Mine area surface subsidence prediction method and system based on segmented weighted parameter assignment

Abstract

The invention provides a mining area surface subsidence prediction method and system based on segmented weighted parameter assignment, and belongs to the field of coal mining subsidence prediction. A surface subsidence curve is drawn in a segmented Knoth time function model, and a time influence coefficient is calculated; determining an actual subsidence curve which comprises an upper half part and a lower half part according to the time influence coefficient in combination with the actual condition of surface subsidence of the mining area; respectively calculating weights of the upper half part and the lower half part of the actual subsidence curve according to different time stages of surface subsidence; based on the weights of the upper half part and the lower half part of the actual subsidence curve, obtaining a subsidence prediction model of segmented weighted parameter assignment, and calculating to obtain a total surface subsidence value; according to the stage characteristics of the surface subsidence process, the weights of the upper half part and the lower half part of an actual subsidence curve are calculated through self-adaptive weighting, the segmented Knoth time function model is improved based on the weights, a subsidence prediction model of segmented weighted parameter assignment is obtained, and the problem that the prediction error of the surface subsidence prediction model is large is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mining subsidence prediction, and in particular to a method and system for predicting surface subsidence in a mining area based on segmented weighted parameter assignment. Background Art

[0002] Large-scale mining of coal resources has caused changes in the structure of the original underground rock layers, resulting in collapse of the surface above, damaging farmland, buildings, water systems, etc., and destroying the ecological environment of the mining area. Accurate prediction of coal mining subsidence areas is a difficult problem that needs to be solved urgently in the industry.

[0003] At present, the commonly used surface subsidence prediction models mainly include probability integral method and time function model, among which the time function model mainly refers to Knothe time function model. The above two surface subsidence prediction models have different focuses when predicting surface subsidence. The probability integral method focuses on predicting the final result of surface subsidence, while the Knothe time function model is more suitable for describing the dynamic process of surface subsidence. In 1953, Polish scholar Knothe assumed that the sinking speed of a certain point on the surface at a certain moment is proportional to the difference between the final sinking amount of the point and the sinking amount at that moment. Based on this assumption, a time function model of the relationship between the sinking amount of a certain point on the surface and time was proposed, namely the Knothe time function model. However, studies have found that it does not quite conform to the actual surface subsidence. In 2003, Chang Zhanqiang proposed the segmented Knothe time function model based on the specific characteristics of surface subsidence in my country, which improved the prediction accuracy. However, the segmented Knothe time function model and other surface subsidence prediction models assume that surface subsidence is a symmetrical process, and eventually a symmetrical subsidence basin with the center of the coal mining face as the symmetry axis is formed. In fact, the surface subsidence basin is not completely symmetrical, and the subsidence rate and morphology at different stages of the subsidence process will change differently. Therefore, using this type of subsidence prediction model for subsidence prediction often causes large errors.

[0004] In the prior art, in order to improve the accuracy of dynamic prediction of surface subsidence, the Chinese invention patent application with publication number CN115660197A, "A method for dynamic prediction of surface subsidence in goaf areas based on a time function combination model", establishes an optimal non-negative variable weight combination model based on time function for dynamic prediction of surface subsidence in goaf areas. However, the problems with this method are as follows: (1) the calculation process of using a time function combination prediction is more complicated than that of a single time function model; (2) the time functions used are Knothe time function and Logistic time function, which do not conform to the actual surface subsidence in my country and have great limitations in practical application. Summary of the invention

[0005] The technical problem to be solved by the present invention is how to solve the problem that the surface subsidence prediction model of coal mining subsidence area using the segmented Knothe time function predicts the surface subsidence with large errors.

[0006] The present invention solves the above technical problems through the following technical solutions: a method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment, comprising:

[0007] Draw the surface subsidence curve in the piecewise Knothe time function model and calculate the time influence coefficient;

[0008] Combined with the actual surface subsidence in the mining area, the actual subsidence curve is determined according to the time influence coefficient. The actual subsidence curve includes the upper half and the lower half.

[0009] The weights of the upper and lower parts of the actual subsidence curve are calculated according to the different time stages of surface subsidence;

[0010] Based on the weights of the upper and lower parts of the actual subsidence curve, a piecewise weighted parameterized subsidence prediction model is obtained to calculate the total surface subsidence value.

[0011] The present invention notes that the existing segmented Knothe time function model assumes that surface subsidence is a symmetrical process when predicting surface subsidence. In fact, the surface subsidence basin is not completely symmetrical, and the subsidence rate and morphology at different stages of the subsidence process will have different changes. At this time, using the segmented Knothe time function model to predict surface subsidence will produce large errors. The present invention calculates the weights of the upper and lower parts of the actual subsidence curve respectively based on the stage characteristics of the surface subsidence process through adaptive weighting, and improves the segmented Knothe time function model based on the weights of the upper and lower parts of the actual subsidence curve to obtain a segmented weighted parameterized subsidence prediction model. The two segments of predicted values ​​are the most accurate in their respective intervals. The total surface subsidence value is obtained based on the weighted calculation of the two segments of predicted values, which can significantly improve the accuracy of surface subsidence prediction work and solve the problem of large prediction errors in the surface subsidence prediction model in coal mining subsidence areas.

[0012] Preferably, the calculation formula of the time influence coefficient is:

[0013]

[0014] Among them, γ is the density of the overburden, in kg / m3; E is the elastic modulus of the overburden, in MPa; σ is the tensile strength of the overburden, in MPa; κ is the Poisson's ratio of the overburden; h is the thickness of the loose layer, in m.

[0015] Preferably, determining the actual sinking curve according to the time influence coefficient specifically includes:

[0016] 1.1. The surface subsidence curves corresponding to two different time influence coefficients are drawn. The time influence coefficients are c 1 、c 2 , taking half of the maximum surface subsidence value as the inflection point, the surface subsidence curve is divided into the upper half and the lower half;

[0017] 1.2. Combined with the actual situation of surface subsidence in the mining area, according to the time influence coefficient c 1 、c 2 Determine the actual sinking curve.

[0018] Preferably, the calculation formula for the weights of the upper and lower parts of the actual sinking curve is:

[0019]

[0020] Among them, p 1 、p 2 are the weights of the upper and lower parts of the actual sinking curve, respectively, c 1 、c 2 They are all time influence coefficients, T represents the total time of surface subsidence, and t represents the moment.

[0021] Preferably, the calculation formula of the subsidence prediction model with segmented weighted parameters is:

[0022]

[0023] Where W(t) is the total surface subsidence value in mm, W i (t) is the subsidence value of the ground surface at time t, in mm, where i = 1, 2, W 0 is the maximum surface subsidence value, in mm; c i is the time coefficient, i = 1, 2; τ is the time corresponding to the maximum sinking speed of the ground point, τ = 0.5T, T represents the time from coal seam mining to stable sinking of the surface, p 1 、p 2 are the weights of the upper and lower parts of the actual sinking curve respectively.

[0024] The present invention also provides a mining area surface subsidence prediction system based on segmented weighted parameter assignment, comprising:

[0025] Model parameter calculation module, used to draw the surface subsidence curve in the piecewise Knothe time function model and calculate the time influence coefficient;

[0026] The actual subsidence curve establishment module is used to determine the actual subsidence curve according to the actual surface subsidence of the mining area and the time influence coefficient. The actual subsidence curve includes the upper half and the lower half;

[0027] The adaptive weighting module is used to calculate the weights of the upper and lower parts of the actual subsidence curve according to the different time stages of surface subsidence;

[0028] The subsidence prediction module is used to obtain a subsidence prediction model with segmented weighted parameters based on the weights of the upper and lower parts of the actual subsidence curve, and calculate the total surface subsidence value.

[0029] Preferably, the calculation formula of the time influence coefficient in the model parameter calculation module is:

[0030]

[0031] Among them, γ is the density of the overburden, in kg / m3; E is the elastic modulus of the overburden, in MPa; σ is the tensile strength of the overburden, in MPa; κ is the Poisson's ratio of the overburden; h is the thickness of the loose layer, in m.

[0032] Preferably, the actual sinking curve establishing module determines the actual sinking curve according to the time influence coefficient and specifically includes:

[0033] 1.1. The surface subsidence curves corresponding to two different time influence coefficients are drawn. The time influence coefficients are c 1 、c 2 , taking half of the maximum surface subsidence value as the inflection point, the surface subsidence curve is divided into the upper half and the lower half;

[0034] 1.2. Combined with the actual situation of surface subsidence in the mining area, according to the time influence coefficient c 1 、c 2 Determine the actual sinking curve.

[0035] Preferably, the calculation formula for the weights of the upper and lower parts of the actual sinking curve in the adaptive weighting module is:

[0036]

[0037]

[0038] Among them, p 1 、p 2 are the weights of the upper and lower parts of the actual sinking curve, respectively, c 1 、c 2 They are all time influence coefficients, T represents the total time of surface subsidence, and t represents the moment.

[0039] Preferably, the calculation formula of the subsidence prediction model with segmented weighted parameters in the subsidence prediction module is:

[0040]

[0041] Where W(t) is the total surface subsidence value in mm, W i (t) is the subsidence value of the ground surface at time t, in mm, where i = 1, 2, W 0 is the maximum surface subsidence value, in mm; c i is the time coefficient, i = 1, 2; τ is the time corresponding to the maximum sinking speed of the ground point, τ = 0.5T, T represents the time from coal seam mining to stable sinking of the surface, p 1 、p 2 are the weights of the upper and lower parts of the actual sinking curve respectively.

[0042] The advantages provided by the present invention are:

[0043] (1) The present invention notes that the existing segmented Knothe time function model assumes that surface subsidence is a symmetrical process when predicting surface subsidence. In fact, surface subsidence basins are not completely symmetrical, and the subsidence rates and shapes at different stages of the subsidence process will vary. At this time, using the segmented Knothe time function model to predict surface subsidence will result in large errors. The present invention calculates the weights of the upper and lower parts of the actual subsidence curve by adaptive weighting according to the stage characteristics of the surface subsidence process, and improves the segmented Knothe time function model based on the weights of the upper and lower parts of the actual subsidence curve to obtain a segmented weighted parameterized subsidence prediction model. The two segments of predicted values ​​are the most accurate in their respective intervals. The total surface subsidence value is obtained based on the weighted calculation of the two segments of predicted values, which can significantly improve the accuracy of surface subsidence prediction and solve the problem of large prediction errors in surface subsidence prediction models in coal mining subsidence areas.

[0044] (2) The present invention improves the segmented Knothe time function model and adopts a single time function model. Under the premise of ensuring the accuracy of surface subsidence prediction, the calculation process is greatly simplified and has better practical application value and prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flow chart of a method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment provided by an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of a method for establishing an actual subsidence curve in a method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment provided by an embodiment of the present invention;

[0047] Figure 3 A curve showing a change in weights in an adaptive weighting module in a mining area surface subsidence prediction system based on segmented weighted parameter assignment provided by an embodiment of the present invention;

[0048] Figure 4The method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment provided by an embodiment of the present invention is applied to the subsidence prediction result diagram of the 61103 working face;

[0049] Figure 5 The method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment provided by an embodiment of the present invention is applied to the subsidence prediction result diagram of the 61208 working face;

[0050] Figure 6 The mining area surface subsidence prediction method based on segmented weighted parameter assignment provided in the embodiment of the present invention is applied to the subsidence prediction result map of the 61302 working face. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the technical solution of the present invention is clearly and completely described below in combination with specific embodiments and with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example 1

[0053] like Figure 1 As shown, this embodiment provides a method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment, comprising the following steps:

[0054] Step 1: Draw the surface subsidence curve in the piecewise Knothe time function model and calculate the time influence coefficient.

[0055] In the piecewise Knothe time function model, the parameter that controls the shape of the surface subsidence curve is the time influence coefficient. The smaller the value of the time influence coefficient, the slower the change trend of the surface subsidence curve. Conversely, the larger the value of the time influence coefficient, the steeper the change trend of the surface subsidence curve. The time influence coefficient is related to the mechanical properties of the overlying rock strata of the coal seam. The calculation formula of the time influence coefficient is:

[0056]

[0057] Among them, γ is the density of the overburden, in kg / m3; E is the elastic modulus of the overburden, in MPa; σ is the tensile strength of the overburden, in MPa; κ is the Poisson's ratio of the overburden; h is the thickness of the loose layer, in m.

[0058] Step 2: Based on the actual surface subsidence situation in the mining area, determine the actual subsidence curve according to the time influence coefficient. The actual subsidence curve includes the upper and lower parts.

[0059] The present invention combines the actual situation of surface subsidence in the mining area and determines the actual subsidence curve according to the size of the time influence coefficient. The specific method is as follows:

[0060] 1.1. Take half of the maximum surface subsidence as the inflection point, such as Figure 2 As shown, the maximum surface subsidence value is W 0 , the subsidence value of the surface subsidence curve is W 0 The coordinate point corresponding to / 2 is the inflection point, which divides the surface subsidence curve into the upper half and the lower half. Figure 2 In the figure, two surface subsidence curves corresponding to different time influence coefficients are drawn. The time influence coefficients are c 1 、c 2 , Figure 2 The time influence coefficient c shown in 1 <c 2 ;

[0061] The time influence coefficients can be calculated as c 1 、c 2 :

[0062]

[0063] In formula (1), γ 1 is the density of the overburden, in kg / m 3 ; E 1 is the elastic modulus of the overburden rock, in MPa; σ t1 is the tensile strength of the overburden, in MPa; κ 1 is the Poisson's ratio of the overburden; h 1 is the thickness of the loose layer, in m.

[0064]

[0065] In formula (2), γ 2 is the density of the overburden, in kg / m 3 ; E 2 is the elastic modulus of the overburden rock, in MPa; σ t2 is the tensile strength of the overburden, in MPa; κ 2 is the Poisson's ratio of the overburden; h 2 is the thickness of the loose layer, in m.

[0066] 1.2. Combined with the actual situation of surface subsidence in the mining area, according to the time influence coefficient 1 、c 2 Determine the actual sinking curve. If the sinking value is less than W 0 / 2 has a faster surface subsidence rate, and the subsidence value is greater than W 0 / 2 surface subsidence rate is slow, then c 2As the time influence coefficient of the upper half of the actual sinking curve, c 1 As the time influence coefficient of the lower half of the actual sinking curve, Figure 2 The circled part is the actual sinking curve. Similarly, if the sinking value is less than W 0 / 2 The surface subsidence speed is slow, and the subsidence value is greater than W 0 / 2 surface subsidence rate is faster, then c 1 As the time influence coefficient of the upper half of the actual sinking curve, c 2 As the time influence coefficient of the lower half of the actual sinking curve.

[0067] Step 3: Calculate the weights p of the upper and lower parts of the actual subsidence curve according to the different time stages of surface subsidence. 1 、p 2 .

[0068] The present invention takes into account that the estimated values ​​of the upper and lower parts of the actual sinking curve are most accurate in their respective intervals. In order to make the actual sinking curve smoother as a whole, the present invention is based on the curve change characteristics of the linear function, such as Figure 3 As shown in the figure, through adaptive weighting, the weights p of the upper and lower parts of the actual subsidence curve are set according to the different time stages of surface subsidence. 1 、p 2 The calculation formula is as follows:

[0069]

[0070] Among them, c 1 、c 2 They are all time influence coefficients, T represents the total time of surface subsidence, and t represents the moment.

[0071] It should be noted that the order of step one and step three is not limited.

[0072] Step 4: Weight p based on the upper and lower parts of the actual sinking curve 1 、p 2 The subsidence prediction model with segmented weighted parameters is obtained, and the total surface subsidence value is calculated.

[0073] The calculation formula of the subsidence prediction model with segmented weighted parameters is:

[0074]

[0075] In formula (5), W(t) is the total surface subsidence value, in mm, and W i (t) is the subsidence value of the ground surface at time t, in mm, where i = 1, 2, W 0is the maximum surface subsidence value, in mm; c i is the time coefficient, i = 1, 2; τ is the time corresponding to the maximum sinking speed of the ground point, τ = 0.5T, T represents the time from coal seam mining to stable sinking of the surface, p 1 、p 2 are the weights of the upper and lower parts of the actual sinking curve respectively.

[0076] The present invention notes that the existing segmented Knothe time function model assumes that surface subsidence is a symmetrical process when predicting surface subsidence. In fact, the surface subsidence basin is not completely symmetrical, and the subsidence rate and morphology at different stages of the subsidence process will have different changes. At this time, using the segmented Knothe time function model to predict surface subsidence will produce large errors. The present invention calculates the weights of the upper and lower parts of the actual subsidence curve respectively based on the stage characteristics of the surface subsidence process through adaptive weighting, and improves the segmented Knothe time function model based on the weights of the upper and lower parts of the actual subsidence curve to obtain a segmented weighted parameterized subsidence prediction model. The two segments of predicted values ​​are the most accurate in their respective intervals. The total surface subsidence value is obtained based on the weighted calculation of the two segments of predicted values, which can significantly improve the accuracy of surface subsidence prediction work and solve the problem of large prediction errors in the surface subsidence prediction model in coal mining subsidence areas.

[0077] Example 2

[0078] This embodiment provides a mining area surface subsidence prediction system based on segmented weighted parameter assignment, including:

[0079] Model parameter calculation module, used to draw the surface subsidence curve in the piecewise Knothe time function model and calculate the time influence coefficient;

[0080] In the piecewise Knothe time function model, the parameter that controls the shape of the surface subsidence curve is the time influence coefficient. The smaller the value of the time influence coefficient, the slower the change trend of the surface subsidence curve. Conversely, the larger the value of the time influence coefficient, the steeper the change trend of the surface subsidence curve. The time influence coefficient is related to the mechanical properties of the overlying rock strata of the coal seam. The calculation formula of the time influence coefficient is:

[0081]

[0082] Among them, γ is the density of the overburden, in kg / m3; E is the elastic modulus of the overburden, in MPa; σ is the tensile strength of the overburden, in MPa; κ is the Poisson's ratio of the overburden; h is the thickness of the loose layer, in m.

[0083] The actual subsidence curve establishment module is used to determine the actual subsidence curve based on the actual surface subsidence in the mining area and the time influence coefficient. The actual subsidence curve includes the upper half and the lower half.

[0084] The present invention combines the actual situation of surface subsidence in the mining area and determines the actual subsidence curve according to the size of the time influence coefficient. The specific method is as follows:

[0085] 1.1. Take half of the maximum surface subsidence value as the inflection point, and the maximum surface subsidence value is W 0 , the subsidence value of the surface subsidence curve is W 0 The coordinate point corresponding to / 2 is the inflection point, which divides the surface subsidence curve into the upper half and the lower half. Figure 2 In the figure, two surface subsidence curves corresponding to different time influence coefficients are drawn. The time influence coefficients are c 1 、c 2 , Figure 2 The time influence coefficient c shown in 1 >c 2 ;

[0086] The time influence coefficients can be calculated as c 1 、c 2 :

[0087]

[0088] In formula (1), γ 1 is the density of the overburden, in kg / m 3 ; E 1 is the elastic modulus of the overburden rock, in MPa; σ t1 is the tensile strength of the overburden, in MPa; κ 1 is the Poisson's ratio of the overburden; h 1 is the thickness of the loose layer, in m.

[0089]

[0090] In formula (2), γ 2 is the density of the overburden, in kg / m 3 ; E 2 is the elastic modulus of the overburden rock, in MPa; σ t2 is the tensile strength of the overburden, in MPa; κ 2 is the Poisson's ratio of the overburden; h 2 is the thickness of the loose layer, in m.

[0091] 1.2. Combined with the actual situation of surface subsidence in the mining area, according to the time influence coefficient c 1 、c 2 Determine the actual sinking curve. If the sinking value is less than W0 / 2 has a faster surface subsidence rate, and the subsidence value is greater than W 0 / 2 surface subsidence rate is slow, then c 2 As the time influence coefficient of the upper half of the actual sinking curve, c 1 As the time influence coefficient of the lower half of the actual sinking curve, Figure 2 The circled part is the actual sinking curve. Similarly, if the sinking value is less than W 0 / 2 The surface subsidence speed is slow, and the subsidence value is greater than W 0 / 2 surface subsidence rate is faster, then c 1 As the time influence coefficient of the upper half of the actual sinking curve, c 2 As the time influence coefficient of the lower half of the actual sinking curve.

[0092] The adaptive weighting module is used to calculate the weights p of the upper and lower parts of the actual subsidence curve according to the different time stages of surface subsidence. 1 、p 2 ;

[0093] The present invention takes into account that the estimated values ​​of the upper and lower parts of the actual sinking curve are most accurate in their respective intervals. In order to make the actual sinking curve smoother as a whole, the present invention is based on the curve change characteristics of the linear function, such as Figure 3 As shown in the figure, the weights p of the upper and lower parts of the actual subsidence curve are calculated according to the different time stages of surface subsidence. 1 、p 2 The calculation formula is as follows:

[0094]

[0095] Among them, c 1 、c 2 They are all time influence coefficients, T represents the total time of surface subsidence, and t represents the moment.

[0096] The subsidence prediction module is used to predict the subsidence based on the weights p of the upper and lower parts of the actual subsidence curve. 1 、p 2 The subsidence prediction model with segmented weighted parameters is obtained, and the total surface subsidence value is calculated.

[0097] The calculation formula of the subsidence prediction model with segmented weighted parameters is:

[0098]

[0099] In formula (5), W(t) is the total surface subsidence value, in mm, and W i(t) is the subsidence value of the ground surface at time t, in mm, where i = 1, 2, W 0 is the maximum surface subsidence value, in mm; c i is the time coefficient, i = 1, 2; τ is the time corresponding to the maximum sinking speed of the ground point, τ = 0.5T, T represents the time from coal seam mining to stable sinking of the surface, p 1 、p 2 are the weights of the upper and lower parts of the actual sinking curve respectively.

[0100] Case Study

[0101] Using the subsidence prediction model with segmented weighted parameters of the present invention, the 61103, 61208 and 61302 working faces of Tangjiahui Coal Mine are selected as application objects. According to the measured data of Tangjiahui Coal Mine, formulas (1) and (2) are used to obtain the working face parameters corresponding to the model as shown in Table 1:

[0102] Table 1 Parameters of Knothe time function model with piecewise weighted parameter assignment

[0103]

[0104] According to the obtained parameters, two sets of predicted subsidence values ​​W at any point on the surface of the 61103, 61208 and 61302 working faces are calculated. 1 (t) and W 2 (t), using the adaptive weighting model to transform W 1 (t) and W 2 (t) Combined with the settlement values ​​of any point on the surface of the 61103, 61208 and 61302 working faces, and taking the measured values ​​as true values, the absolute error of the predicted values ​​is calculated. The results are shown in Tables 2-4 and Figure 4-Figure 6 As shown:

[0105] Table 261103 Estimated results of surface subsidence at the working face

[0106]

[0107] Table 361208 Estimated results of surface subsidence at the working face

[0108]

[0109]

[0110] Table 461302 Estimated results of surface subsidence at working face

[0111]

[0112] It can be seen that the mining area surface subsidence prediction method based on segmented weighted parameter assignment of the present invention can significantly improve the accuracy of surface subsidence prediction work.

[0113] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment, characterized in that: include: Draw the surface subsidence curve in the piecewise Knothe time function model and calculate the time influence coefficient; Combined with the actual surface subsidence in the mining area, the actual subsidence curve is determined according to the time influence coefficient. The actual subsidence curve includes the upper half and the lower half. The weights of the upper and lower parts of the actual subsidence curve are calculated according to the different time stages of surface subsidence; Based on the weights of the upper and lower parts of the actual subsidence curve, a piecewise weighted parameterized subsidence prediction model is obtained to calculate the total surface subsidence value.

2. The method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment according to claim 1 is characterized in that: The calculation formula of the time influence coefficient is: Among them, γ is the density of the overburden, in kg / m3; E is the elastic modulus of the overburden, in MPa; σ is the tensile strength of the overburden, in MPa; κ is the Poisson's ratio of the overburden; h is the thickness of the loose layer, in m.

3. The method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment according to claim 1 is characterized in that: Determining the actual sinking curve according to the time influence coefficient specifically includes: 1.

1. Two surface subsidence curves corresponding to different time influence coefficients are drawn. The time influence coefficients are c1 and c2 respectively. The surface subsidence curve is divided into the upper half and the lower half with half of the maximum surface subsidence value as the inflection point. 1.

2. Based on the actual surface subsidence situation in the mining area, determine the actual subsidence curve according to the time influence coefficients c1 and c2.

4. The method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment according to claim 1 is characterized in that: The calculation formula for the weights of the upper and lower parts of the actual sinking curve is: Among them, p1 and p2 are the weights of the upper and lower parts of the actual subsidence curve, c1 and c2 are time influence coefficients, T represents the total time of surface subsidence, and t represents the moment.

5. The method for predicting surface subsidence in a mining area based on segmented weighted parameter assignment according to claim 1 is characterized in that: The calculation formula of the subsidence prediction model with segmented weighted parameters is: Where W(t) is the total surface subsidence value in mm, W i (t) is the subsidence value of the ground surface at time t, in mm, where i = 1, 2, W0 is the maximum subsidence value of the ground surface, in mm; c i is the time coefficient, i=1, 2; τ is the moment corresponding to the maximum sinking velocity of the ground point, τ=0.5T, T represents the time from coal seam mining to surface steady sinking, p1 and p2 are the weights of the upper and lower parts of the actual sinking curve respectively.

6. A mining area surface subsidence prediction system based on segmented weighted parameter assignment, characterized in that: include: Model parameter calculation module, used to draw the surface subsidence curve in the piecewise Knothe time function model and calculate the time influence coefficient; The actual subsidence curve establishment module is used to determine the actual subsidence curve according to the actual surface subsidence of the mining area and the time influence coefficient. The actual subsidence curve includes the upper half and the lower half; The adaptive weighting module is used to calculate the weights of the upper and lower parts of the actual subsidence curve according to the different time stages of surface subsidence; The subsidence prediction module is used to obtain a subsidence prediction model with segmented weighted parameters based on the weights of the upper and lower parts of the actual subsidence curve, and calculate the total surface subsidence value.

7. The mining area surface subsidence prediction system based on segmented weighted parameter assignment according to claim 6 is characterized in that: The calculation formula of the time influence coefficient in the model parameter calculation module is: Among them, γ is the density of the overburden, in kg / m3; E is the elastic modulus of the overburden, in MPa; σ is the tensile strength of the overburden, in MPa; κ is the Poisson's ratio of the overburden; h is the thickness of the loose layer, in m.

8. The mining area surface subsidence prediction system based on segmented weighted parameter assignment according to claim 6 is characterized in that: Determining the actual sinking curve according to the time influence coefficient in the actual sinking curve establishing module specifically includes: 1.

1. Two surface subsidence curves corresponding to different time influence coefficients are drawn. The time influence coefficients are c1 and c2 respectively. The surface subsidence curve is divided into the upper half and the lower half with half of the maximum surface subsidence value as the inflection point. 1.

2. Based on the actual surface subsidence situation in the mining area, determine the actual subsidence curve according to the time influence coefficients c1 and c2.

9. The mining area surface subsidence prediction system based on segmented weighted parameter assignment according to claim 6 is characterized in that: The calculation formula for the weights of the upper and lower parts of the actual sinking curve in the adaptive weighting module is: Among them, p1 and p2 are the weights of the upper and lower parts of the actual subsidence curve, c1 and c2 are time influence coefficients, T represents the total time of surface subsidence, and t represents the moment.

10. The mining area surface subsidence prediction system based on segmented weighted parameter assignment according to claim 6 is characterized in that: The calculation formula of the subsidence prediction model with segmented weighted parameters in the subsidence prediction module is: Where W(t) is the total surface subsidence value in mm, W i (t) is the subsidence value of the ground surface at time t, in mm, where i = 1, 2, W0 is the maximum subsidence value of the ground surface, in mm; c i is the time coefficient, i=1, 2; τ is the moment corresponding to the maximum sinking velocity of the ground point, τ=0.5T, T represents the time from coal seam mining to surface steady sinking, p1 and p2 are the weights of the upper and lower parts of the actual sinking curve respectively.

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