-r′, R=R′,

[0116] That is, the special case of piecewise function is:

[0117]

[0118] The piecewise function is further improved into an independent single formula mathematical model. The logarithmic function model introduced and analyzed above can vividly describe the surface subsidence curve formed by mining, so the research and analysis are carried out from the logarithmic function. The mathematical model initially determined is:

[0119] W(x)=K w [f1(x)-f2(x)] (1-15)

[0120] Where W(x) is the expected ground subsidence value on the main section, K w is the maximum sinking value, f1(x) and f2(x) are functions with independent variable x in the range of [0,1]. Based on the curve shape of the logarithmic function and multiple Matlab numerical verification and analysis, it is preliminarily determined to be:

[0121]

[0122] The parameter that affects the maximum value in the model is K w , but a single change in K wThe overall fitting effect of the curve is worse, so a new parameter ω is introduced here. ω replaces the term with exponent "-1" in the f1(x) and f2(x) functions. Its role is to affect the maximum value of the function fitting. The modified f1(x) and f2(x) functions are:

[0123]

[0124] The final new model for surface subsidence prediction is:

[0125]

[0126] In the formula, K w Represents a new parameter related to the maximum sink value; represents the new parameter related to the mining depth in the uphill direction; σ represents the new parameter related to the length of the mining face; τ represents the new parameter related to the mining depth in the downhill direction; ω represents the new parameter related to K w and new parameters related to the coal seam inclination α; f1(x) and f2(x) are both functions containing independent variables x with a value range of [0,1].

[0127] The correlation analysis of the above five newly added parameters is only preliminary. The physical meaning of the new parameters assumed here and the inferred correlation are only preliminarily determined and considered based on the relationship to be expressed in the model establishment or the morphological factors affecting the function. Later, the fitting values ​​of the analysis parameters and the physical meaning they represent, as well as the mining factors of the working face (including mining thickness, mining depth, working face size, mining geological conditions, coal seam inclination, etc.) will be specifically studied and analyzed. The reliability of the mathematical model must be fitted and analyzed by the measured data of similar material models.

[0128] The form of fitting is described mathematically. Assuming that the mathematical model used is y=f(x,θ), where θ is a variable nonlinear function, x is the input vector, and y is the output variable, the total square error is:

[0129]

[0130] Where (x i ,y i) are known observed data points. The goal is to minimize the total square error E(θ). Since the parameter θ is nonlinear, E(θ) is not a linear form of θ. Therefore, when seeking the minimum value of the total square error E(θ), it is impossible to solve the optimal θ value by making the derivative of E(θ) with respect to θ equal to zero. In other words, it is impossible to use the derivation method to find the optimal parameter value in formula (2-20). Therefore, it is necessary to use a general optimization method to find the minimum value of E(θ). This time, the Downhill Simplex Search method in the Matlab package function fitting is used. Its function command is fminsearch. According to the calling format of the Matlab package function, the corresponding program code is written and substituted into the observed data. The minimum value of the objective function can be found, thereby obtaining the various parameters in the mathematical model function and calculating the fitting value of the observation point.

[0131] The Downhill Simplex Search method analyzed above is used to fit the nonlinear function and obtain the fitting effect of the surface subsidence curve in each similar material model. Figure 5 shown.

[0132] (2) Study on parameters of logarithmic function model

[0133] In order to study the role of each parameter in the function, the method of graphical analysis is used. When studying any parameter, the values ​​of other parameters are fixed.

[0134] 1) Parameters related to the main impact radius of going uphill

[0135] In the logarithmic function model, when the parameter ζ is larger, the maximum sinking value is smaller, the boundary convergence in the uphill direction is slower, and the curve shape in the downhill direction does not change significantly. Figure 6 In the probability integral method model, the smaller the tangent value of the main impact angle of the uphill direction, that is, the larger the main impact radius r2 of the uphill direction, the smaller the maximum sinking value, and the slower the uphill boundary converges. Figure 7 Therefore, it can be inferred that the parameter ζ is closely related to the main impact radius r2 of the uphill movement in the probability integral model.

[0136] 2) Parameters related to the main impact radius of downhill

[0137] In the logarithmic function model, when the parameter τ is larger, the maximum sinking value is smaller, the boundary convergence in the downhill direction is slower, and the curve shape in the uphill direction does not change significantly. Figure 8 .

[0138] In the probability integral method model, the smaller the tangent value of the downhill direction's main influence angle tanβ is, that is, the larger the downhill direction's main influence radius r1 is, the smaller the maximum sinking value is, and the slower the downhill boundary converges. Fig. 9 Therefore, it can be inferred that the parameter τ is closely related to the main influence radius r1 of downhill in the probability integral model.

[0139] 3) Parameters related to the working surface advancement distance and working surface size

[0140] In the logarithmic function model, the larger the parameter σ is, the more the function curve changes. This is consistent with the change in the function curve shape in the probability integral method model when the inclined length L of the working surface increases. Fig.10 , Fig.11 From this, it can be inferred that the parameter σ is closely related to the working surface parameters.

[0141] 4) Parameters related to inflection point offset

[0142] In the logarithmic function model, the parameter ω becomes larger, causing the overall shape of the function curve to shift upward, as shown in Fig.12 , Fig.13 In the probability integration method model, the above phenomenon will occur when the inflection point offset changes.

[0143] 5) Parameters related to maximum subsidence value and mining thickness

[0144] It can be seen from the figure that K w It mainly controls the maximum sinking value. It can be seen from the form of the function that it is similar to the mqcosa function in the probability integral method model. Fig.14 , Fig.15 .

[0145] By comparing and analyzing the parameters in the probability integral method model, we can clearly understand the meaning of each parameter in the model. Before the estimated calculation, the parameters must be inverted using the measured data.

[0146] The embodiment of the present invention provides a simulation system for coal seam mining subsidence, comprising:

[0147] The coal seam mining simulation module is used to obtain the surface subsidence curve formed by surface subsidence. The mining subsidence simulation module is used to segment the surface subsidence curve to obtain the basin curve in the middle of the surface subsidence curve and the settlement curve above the middle of the surface subsidence curve; the settlement curve is fitted with a logarithmic function model to simulate the subsidence of the surface above the middle of the surface subsidence curve along the direction of the mining working face; and the basin curve is fitted with a parabolic function model to simulate the subsidence of the surface in the middle of the surface subsidence curve along the direction of the mining working face; by fitting the logarithmic function model and the parabolic function model of the surface subsidence curve, a mining subsidence prediction model is constructed.

[0148] A specific embodiment is as follows:

[0149] The 11118 working face of a certain mine adopts comprehensive mechanized coal mining technology, and the roof is managed by caving method. Fig.16 As shown. The maximum mining thickness is 3.46m, the minimum is 2.2m, and the average is 2.95m. The coal seam dip angle is between 12° and 14°, with an average of 13.5°. The actual strike length of the working face is 620m, and the dip length is 171.8m. The mining depth is 498m, of which the loose layer thickness is 400m and the bedrock thickness is 98m.

[0150] Table 1 Comparison of parameter calculation effects between probability integral method and logarithmic function model

[0151] Model parameter [vv] Fitting error / mm Probability integral method q=0.76; s1=79.15; s2=6.4; r=305.8 125450 43.6 Logarithmic function method δ=66.9; σ=524.7; τ=69; ω=2.5; K=884.6 95140 37.9

[0152] from Fig.17 It can be seen from Table 1 that the effect of using the logarithmic function model to fit the measured values ​​is significantly improved.

[0153] The method flow chart of the present invention is as follows Fig.18 shown.

[0154] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for simulating coal seam mining subsidence, characterized in that: The following steps are involved: Obtaining a surface subsidence curve formed by surface subsidence, wherein the surface subsidence represents the subsidence of the surface above the coal seam mining working face; The surface subsidence curve is segmented to obtain the basin curve in the middle of the surface subsidence curve and the settlement curve above the middle of the surface subsidence curve; the settlement curve is fitted using a logarithmic function model to simulate the settlement of the surface above the middle of the surface subsidence curve along the direction of the mining working face; and the basin curve is fitted using a parabolic function model to simulate the settlement of the surface in the middle of the surface subsidence curve along the direction of the mining working face; A mining subsidence prediction model is constructed by fitting the logarithmic function model and the parabolic function model of the surface subsidence curve.

2. A method for simulating coal seam mining subsidence as claimed in claim 1, characterized in that: The said fitting of the sedimentation curve comprises the following specific steps: Construct a coordinate system, the origin of which is the surface position corresponding to the midpoint in the direction of the coal seam mining working face; the x-axis of the coordinate system is parallel to the direction of the working face, and the y-axis is the vertical direction perpendicular to the surface; Among them, x is the coordinate value in the direction of the coal seam mining working face, y is the settlement value in the vertical direction perpendicular to the ground surface, r is the horizontal distance from the coordinate origin to the inflection point of the settlement curve, R is the horizontal distance between the coordinate origin and the outermost boundary of the settlement curve, and a and h are both constants related to the mining thickness of the working face.

3. A method for simulating coal seam mining subsidence as claimed in claim 2, characterized in that: The basin curve is fitted, and the specific steps include: Design functions to simulate the basin curve in the middle of the surface subsidence curve according to simulation conditions, including: The sedimentation curve is simulated by y1(x) and y3(x), and the basin curve is simulated by y2(x), as the first simulation condition; set up is the second simulation condition; Set y1′(r)=y2′(r), y2′(r′)=y3′(r′) as the third simulation condition; Analyze the inflection point where the settlement curve connects with the basin curve to obtain: y1(r)=a+h; y3(r′)=a′+h′; Among them, r' is the horizontal distance between the coordinate origin on the other side of the coordinate origin and the inflection point of the settlement curve, R' is the horizontal distance between the coordinate origin on the other side of the coordinate origin and the outermost boundary of the settlement curve, and a' and h' are both constants related to the mining thickness of the working face on the other side of the coordinate origin; Construct y2(x) to simulate the basin curve: y2(x)=b1x 2 +b2x+b3 Among them, b1, b2 and b3 are all constants; make but: Integrating y2'(x) yields: Where C represents a constant related to the maximum settlement; The basin curve y2(x) in the middle of the simulated surface subsidence curve is obtained.

4. A method for simulating coal seam mining subsidence as claimed in claim 2, characterized in that: The construction of the mining subsidence prediction model specifically includes the following steps: according to get: but: Substitute h and h' into the simulated settlement curve respectively, and together with the simulated basin curve, construct the mining subsidence prediction model: in, When the coal seam to be mined is a horizontal coal seam, a=a′, r=-r′, R=R′, The case of piecewise functions is:

5. A simulation system for coal seam mining subsidence, characterized in that: include: Coal mining simulation module, used to obtain the surface subsidence curve formed by surface subsidence; The mining subsidence simulation module is used to segment the surface subsidence curve to obtain the basin curve in the middle of the surface subsidence curve and the settlement curve above the middle of the surface subsidence curve; use the logarithmic function model to fit the settlement curve to simulate the subsidence of the surface above the middle of the surface subsidence curve along the direction of the mining working face; and use the parabolic function model to fit the basin curve to simulate the subsidence of the surface in the middle of the surface subsidence curve along the direction of the mining working face; by fitting the logarithmic function model and the parabolic function model of the surface subsidence curve, a mining subsidence prediction model is constructed.

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