A simulation method and system for coal seam mining subsidence

Abstract

The present invention discloses a simulation method and system for coal seam mining subsidence, which relates to the field of coal mine mining subsidence, and includes: coal seam mining work causes the surface above the mining face to subside, and the surface subsidence curve formed by the surface subsidence is obtained; the surface subsidence curve is segmented to obtain the basin curve in the middle of the surface subsidence curve and the subsidence curve above the middle of the surface subsidence curve; function models are used to fit the subsidence curve and the basin curve respectively to construct a mining subsidence prediction model, which reflects the vertical subsidence caused by surface subsidence in the strike direction of the mining face. The present invention can accurately predict the mining subsidence situation.

Description

Technical Field

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

[0002] The mining subsidence of coal resources 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 to be solved. If the relevant information of the damaged land can be obtained in advance before the coal resources are mined, it is of great significance for controlling the degradation of the ecological environment and formulating reclamation measures.

[0003] In the prior art, the probability integral method is used to predict mining subsidence. The subsidence caused by mining subsidence will generate a basin. The probability integral method is relatively accurate in predicting near the maximum subsidence value, but the predicted subsidence range is inaccurate at the two side boundaries of the basin, resulting in inaccurate prediction of mining subsidence by this method. Summary of the Invention

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

[0005] The embodiments of the present invention provide a simulation method for coal seam mining subsidence, including the following steps: obtaining a surface subsidence curve formed by surface subsidence, where the surface subsidence represents the subsidence occurring on the surface above the coal seam 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; using a logarithmic function model to fit the subsidence 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; constructing a mining subsidence prediction model by fitting the logarithmic function model and the parabolic function model of the surface subsidence curve.

[0006] Further, the specific steps of fitting the subsidence curve include:

[0007] Construct a coordinate system, where the origin of the coordinate system 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 strike of the coal seam mining face, y is the settlement value in the vertical direction perpendicular to the ground surface, r is the horizontal distance between the coordinate origin and 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 both a and h represent constants related to the mining thickness of the working face.

[0010] Further, the specific steps for fitting the basin curve include:

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

[0012] Through y 1 (x) and y 3 (x) to simulate the settlement curve, and through y 2 (x) to simulate the basin curve as the first simulation condition;

[0013] Set as the second simulation condition;

[0014] Set y 1 ′(r) = y 2 ′(r), y 2 ′(r′) = y 3 ′(r′) as the third simulation condition;

[0015] Analyze the inflection point at the connection of the settlement curve and the basin curve to obtain:

[0016] y 1 (r) = a + h; y 3 (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 both a’ and h’ are constants related to the mining thickness of the working face on the other side of the coordinate origin;

[0018] Construct y 2 (x) to simulate the basin curve:

[0019] y 2 (x) = b 1 x 2 + b 2 x + b 3

[0020] Among them, b 1 , b 2 and b 3 are all constants;

[0021] Let Then:

[0022]

[0023] Integrating \(y'(x)\) with respect to \(y\) gives: 2 ’(x) to obtain:

[0024]

[0025] where \(C\) represents a constant related to the maximum settlement;

[0026] Obtain the basin curve \(y\) in the middle of the simulated surface subsidence curve 2 (x).

[0027] Furthermore, the steps of constructing the mining subsidence prediction model specifically include:

[0028] According to Obtain:

[0029]

[0030] Then:

[0031]

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

[0033]

[0034] where,

[0035] When the mined coal seam is a horizontal coal seam, \(a = a'\), \(r=-r'\), \(R = R'\), The case of the piecewise function is:

[0036]

[0037] An embodiment of the present invention provides a simulation system for coal seam mining subsidence, including: obtaining a surface subsidence curve formed by surface subsidence; a mining subsidence simulation module for 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; using a logarithmic function model to fit the settlement curve to simulate the surface subsidence above the middle of the surface subsidence curve along the strike of the mining face; and using a parabola function model to fit the basin curve to simulate the surface subsidence in the middle of the surface subsidence curve along the strike of the mining face; constructing a mining subsidence prediction model by fitting the logarithmic function model and the parabola function model of the surface subsidence curve.

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

[0039] The surface subsidence curve is divided into the basin curve in the middle of the surface subsidence curve and the subsidence curve above the middle of the surface subsidence curve for segmented analysis. The logarithmic function model is used to fit the subsidence curve, and the parabolic function model is used to fit the basin curve. Compared with the method of using one function for simulation, the subsidence conditions in different regions can be accurately distinguished by segmentation, so as to accurately simulate the mining subsidence of the coal seam. Description of the Drawings

[0040] Figure 1 It is a similarity material model design drawing (10-degree model) provided by an embodiment of the present invention;

[0041] Figure 2 It is an overall movement vector diagram after the inclined coal seam mining is stable provided by an embodiment of the present invention;

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

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

[0044] Figure 5 It is an effect diagram of the fitting of the subsidence curve of the similarity material inclined model provided by an embodiment of the present invention;

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

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

[0047] Figure 8 It is a morphological diagram of the change curve of the parameter τ provided by an embodiment of the present invention;

[0048] Figure 9 It is a morphological diagram of the change curve of tanβ in the downhill direction in the probability integral model provided by an embodiment of the present invention;

[0049] Figure 10 It is a morphological diagram of the change curve of the parameter σ provided by an embodiment of the present invention;

[0050] Figure 11 It is a morphological diagram of the change curve of the working face dip length in the probability integral model provided by an embodiment of the present invention;

[0051] Figure 12The morphological diagram of the parameter ω variation curve provided by the embodiment of the present invention;

[0052] Figure 13 The morphological diagram of the inflection point offset distance variation curve in the probability integral model provided by the embodiment of the present invention;

[0053] Figure 14 For the parameter K provided by the embodiment of the present invention w Variation curve morphological diagram;

[0054] Figure 15 The morphological diagram of the subsidence coefficient variation curve in the probability integral model provided by the embodiment of the present invention;

[0055] Figure 16 The layout diagram of the 11118 observation station provided by the embodiment of the present invention;

[0056] Figure 17 The comparison curve graph of the subsidence between the probability integral method and the logarithmic function model provided by the embodiment of the present invention;

[0057] Figure 18 The method flow chart provided by the embodiment of the present invention. Detailed implementation manners

[0058] To make the above objects, features and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention in conjunction with the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

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

[0060] Step 1: Obtain the surface subsidence curve formed by surface subsidence, where the surface subsidence refers to the subsidence that occurs on the surface above the coal seam mining working face.

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

[0062] The specific analysis process is as follows:

[0063] 1. Similar material simulation experiment.

[0064] (1) Similar material simulation.

[0065] The similar material simulation experiment belongs to physical simulation experiments. According to the similarity principle, the rock stratum prototype is scaled down by a certain ratio and a model is made using similar materials. The coal seam in the model is mined according to the time ratio, and the displacement of the target points on the model is obtained through deformation monitoring means, and then converted into actual values. By analysis, the transmission law of mining subsidence in overlying strata is obtained.

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

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

[0068] 1) Design the thickness of each rock stratum according to the borehole columnar section of a certain mining area.

[0069] 2) The height of the water-conducting fissure zone formed after model mining is basically the same as the measured value.

[0070] 3) The maximum subsidence on the model surface is basically the same as the measured value.

[0071] The model simulates the mining of an inclined coal seam with an inclination angle of 10 degrees. The design drawing of the model results is as Figure 1 shown.

[0072] In order to obtain high-precision and high-quality test data, this test plans 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 simply referred to as industrial measurement systems.

[0073] Before model observation, it is first necessary to paste necessary marker points on the model. The marker points can be divided into coded points and non-coded points. Both types of marker points can be automatically recognized in the XJTUDP software system and their three-dimensional coordinates can be calculated 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 0.03 mm to 0.1 mm. The number of point clouds measured by the XJTUOM dense point cloud measurement system in one scan is 1 million to 6 million, and the interval between each point is 0.08 mm to 1 mm. The measurement accuracy is 0.01 to 1 mm according to the single-shot field size and the number of camera pixels, and is generally 0.03 mm.

[0075] (2) Results of similar material simulation.

[0076] A series of observational data were obtained from the simulation test, such as the overall movement vector diagram after the extraction of inclined coal seams stabilized, as Figure 2 shown.

[0077] According to the results of the similar material simulation and fitting with the existing probability integral method prediction model, it was found that the fitting effect was relatively poor in the section where the subsidence value was less than 300 mm on both sides of the basin, as Figure 3 shown. A large number of measured data also proved that the probability integral method was relatively accurate in prediction near the maximum subsidence value, but converged rapidly at the basin boundary, and the predicted basin range was smaller than the actual basin range.

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

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

[0080] (1) Model construction

[0081] The study found that the principle of surface subsidence after coal mining was similar to that of groundwater seepage. After the coal seam was mined, the overlying rocks gradually collapsed and compacted downward and then transmitted upward, while due to the pumping well, the groundwater gradually seeped downward along the water level, and their movement principles were somewhat similar. The surface subsidence formed by the influence of coal seam working face mining was a subsidence basin, just like a subsidence funnel, which was very similar to the precipitation funnel formed by pumping water from a pumping well in a phreatic aquifer. As Figure 4 shown, the water level drawdown funnel formed by pumping water from a fully penetrating well in a confined aquifer in the cross-section. The thickness of the homogeneous and isotropic medium aquifer was M, the original water level of the aquifer was H 0 , and the water level in the well after the pumping of the fully penetrating well stabilized was h w , the thickness of the saturated water layer was H, the permeability coefficient was K, and the radius of the wellbore was r w . The range of the water level line affected by the well pumping was represented by the radius of influence R (unit: m), and it was calculated according to the Siechardt empirical formula:

[0082]

[0083] where: S w was the drawdown of the water level in the well (m); K was the permeability coefficient (m / s).

[0084] Based on the calculation and derivation of the seepage model, the equation of the phreatic surface was finally obtained as:

[0085]

[0086] Inspired by the phreatic surface equation derived from the seepage model of the overlying confined aquifer above, the subsidence prediction model on the main section of the working face mining subsidence initially established 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 working 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 takes a negative value, the corresponding function formula can be changed to:

[0089]

[0090] From the preliminary tests of formulas (1-3) and (1-4), the subsidence curve shape of the upper boundary of the surface subsidence basin on the main section caused by mining subsidence can be obtained. A particularly sharp and narrow funnel is formed in the middle, and it changes with the changes of r and the value range of x in the formula (x is not taken in a range close to 0, for example ). In order to adapt to the shape of the subsidence curve on the main section of the mining working face strike, a function must be constructed to express the curve shape in the middle of the funnel. Assume this function is y 2 (x). Since the constructed mathematical model is a prediction model applicable to all coal seam inclinations, it is assumed that the prediction function on the main section along the full dip on the uphill side is y 1 (x), and the prediction function on the main section along the full dip on the downhill side is y 3 (x), and its 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 y 2 (x) to make it conform to the subsidence curve shape to be expressed, three conditions must be satisfied simultaneously:

[0094] (1) The basin curve shape of the function y 2 (x) plot.

[0095] (2)

[0096] (3) y 1 ′(r) = y 2 ′(r), y 2 ′(r′) = y 3 ′(r′).

[0097] According to: y 1y(r) = a + h

[0098]

[0099] y 3 y(r′) = a′ + h′

[0100]

[0101] Construct y 2 The function of y(x) is:

[0102] y 2 y(x) = b 1 x 2 + b 2 x + b 3 (1 - 6)

[0103] Let Then the function of y′(x) can be constructed as: 2 ′(x) function is:

[0104]

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

[0106]

[0107] Also from We get:

[0108]

[0109] Then

[0110]

[0111]

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

[0113]

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

[0115] a = a′, r = -r′, R = R′,

[0116] That is, the special case of the 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 before can vividly describe the surface subsidence curve formed by mining, so the research and analysis start from the logarithmic function. The preliminarily determined mathematical model is:

[0119] W(x) = K w [f 1 (x)-f 2 (x)] (1 - 15)

[0120] In the formula, W(x) is the predicted surface subsidence value on the main section, K w is the maximum subsidence value, f 1 (x), f 2 (x) are functions containing the independent variable x with values in the range [0, 1]. According to the curve shape of the logarithmic function and multiple Matlab numerical verification and analysis, it is preliminarily determined that:

[0121]

[0122] The parameter affecting the maximum value in the model is K w , but simply changing the value of K w results in a worse overall fitting effect of the curve. Therefore, a new parameter ω is introduced here. ω replaces the term with exponent “-1” in the functions f 1 (x), f 2 (x), and its role is to affect and change the maximum value of the function fitting. The modified f 1 (x), f 2 (x) functions are:

[0123]

[0124] The newly determined predicted model of surface subsidence is:

[0125]

[0126] In the formula, K w represents a new parameter related to the maximum subsidence value; represents a new parameter related to the mining depth in the uphill direction; σ represents a new parameter related to the length of the mining face; τ represents a new parameter related to the mining depth in the downhill direction; ω represents a new parameter related to K w and the coal seam dip angle α; f 1 (x) and f 2 (x) are both functions containing the independent variable x with values in the range [0, 1].

[0127] The relevant analysis of the above five newly added parameters is only preliminary. The physical meanings of the newly assumed parameters and the inferred correlations are only initially determined based on the relationships to be expressed in the model building or the morphological factors of the influence function, etc. Subsequently, the fitting values of the parameters and their physical meanings, as well as the working face mining factors covered (including mining thickness, mining depth, working face size, mining geological conditions, coal seam dip angle, etc.) will be specifically studied and analyzed. The reliability of the established mathematical model must be analyzed by fitting with the measured data of the similar material model.

[0128] The fitting form is described mathematically. Assuming the mathematical model used is y = f(x, θ), where θ is a variable non - linear function, x is the input vector, and y is the output variable, then the total squared error is:

[0129]

[0130] In the formula, (x i , y i ) are the known observed data points. The goal to be achieved is to minimize the total squared error E(θ). Since the parameter θ is non - linear, E(θ) is not a linear function of θ. Therefore, when finding the minimum value of the total squared error E(θ), the best value of θ cannot be obtained by setting the derivative of E(θ) with respect to θ equal to zero. That is to say, the best parameter value in Equation (2 - 20) cannot be obtained by the method of taking the derivative. So, a general optimization method must be adopted to find the minimum value of E(θ). In this case, the downhill simplex search method in the Matlab encapsulated function fitting is used, and its function command is fminsearch. By writing the corresponding program code according to the call format of the Matlab encapsulated function and substituting the observed data, the minimum value of the objective function can be obtained, and thus each parameter in the mathematical model function can be obtained, and the fitting values of the observation points can be calculated.

[0131] Using the downhill simplex search method (Downhill Simplex Search) analyzed above for non - linear function fitting to find parameters, the fitting effect diagrams of the surface subsidence curves in each similar material model are as Figure 5 shown.

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

[0133] To study the role of each parameter in the function, the method of graphical analysis is adopted. When studying any one parameter, the values of other parameters are fixed.

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

[0135] In the logarithmic function model, when the parameter ζ is larger, the maximum subsidence value is smaller, the convergence of the boundary in the uphill direction is slower, and the change in the curve shape in the downhill direction is not obvious. See Figure 6 . In the probability integral method model, when the tangent value of the main influence angle in the uphill direction, tanβ, is smaller, that is, when the main influence radius r 2 in the uphill direction is larger, the maximum subsidence value is smaller, and the convergence of the uphill boundary is slower. See Figure 7 . Thus, it can be inferred that the parameter ζ is closely related to the main influence radius r 2 in the probability integral model.

[0136] 2) Parameters related to the main influence radius in the downhill direction

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

[0138] In the probability integral method model, when the tangent value of the main influence angle in the downhill direction, tanβ, is smaller, that is, when the main influence radius r 1 in the downhill direction is larger, the maximum subsidence value is smaller, and the convergence of the downhill boundary is slower. See Figure 9 . Thus, it can be inferred that the parameter τ is closely related to the main influence radius r 1 in the probability integral model.

[0139] 3) Parameters related to the working face advancing distance and the working face size

[0140] In the logarithmic function model, when the parameter σ is larger, the change in the function curve shape is consistent with the change in the function curve shape in the probability integral method model when the inclined length L of the working face increases. See Figure 10 、 Figure 11 . Thus, it can be inferred that the parameter σ is closely related to the working face parameters.

[0141] 4) Parameters related to the inflection point offset distance

[0142] In the logarithmic function model, when the parameter ω becomes larger, it causes the overall trend of the function curve shape to shift upward in the uphill direction. See Figure 12 、 Figure 13 . In the probability integral method model, the above phenomenon occurs when the inflection point offset distance changes.

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

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

[0145] According to the comparative analysis with the parameters in the probability integral method model, the meanings represented by each parameter in the model can be clearly obtained. Before the prediction calculation, the parameters must be inverted with the measured data first.

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

[0147] A coal seam mining simulation module, used to obtain the surface subsidence curve formed by surface subsidence. A mining subsidence simulation module, used to segment the surface subsidence curve to obtain the basin curve in the middle of the surface subsidence curve and the subsidence curve above the middle of the surface subsidence curve; use the logarithmic function model to fit the subsidence curve to simulate the subsidence of the surface above the middle of the surface subsidence curve along the strike of the mining face; and use the parabola function model to fit the basin curve to simulate the subsidence of the surface in the middle of the surface subsidence curve along the strike of the mining face; construct a mining subsidence prediction model by fitting the logarithmic function model and the parabola function model of the surface subsidence curve.

[0148] A specific embodiment is as follows:

[0149] The 11118 working face of a certain mine adopts the fully mechanized coal mining technology, and the roof is managed by the caving method, as Figure 16 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 thickness of the loose layer is 400m and the thickness of the bedrock is 98m.

[0150] Table 1 Comparison of the parameter calculation effects between the probability integral method and the 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 Figure 17 and Table 1, it can be seen that: using the logarithmic function model to fit the measured values, the effect is significantly improved.

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

[0154] The above-mentioned embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended 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; Wherein, the fitting of the sedimentation curve comprises: 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 between the coordinate origin and 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; 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 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.

3. A method for simulating coal seam mining subsidence as claimed in claim 1, 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:

4. 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 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; wherein, the fitting of the settlement curve includes: constructing a coordinate system, the origin of the coordinate system 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 between the coordinate origin and 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; A mining subsidence prediction model is constructed by fitting the logarithmic function model and the parabolic function model of the surface subsidence curve.

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