Real-time inversion of physical property parameters in intelligent mines and real-time optimization method for coal seam mining simulation

Abstract

The present invention relates to the technical field of intelligent mine exploitation, and in particular to a method for real-time inversion of physical property parameters and real-time optimization of coal seam exploitation simulation in an intelligent mine. Specifically, it includes arranging monitoring points to monitor physical property parameter stress data σ and displacement data u in real time; establishing a mathematical model composed of geometric equations, equilibrium equations, constitutive equations, and initial-boundary conditions; using a solver to simulate the coal seam exploitation process to obtain simulated stress data σ 模拟 and displacement data u 模拟 ; comparing the real-time simulated stress data σ 模拟 and displacement data u 模拟 of each monitoring point with the on-site real-time monitored stress data σ 监测 and displacement data u 监测 , and constructing a loss function; using an optimization algorithm to make the coal seam exploitation simulation more accurate and determine the formation physical property parameters at the same time. This method can improve the accuracy and optimization effect of coal seam exploitation simulation, thereby improving the safety and resource utilization rate of mine exploitation.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent mine exploitation, and particularly to a method for real-time inversion of physical property parameters of the strata in an intelligent mine and optimization of the simulation results of coal seam exploitation by using on-site monitoring data. Background Art

[0002] With the continuous development of mine exploitation technology, intelligent mines have become an important part of modern mining industry. Intelligent mines mainly carry out intelligent transformation on various links such as mine production, management, and safety by applying advanced information technology, Internet of Things, big data, artificial intelligence and other technical means to achieve efficient, safe, environmentally friendly and sustainable development of mine production. Intelligent mines rely on a large amount of real-time monitoring data and numerical simulation technology to achieve efficient exploitation and management of mine resources.

[0003] Coal seam exploitation simulation refers to the virtual simulation of the coal seam exploitation process through computer simulation technology to evaluate and optimize the exploitation plan and improve the exploitation efficiency and safety. Coal seam exploitation simulation involves multiple aspects such as geological modeling, exploitation process simulation, and environmental impact assessment, and is an important tool in modern mining engineering. The first step is geological data collection, mainly obtaining the geological data of the coal seam through means such as drilling and logging, including information such as coal seam thickness, dip angle, faults, and joints; then using these geological data to establish a three-dimensional geological model of the coal seam to accurately describe the shape and distribution of the coal seam.

[0004] Traditional coal seam exploitation simulation usually based on static physical property parameters of the strata, and these parameters come from historical data or prior models. However, the physical property parameters measured by current geophysical exploration technology have problems such as large discreteness, much noise, and many outliers, with high data processing costs and insufficient accuracy. In addition, due to the complexity of geological conditions and the dynamic changes of strata characteristics during the coal seam exploitation process, the simulation results relying only on static models are difficult to accurately reflect the actual exploitation situation, thus affecting the safety and economy of coal seam exploitation. Therefore, how to use on-site monitoring data to real-time invert the physical property parameters of the strata and optimize the exploitation simulation results has become an urgent problem to be solved in the current field of intelligent mines. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for real-time inversion of physical property parameters of an intelligent mine and real-time optimization of the simulation results of coal seam exploitation by using on-site monitoring data, so as to improve the accuracy and optimization effect of coal seam exploitation simulation, thereby improving the safety and resource utilization rate of mine exploitation.

[0006] To solve the problems of the existing technology, the present invention discloses a method for real-time inversion of physical property parameters and real-time optimization of coal seam mining simulation in an intelligent mine. The first step: arranging monitoring points to monitor physical property parameters in real time: establishing M strata above the coal seam to be mined, assigning Young's modulus E and Poisson's ratio v to each stratum, setting N monitoring points in the roadway of the coal seam to be mined, and collecting rock stress data σ and displacement data u at the position of each monitoring point in real time.

[0007] Preferably, for the M strata established above the coal seam to be mined, M is determined according to the actual situation, and M ≥ 25.

[0008] Preferably, the N monitoring points set in the roadway of the coal seam to be mined are arranged at equal intervals, and N ≥ 3. More preferably, the distance between two adjacent monitoring points is set to 10 meters.

[0009] Preferably, for Young's modulus E and Poisson's ratio v of each stratum, their value ranges are: 10 8 ≤ E ≤ 10 11 , 0.1 ≤ v ≤ 0.4.

[0010] The second step: modeling the coal seam mining area:

[0011] The geometric equation of this model is expressed by formula (1), where ε is the strain tensor and u represents the displacement,

[0012] The equilibrium equation of this model is expressed by formula (2), ▽·σ + f = 0 (2), where σ is the stress tensor and f is the body force,

[0013] The relationship σ(ε) between σ and ε in formula (1) and formula (2) is called the constitutive equation. For viscoelastic materials, the stress is a function of the strain rate.

[0014] Setting the two sides and the bottom of the roadway as non-slip boundary conditions, the fixed constraint and the normal displacement constraint are respectively expressed by formula (3) and formula (4):

[0015] u| Γ = 0 (3), where u represents the displacement and Γ represents the boundary part of the roadway,

[0016] u·n Γ′ = 0 (4), where u represents the displacement, n is the unit normal vector at the boundary, and Γ’ represents the bottom of the calculation domain of the fixed constraint.

[0017] Preferably, setting the two sides and the bottom of the roadway as non-slip boundary conditions means fixing the displacements of the two sides and the bottom of the roadway to 0.

[0018] More preferably, in a specific three-dimensional case, if the displacement field is u(x, y, z), the components of the displacement in the x, y, and z directions are all zero, and the fixed constraint conditions are:

[0019] u x |Γ= 0 (9), u y |Γ=0 (10), u z |Γ=0 (11).

[0020] The stress and normal stress boundary conditions are respectively expressed as formulas (5) and (6),

[0021] n·σ Γ″ =t0(5), where n is the unit vector of the outer normal at the boundary, t0 is the surface force vector acting on the rock mass at the boundary, and Γ” is the boundary region where the stress is applied,

[0022] n·σ·n Γ″ =-p0(6), where n is the unit vector of the outer normal at the boundary, p0 is the pressure acting on the rock mass at the boundary, and Γ” is the boundary region where the stress is applied.

[0023] The gravitational field added to the model is expressed as formula (7), f g =ρg(7), where ρ is the density of the formation, with the unit: kg / m 3 , g is the gravitational acceleration vector, and in the three-dimensional case, g = (0, 0, -9.81) m / s 2 .

[0024] Preferably, the gravitational acceleration vector g is a fixed value within the range of (0, 0, -9.81) m / s 2 range.

[0025] Step 3: Use the solver to simulate the coal seam mining process and obtain the simulated stress data σ 模拟 and displacement data u 模拟 : Input the established model formulas (1)-(7) into the solver, and according to the actual coal seam mining situation, set the initial conditions and boundary conditions for the simulation, including the mining depth, mining speed, roadway shape and size, geological structure, and external forces, to obtain the simulated stress data σ 模拟 and displacement data u 模拟 .

[0026] Preferably, the solver used is the open-source software OpenGeoSys.

[0027] Step 4: Compare the real-time simulated stress data σ 模拟 and displacement data u 模拟 of each monitoring point with the on-site real-time monitored stress data σ 监测and displacement data u 监测 , construct a loss function:

[0028] The real-time simulated stress data σ 模拟 and the real-time simulated displacement data u 模拟 are respectively aligned with the on-site real-time monitored stress data σ 监测 and the on-site real-time monitored displacement data u 监测 to ensure the matching of the two sets of data in space and time; based on the two sets of aligned data, the sum of the squares of the differences between the on-site actual monitored data and the corresponding simulated data is used to construct a loss function; then, in the case of the isotropic linear elastic constitutive equation, the expression of the loss function L is formula (8):

[0029]

[0030] where N is the total number of monitoring points in step (S1), including k stress monitoring points and N - k displacement monitoring points, i is each natural number less than or equal to N, M is the total number of strata divided in step (S1), j is each natural number less than or equal to M, E j is the Young's modulus of the j-th stratum, and v j is the Poisson's ratio of the j-th stratum.

[0031] Fifth step: Use an optimization algorithm to determine the physical properties parameters of the strata:

[0032] Set the initial physical properties parameters of each stratum, the Young's modulus E 初始 and the Poisson's ratio v 初始 , and use different optimization methods to optimize the loss function, and determine the optimization algorithm after comparison.

[0033] Use this optimization algorithm to optimize the loss function shown in formula (8) so that the difference between the simulated stress data σ 模拟 obtained in the solver and the on-site real-time monitored stress data σ 监测 , the simulated displacement data u 模拟 and the on-site real-time monitored displacement data u 监测 becomes smaller and smaller until the value of the loss function L calculated according to formula (8) is within the threshold range, and the physical properties parameters of the strata are determined.

[0034] Preferably, the determined optimization algorithm is the sequential quadratic programming method.

[0035] By adopting the method of combining on-site monitoring data with numerical simulation, the present invention can inversely determine the physical properties parameters of the strata of the intelligent mine in real time and optimize the simulation results of coal seam mining, and has the following remarkable beneficial effects:

[0036] 1. Accuracy and safety: This application constructs a closed-loop feedback system. By continuously optimizing the loss function and dynamically updating the formation physical property parameters, it realizes the real-time optimization of the coal seam mining process, ensuring a high degree of consistency between the simulation results and the actual mining situation. At the same time, the present invention can reflect the stress and displacement changes of the formation during the coal seam mining process in real time. By accurately inversing the formation physical property parameters, potential dangerous areas can be identified in advance, thereby enhancing the safety of coal seam mining.

[0037] 2. Low cost and universality: The present invention can replace traditional methods to obtain more accurate formation physical property parameters, reducing the data processing cost and improving the efficiency and accuracy of data processing. Due to the ability to inversely calculate and update the formation physical property parameters in real time, the method of the present invention can adapt to complex geological conditions and the dynamic changes of formation characteristics during the coal seam mining process, ensuring the wide adaptability of the model in practical applications.

[0038] 3. While introducing new technologies to the transparent geology field, the present invention also provides sufficient data support for the intelligent mine field. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a schematic diagram of the two-dimensional structure of the present invention;

[0040] Figure 2 is a comparison chart of the optimization errors using three line search techniques;

[0041] Figure 3 is a line chart of the errors of the three line search techniques between each iteration step. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The present invention will be further described below with reference to the drawings. The following embodiments are only used to more clearly illustrate the structure of the present invention.

[0043] A method for real-time inversion of physical property parameters in an intelligent mine and real-time optimization of coal seam mining simulation. First step: We need to arrange monitoring points to monitor the physical property parameters in real time. As Figure 1 shown, M formations are established above the coal seam to be mined, and the Young's modulus E and Poisson's ratio v of each formation are assigned values. N monitoring points are set in the roadway of the coal seam to be mined to collect the rock stress data σ and displacement data u at the position of each monitoring point in real time.

[0044] The M formations established above the coal seam to be mined, M is determined according to the actual situation, and M≥25.

[0045] The N monitoring points set in the roadway of the coal seam to be mined are arranged at equal intervals, and N≥3. More preferably, the interval between two adjacent monitoring points is set at 10 meters.

[0046] The Young's modulus E and Poisson's ratio v of each formation, the value range is: 108 ≤E≤10 11 ,0.1 ≤ v ≤ 0.4

[0047] The Young's modulus E and Poisson's ratio v of each stratum are different. In this embodiment, we set M to 32. Then, according to the situation, we set the Young's modulus of the 1st - 25th strata to 1×10 10 、while the Young's moduli of the 26th - 30th strata are 2.8×10 10 、1.5×10 9 、6×10 9 、6×10 9 、6×10 9 、and the Young's modulus of the 31st - 32nd strata is 1×10 10 ; the Poisson's ratio of the 1st - 32nd strata is set to 0.4

[0048] Step 2: Model the coal seam mining area. The mechanical mathematical model of rock mass deformation consists of geometric equations, equilibrium equations, constitutive equations, and initial - boundary conditions

[0049] The geometric equation of this model is expressed as formula (1), where ε is the strain tensor and u represents displacement

[0050] The equilibrium equation of this model is expressed as formula (2), ▽·σ + f = 0 (2), where σ is the stress tensor and f is the body force

[0051] The relationship σ(ε) between σ and ε in formula (1) and formula (2) is called the constitutive equation. For visco - elastic materials, stress is a function of the strain rate

[0052] Regarding the setting of the initial - boundary conditions, set the cross - section of the roadway to be rectangular, and the two sides and the bottom to be non - slip boundary conditions. Then, the fixed constraint and the normal displacement constraint are respectively expressed as formula (3) and formula (4):

[0053] u| Γ = 0 (3), where u represents displacement and Γ represents the boundary part of the roadway

[0054] u·n Γ′ = 0 (4), where u represents displacement, n is the unit outer normal vector at the boundary, and Γ’ represents the bottom of the calculation domain of the fixed constraint

[0055] Setting the two sides and the bottom of the roadway to be non - slip boundary conditions means fixing the displacements of the two sides and the bottom of the roadway to 0. In the specific three - dimensional case, the displacement field is u(x, y, z), then the components of the displacement in the x, y, and z directions are all zero. The fixed constraint condition is

[0056] u x|Γ = 0 (9), u y |Γ = 0 (10), u z |Γ = 0 (11).

[0057] Its stress and normal stress boundary conditions are respectively expressed as equations (5) and (6),

[0058] n·σ Γ″ = t0 (5), where n is the unit outer normal vector at the boundary, t0 is the surface force vector acting on the rock mass at the boundary, Γ” is the boundary region where the stress is applied,

[0059] n·σ·n Γ″ = -p0 (6), where n is the unit outer normal vector at the boundary, p0 is the pressure acting on the rock mass at the boundary, Γ” is the boundary region where the stress is applied.

[0060] Considering the influence of the vertical pressure on the formation, in order to fully ensure the reliability of the model, we add a gravity field to the model. This gravity field is expressed as equation (7), f g = ρg (7), where ρ is the density of the formation, unit: kg / m 3 , g is the gravitational acceleration vector, in three dimensions g = (0, 0, -9.81) m / s 2 .

[0061] This gravitational acceleration vector g is a fixed value within the range of (0, 0, -9.81) m / s 2 .

[0062] Step 3: Use the solver to simulate the coal seam mining process to obtain the simulated stress data σ 模拟 and displacement data u 模拟 : Input the established model equations (1)-(7) into the OpenGeoSys solver, and according to the actual coal seam mining situation, set the initial conditions and boundary conditions for the simulation, including the mining depth, mining speed, roadway shape and size, geological structure, and external forces, to obtain the simulated stress data σ 模拟 and displacement data u 模拟 . When the mining vehicle is mining forward in the coal seam area, for each mined time step, calculate the simulated stress data σ 模拟 and displacement data u 模拟 , and finally collate and summarize multiple sets of measurement values for multiple time steps.

[0063] Step 4: Compare the real-time simulated stress data σ 模拟 and displacement data u 模拟 of each monitoring point with the on-site real-time monitored stress data σ 监测 and displacement data u 监测, construct the loss function:

[0064] Align the real-time simulated stress data σ 模拟 , the real-time simulated displacement data u 模拟 with the on-site real-time monitored stress data σ 监测 and the on-site real-time monitored displacement data u 监测 respectively to ensure the matching of the two groups of data in space and time; based on the two aligned groups of data, construct a loss function with the sum of squares of the differences between the on-site actual monitored data and the corresponding simulated data; then, in the case of the isotropic linear elastic constitutive equation, the expression of the loss function L is formula (8):

[0065]

[0066] where N is the total number of monitoring points in step (S1), including k stress monitoring points and N - k displacement monitoring points, i is each natural number less than or equal to N, M is the total number of strata divided in step (S1), j is each natural number less than or equal to M, E j is the Young's modulus of the j-th stratum, and v j is the Poisson's ratio of the j-th stratum.

[0067] Fifth step: Use the optimization algorithm to determine the physical properties parameters of the strata:

[0068] Set the initial physical properties parameters of each stratum, the Young's modulus E 初始 and the Poisson's ratio v 初始 , and use different optimization methods to optimize the loss function, and then determine the optimization algorithm after comparison.

[0069] Use this optimization algorithm to optimize the loss function shown in formula (8) so that the difference between the simulated stress data σ 模拟 obtained in the solver and the on-site real-time monitored stress data σ 监测 , the simulated displacement data u 模拟 and the on-site real-time monitored displacement data u 监测 becomes smaller and smaller until the value of the loss function L calculated according to formula (8) is within the threshold range, and then determine the physical properties parameters of the strata.

[0070] When we set the Young's modulus of the 1st - 25th strata to 1×10 10 , and the Young's moduli of the 26th - 30th strata are 2.8×10 10 , 1.5×10 9 , 6×10 9 , 6×10 9 , 6×10 9 , and the Young's modulus of the 31st - 32nd strata is 1×10 10; Set the Poisson's ratio of the 1st to 32nd strata to 0.4. Then, use different optimization algorithms to optimize the loss function formula (8). Here, we determine the sequential quadratic programming method as the optimization algorithm. During optimization, in each iteration step, apply the updated parameter values to the numerical simulation, and recalculate the real-time simulated stress data σ 模拟 and the real-time simulated displacement data u 模拟 . Then, calculate the loss function L formula (8) again. This process continues until the value of the loss function L converges within a small error range, indicating that the formation parameters are determined.

[0071] As Figure 2 shown, in the sequential quadratic programming algorithm, three different inexact line search techniques, namely the Armijo criterion, the Wolfe criterion, and the Fletcher criterion, are used for optimization. It can be seen that:

[0072] 1. When the number of iteration steps is the same, the optimization effect of the Wolfe criterion is better. After 25 iterations, the root mean square error of the loss function is only 6.2855.

[0073] 2. When the optimization effects are the same, the time required by the Wolfe criterion is the shortest.

[0074] 3. Overall effect: The Wolfe criterion is better than the Fletcher criterion, which is better than the Armijo criterion.

[0075] While Figure 3 shows the relative error of Young's modulus E between each iteration step after optimization using the three different inexact line search techniques of the Armijo criterion, the Wolfe criterion, and the Fletcher criterion. It can also be intuitively seen from the figure the change amount of E at each iteration step, indicating that the above optimization algorithm is effective and further verifying the convergence of the algorithm.

[0076] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and deformations can be made, and these improvements and deformations should also be regarded as the protection scope of the present invention.

Claims

1. A real-time inversion method for physical property parameters of an intelligent mine and a real-time optimization method for coal seam mining simulation, characterized in that: It includes the following steps: (S1) Arrange monitoring points to monitor physical properties in real time: Establish M strata above the coal mining seam, assign values to the Young's modulus E and Poisson's ratio v of each stratum, set N monitoring points in the roadway of the coal mining seam, and collect the overlying strata stress data σ and displacement data u at the positions of each monitoring point in real time; (S2) Model the coal mining area: The geometric equation of this model is expressed as formula (1), where ε is the strain tensor and u represents displacement, The equilibrium equation of the model is expressed as Equation (2), where σ is the stress tensor and f is the body force, The relationship σ(ε) between σ and ε in Formula (1) and Formula (2) is called the constitutive equation. For viscoelastic materials, stress is a function of the strain rate. Set the two sides and the bottom of the roadway as non-slip boundary conditions, and the fixed constraint and normal displacement constraint are respectively expressed as Formula (3) and Formula (4): u| Γ = 0 (3), where u represents displacement, Γ represents the boundary part of the roadway, u·n| Γ′ = 0 (4), where u represents displacement, n is the unit normal vector at the boundary, and Γ’ represents the bottom of the computational domain with fixed constraints, The stress and normal stress boundary conditions are respectively expressed as Formula (5) and Formula (6). n·σ| Γ″ = t0 (5), where n is the unit normal vector at the boundary, t0 is the surface force vector exerted on the rock mass at the boundary, and Γ” is the boundary region where the stress is applied. n·σ·n| Γ″ = -p0 (6), where n is the unit outer normal vector at the boundary, p0 is the pressure exerted on the rock mass at the boundary, and Γ” is the boundary region where the stress is applied. The vertical gravity field added to the model is expressed as Formula (7). f g = ρg (7), where ρ is the density of the formation, unit: kg / m 3 , g is the gravitational acceleration vector, and in three dimensions g = (0, 0, -9.81) m / s 2 ; (S3) Use a solver to simulate the coal seam mining process and obtain the simulated stress data σ at each monitoring point 模拟 and displacement data u 模拟 : Input the established model formulas (1)-(7) into the solver, and set the initial conditions and boundary conditions for the simulation according to the actual coal seam mining situation, including the mining depth, mining speed, roadway shape and size, geological structure, and external forces, to obtain the simulated stress data σ 模拟 and displacement data u 模拟 ; (S4) Compare the real-time simulated stress data σ of each monitoring point 模拟 and displacement data u 模拟 with the on-site real-time monitored stress data σ 监测 and displacement data u 监测 , and construct a loss function: The real-time simulated stress data σ 模拟 and the real-time simulated displacement data u 模拟 are respectively aligned with the on-site real-time monitored stress data σ 监测 and the on-site real-time monitored displacement data u 监测 to ensure the matching of the two sets of data in space and time; based on the two sets of aligned data, the sum of the squares of the differences between the on-site actual monitored data and the corresponding simulated data is used to construct a loss function; then, in the case of the isotropic linear elastic constitutive equation, the expression of the loss function L is given by Equation (8): Among them, N is the total number of monitoring points in step (S1), including k stress monitoring points and N - k displacement monitoring points, i is each natural number less than or equal to N, M is the total number of strata divided in step (S1), j is each natural number less than or equal to M, E j is the Young's modulus of the j-th stratum, v j is the Poisson's ratio of the j-th stratum; (S5) Use the optimization algorithm to determine the physical properties of the strata: Set the initial physical property parameters of each stratum, Young's modulus E 初始 and Poisson's ratio v 初始 , optimize the loss function using different optimization methods, and determine the optimization algorithm after comparison; Use this optimization algorithm to optimize the loss function shown in formula (8), so that the simulated stress data σ 模拟 obtained in the solver and the on-site real-time monitored stress data σ 监测 , the simulated displacement data u 模拟 and the on-site real-time monitored displacement data u 监测 have an increasingly smaller difference, until the value of the loss function L calculated according to formula (8) is within the threshold range, and the formation physical property parameters are determined.

2. A real-time inversion method for physical property parameters and a real-time optimization method for coal seam mining simulation in an intelligent mine according to claim 1, characterized in that: For the fixed constraint condition shown in Formula (3) in step (S2), in the specific three-dimensional case, the displacement field is u(x, y, z), then the components of the displacement in the x, y, and z directions are all zero, and the fixed constraint condition is: u x |Γ = 0 (9), u y |Γ = 0 (10), u z |F = 0 (11).

3. A real-time inversion method for physical property parameters and a real-time optimization method for coal seam mining simulation in an intelligent mine according to claim 1, characterized in that: In step (S2), the gravitational acceleration vector g in formula (7) is a fixed value within the range of (0, 0, -9.81) m / s 2 2.

4. A real-time inversion method for physical property parameters and a real-time optimization method for coal seam mining simulation in an intelligent mine according to claim 1, characterized in that: The M strata established above the coal mining seam in step (S1), M is determined according to the actual situation, and M≥25.

5. A real-time inversion method for physical property parameters and a real-time optimization method for coal seam mining simulation in an intelligent mine according to claim 1, characterized in that: The N monitoring points set in the roadway of the coal mining seam in step (S1) are set at equal intervals, and N≥3.

6. A real-time inversion method for physical property parameters and a real-time optimization method for coal seam mining simulation in an intelligent mine according to claim 5, characterized in that: The adjacent two monitoring points in step (S1) are set at an interval of 10 meters.

7. A real-time inversion method for physical property parameters and a real-time optimization method for coal seam mining simulation in an intelligent mine according to claim 1, characterized in that: The solver used in step (S3) is the open-source software OpenGeoSys.

8. A real-time inversion method for physical property parameters and a real-time optimization method for coal seam mining simulation in an intelligent mine according to claim 1, characterized in that: The optimization algorithm determined in step (S5) is the sequential quadratic programming method.

9. A real-time inversion method for physical property parameters and a real-time optimization method for coal seam mining simulation in an intelligent mine according to claim 1, characterized in that: In step (S1), the Young's modulus E and Poisson's ratio v of each formation have value ranges as follows: 10 8 ≤ E ≤ 10 11 , 0.1 ≤ v ≤ 0.

4.

10. A real-time inversion method for physical property parameters and a real-time optimization method for coal seam mining simulation in an intelligent mine according to claim 1, characterized in that: The non-slip boundary condition set for the two sides and the bottom of the roadway in step (S1) is to fix the displacements of the two sides and the bottom of the roadway to 0.

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