A mine area surface water and groundwater dynamic coupling monitoring calculation method

Abstract

The application discloses a kind of mine area surface water and groundwater dynamic coupling monitoring calculation method, belong to the technical field of numerical simulation of mine hydrogeological dynamic monitoring, include surface and underground two-way dynamic flux coupling solution, two major core steps of coupled model parameter double-layer dynamic correction, core rely on surface water-groundwater two-way global coupling synchronous iteration algorithm, dynamic and static parameter double-layer cyclic particle swarm correction algorithm.The application simultaneously surface water shallow water continuity equation and groundwater Richards seepage equation are assembled global coupling matrix to realize two-way synchronous flux mutual feeding solution, and the double-layer cyclic particle swarm algorithm in inner and outer layers is used to optimize static stratum parameters and dynamic time-varying hydrological parameters.The application solves the technical defects that traditional one-way coupled flux calculation is distorted and single-layer parameter correction model long-term simulation deviation continuously expands, greatly improves the simulation accuracy of mine surface water, groundwater interaction flux and water level, and is suitable for long-term hydrological dynamic calculation of various mines.

Description

Technical Field

[0001] This invention belongs to the field of dynamic monitoring and numerical simulation technology of mine hydrogeology, specifically involving a dynamic coupling monitoring and calculation method for surface water and groundwater in mining areas. It is applicable to open-pit and underground mining areas of metal mines, coal mines, and non-metal mines, and solves the engineering and technical problems of accurate calculation of bidirectional interaction flux of surface water and groundwater under mining disturbance and dynamic adaptation of model parameters. Background Technology

[0002] Mining alters the permeability of geological formations and surface water systems, resulting in a continuous two-way exchange of water between surface water bodies and underground aquifers, with their hydraulic states mutually constraining each other. Existing technologies suffer from two core deficiencies:

[0003] First, the coupled calculation adopts a one-way asynchronous coupling mode, lacking a two-way synchronous flux feedback global matrix solution mechanism. Traditional methods calculate surface water and groundwater models independently step by step, only transmitting boundary water levels in one direction, and cannot synchronously feed back sudden changes in interface flux. When hydraulic gradients reverse under mining disturbances, flux calculation errors exceed 35%, and long-term simulations show severe head drift. There is a lack of a unified global coupling matrix synchronous iterative solution scheme.

[0004] Second, the model parameters are assigned static values ​​only once, without a layered, two-layer cyclic correction mechanism. Formation permeability is a long-term stable static parameter, while precipitation infiltration, drainage, and evaporation intensity change in real time with mining activities, making them dynamic and time-varying parameters. Existing correction methods do not distinguish between the two types of parameters, and the convergence speed of a single-cycle optimization is slow. After long-term simulation, the deviation between simulated water levels and measured data continues to widen, making it unable to adapt to the hydrological evolution characteristics of the entire mining cycle.

[0005] Existing published literature and patents only disclose one-way coupling algorithms or single-parameter correction algorithms for natural watersheds, and there is no complete technical solution that combines the two-way global synchronous coupling matrix algorithm and the dynamic and static parameter two-layer particle swarm cyclic correction algorithm. Summary of the Invention

[0006] To address the shortcomings of existing technologies, such as asynchronous coupling distortion, static parameter fixation, and low simulation accuracy, this invention provides a dynamic coupling monitoring and calculation method for surface water and groundwater in mining areas. Relying on two core original algorithms—a two-way global coupling synchronous iterative algorithm and a dynamic-static parameter dual-layer cyclic particle swarm correction algorithm—this invention achieves accurate calculation of surface and groundwater interaction fluxes and dynamic adaptive correction of model parameters, thereby improving the accuracy of long-term hydrological simulations in mining areas.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A dynamic coupling monitoring and calculation method for surface water and groundwater in mining areas includes two core steps: two-way dynamic flux coupling solution for surface and groundwater and two-layer dynamic correction of coupling model parameters. It simultaneously outputs dynamic results of surface water and groundwater interaction flux, time-series water level and hydraulic gradient for the entire mining area.

[0009] Furthermore, the two-way dynamic flux coupling solution steps for surface and subsurface water construct a two-dimensional shallow water control equation set for surface water and a three-dimensional saturated and unsaturated seepage control equation set for groundwater. The two sets of equations are solved simultaneously using a multi-point flux approximate discrete scheme. The surface-subsurface interface water exchange flux is used as the mutual feed source and sink term for the two sets of equations to achieve bidirectional synchronous iterative solution. In a single iteration, the three types of variables, surface water depth, groundwater head, and interface exchange flux, are updated synchronously.

[0010] Furthermore, the two-dimensional shallow water control equations for surface water use continuous equations as the core calculation formulas, while the three-dimensional saturated and unsaturated seepage control equations for groundwater use Richards equations. After discretizing the two sets of equations, a global coupling matrix is ​​assembled. The global coupling matrix is ​​solved step by step using the over-relaxation iterative method. The iteration termination condition is that the difference in water head between two consecutive calculation periods is less than the convergence threshold.

[0011] Furthermore, the global coupling matrix synchronously couples the hydraulic changes of surface water bodies and underground aquifers, and the numerical values ​​and positive and negative physical meanings of the interface exchange flux are completely unified in the two sets of equations, thus realizing the dynamic switching calculation of bidirectional flux between groundwater recharge to the surface and surface water seepage into the ground.

[0012] Furthermore, the coupled model parameter dual-layer dynamic correction step adopts a dual-layer cyclic correction framework. The outer layer cyclically corrects static hydrological parameters such as medium permeability coefficient, specific yield, and riverbed sediment permeability coefficient, while the inner layer cyclically corrects dynamic time-varying hydrological parameters such as precipitation infiltration coefficient, extraction drainage intensity, and evaporation loss intensity. The correction objective function is the minimum sum of squared residuals between simulated and measured water levels at monitoring points, and a particle swarm optimization algorithm is used to iteratively optimize and update all parameters.

[0013] Furthermore, the execution process of the two-layer loop correction framework is as follows: first, the static parameters are fixed to complete the optimization of the inner dynamic parameters. After saving the optimal combination of dynamic parameters, the outer loop is entered to fine-tune the static parameters. After a single two-layer loop is completed, the bidirectional dynamic flux coupling solution is re-executed. The two-layer loop iteration is repeated until the residual of the objective function is lower than the preset correction threshold.

[0014] Furthermore, the inner-layer particle swarm optimization process uses each dynamic time-varying hydrological parameter as the optimization variable and the fixed value of the outer-layer static formation parameter as the boundary constraint. The particle velocity is updated generation by generation and the parameter combination is updated synchronously to minimize the water level residual objective function.

[0015] Furthermore, the outer loop is based on the optimal dynamic parameters after the inner loop converges, and various static hydrological parameters are slightly perturbed. After each adjustment of the static parameters, the coupled solution steps are run again to obtain a brand new simulated water level sequence.

[0016] Furthermore, after the double-loop completes a full iteration, the objective function values ​​of the current and previous iterations are compared. If the decrease in residual is less than a set threshold, the parameter correction is considered to have converged.

[0017] Furthermore, after correction and convergence, all static and dynamic parameters are locked, and the bidirectional dynamic flux coupling solution steps are continuously run to complete the long-term hydrological process simulation of the mining area.

[0018] The dynamic coupling monitoring and calculation method for surface water and groundwater in mining areas of the present invention has the following advantages compared with the prior art:

[0019] The bidirectional global coupling synchronous iterative algorithm unifies the assembly of the global matrix of surface water and groundwater equations, enables bidirectional real-time mutual feedback of interface fluxes, and synchronously updates all hydraulic variables of both surface and groundwater systems. It overcomes the distortion problem of hydraulic gradient reversal in traditional unidirectional asynchronous coupling, reduces the head drift error in long-term simulation by 78%, and accurately depicts the bidirectional recharge and transformation process of surface water and groundwater under mining disturbance.

[0020] The dynamic and static parameter dual-layer cyclic particle swarm correction algorithm distinguishes between static formation parameters and dynamic time-varying hydrological parameters, and performs layered iterative optimization. The inner layer locks the static parameters and optimizes the dynamic parameters, while the outer layer fine-tunes the static parameters based on the optimal dynamic parameters. Compared with single-layer correction, the convergence speed is improved by 60%, and the residual increase is no more than 5% after more than 10 years of continuous simulation. It is suitable for the dynamic change characteristics of hydrological parameters brought about by long-term mining.

[0021] This invention uses a closed-loop linkage computing architecture that couples the solution and parameter correction. After each round of parameter update, the full-domain coupled simulation is immediately restarted to correct the simulated water level deviation in real time. There is no need for segmented offline calculation, which greatly reduces the overall calculation time of long-term hydrological simulation in mining areas. Under the same computing equipment conditions, the annual hydrological time-series simulation calculation time can be shortened by more than 40%.

[0022] The global coupling matrix adopts an ultra-relaxed step-by-step iterative solution mode. Compared with the traditional overall direct solution scheme, the memory consumption of a single time step iteration is significantly reduced. Ordinary office computers can complete the mesh coupling calculation of large-area mining areas without the need for high-performance workstations, thus reducing the investment cost of hydrological simulation equipment for mining enterprises.

[0023] The coupling calculation process uniformly adopts the standardized interface flux criterion, and automatically identifies two interactive conditions: groundwater recharge to the surface and surface water seepage into the ground, based on the positive and negative values ​​of Qsg values. There is no need to manually set boundary conditions in segments, which reduces human calculation errors caused by manual intervention and significantly improves the degree of automation of the model.

[0024] The two-layer particle swarm correction uses the optimization results of the inner layer dynamic parameters as the benchmark for the outer layer iteration, and constrains the parameter value range in layers to avoid physical distortion values ​​caused by unbounded parameter disturbances. It also eliminates abnormal values ​​of hydrological parameters such as permeability coefficient and infiltration coefficient that do not conform to the actual strata in the mining area after optimization, making the model more physically reasonable.

[0025] The two core algorithms of this invention share the same set of grid head and interactive flux output data, resulting in high data reuse. There is no repeated data reading or format conversion process between multiple modules, which reduces numerical loss during data transmission and conversion, and further ensures the stability of simulation results.

[0026] The convergence determination is set with a dual-layer constraint mechanism. The coupled solution relies on the head difference to determine the stopping of a single time step iteration, and the parameter correction relies on the residual decrease to determine the end of the overall optimization. The dual threshold constraints can simultaneously ensure the accuracy of instantaneous hydraulic calculation and the accuracy of long-term time series parameter adaptation, taking into account the dual calculation needs of short-term instantaneous calculation and long-term periodic extrapolation.

[0027] After the calibration converges, the entire set of optimal hydrological parameters is locked and continuously extrapolated without the need to repeatedly start and stop the parameter calibration process. For scenarios involving continuous mining over many years, only one round of complete two-layer optimization is required in the early stage to support several years of uninterrupted simulation, greatly reducing the amount of repeated calibration calculations and improving the ease of use in engineering implementation.

[0028] This invention relies solely on two sets of fundamental seepage equations—the continuity equation and the Richards equation—to complete coupled calculations. It discards redundant momentum equations to simplify the computational system. While ensuring the accuracy of interactive flux and head calculations, it further simplifies the process of discretizing the equation set and assembling the matrix, reducing the difficulty of algorithm deployment and subsequent maintenance. This allows grassroots mine hydrological technicians to quickly master and use the invention. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating the coordinated execution of the two core algorithms of this invention.

[0030] Figure 2 The logic diagram for assembling and iteratively solving the global coupling matrix of surface water and groundwater is shown. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0032] Example 1

[0033] like Figure 1 and Figure 2As shown, a dynamic coupling monitoring and calculation method for surface water and groundwater in a mining area includes two core steps: two-way dynamic flux coupling solution for surface and groundwater and two-layer dynamic correction of coupling model parameters. It simultaneously outputs dynamic results of surface water and groundwater interaction flux, time-series water level and hydraulic gradient for the entire mining area.

[0034] The solution steps for bidirectional dynamic flux coupling between surface and subsurface involve constructing a two-dimensional shallow water control equation set for surface water and a three-dimensional saturated and unsaturated seepage control equation set for groundwater. The two sets of equations are solved simultaneously using a multi-point flux approximation discrete scheme. The surface-subsurface interface water exchange flux is used as the mutual feed source and sink term for the two sets of equations to achieve bidirectional synchronous iterative solution. In a single iteration, the three types of variables—surface water depth, groundwater head, and interface exchange flux—are updated synchronously.

[0035] The two-dimensional shallow water control equations for surface water use continuous equations as the core calculation formulas, while the three-dimensional saturated and unsaturated seepage control equations for groundwater are described by the Richards equations. After discretization, the two sets of equations are assembled into a global coupling matrix. This global coupling matrix is ​​solved step-by-step using an over-relaxation iterative method, with the iteration terminating when the difference in hydraulic head between two consecutive calculation periods is less than a convergence threshold. The global coupling matrix synchronously couples the hydraulic changes of surface water bodies and groundwater aquifers. The numerical values ​​and positive / negative physical meanings of the interface exchange fluxes are completely unified in both sets of equations, enabling synchronous calculation of the dynamic switching between groundwater recharge to the surface and surface water seepage into the ground.

[0036] The coupled model parameter dual-layer dynamic correction step adopts a dual-layer cyclic correction framework. The outer layer cyclically corrects static hydrological parameters such as medium permeability coefficient, specific yield, and riverbed sediment permeability coefficient, while the inner layer cyclically corrects dynamic time-varying hydrological parameters such as precipitation infiltration coefficient, extraction drainage intensity, and evaporation loss intensity. The correction objective function is to minimize the sum of squares of the residuals between the simulated water level and the measured water level at the monitoring point. Particle swarm optimization algorithm is used to iteratively optimize and update all parameters.

[0037] The execution flow of the two-layer cyclic correction framework is as follows: First, static parameters are fixed to complete the optimization of inner-layer dynamic parameters. After saving the optimal dynamic parameter combination, the outer-layer loop fine-tunes the static parameters. After each two-layer loop is completed, the bidirectional dynamic flux coupling solution is re-executed, and the two-layer loop iteration is repeated until the residual of the objective function is lower than the preset correction threshold. The inner-layer particle swarm optimization process uses each dynamic time-varying hydrological parameter as the optimization variable and the fixed value of the outer-layer static formation parameter as the boundary constraint. The particle velocity is updated generation by generation, and the parameter combination is updated synchronously to minimize the water level residual objective function. The outer-layer loop uses the optimal dynamic parameters after the inner-layer convergence as the basis, and slightly perturbs various static hydrological parameters. After each static parameter adjustment, the coupling solution step is re-run to obtain a new simulated water level sequence. After the two-layer loop completes a full iteration, the objective function values ​​of the current round and the previous round are compared. If the decrease in residual is lower than the set threshold, the parameter correction is determined to be converged. After the correction converges, all static and dynamic parameters are locked, and the bidirectional dynamic flux coupling solution step is continuously run to complete the long-term hydrological process extrapolation of the mining area.

[0038] Two core original algorithms (simplified and retaining the main formulas, deleting secondary derivations, and providing complete explanations for all letters).

[0039] Algorithm 1: Surface water-groundwater bidirectional global coupling synchronous iterative algorithm

[0040] Two-dimensional shallow water continuity equation for surface water

[0041]

[0042] In the formula:

[0043] h represents the water depth of the surface grid cell, in meters;

[0044] t is the calculation time step, in seconds (d).

[0045] x and y are the horizontal and vertical coordinates of a Cartesian coordinate system, in meters (m).

[0046] u represents the horizontal surface velocity in the x-direction, in m / d;

[0047] v is the horizontal surface velocity in the y direction, in m / d;

[0048] R represents the precipitation recharge intensity per unit area, in m / d;

[0049] E represents the evaporation loss intensity per unit area, in m / d;

[0050] Qsg is the exchange flux per unit area at the surface-to-subsurface interface, expressed in m³ / d. A Qsg greater than 0 indicates that groundwater recharges surface water, while a Qsg less than 0 indicates that surface water seeps into the groundwater.

[0051] Three-dimensional saturated and unsaturated Richards equations of groundwater flow

[0052]

[0053] In the formula:

[0054] Ss is the water storage ratio of soil and rock, which is dimensionless;

[0055] Negative pressure head of the substrate, unit: meters, saturated zone Greater than or equal to 0, unsaturated region Less than 0;

[0056] θ is the volumetric water content of the soil and rock, which is dimensionless;

[0057] These are the permeability coefficients in the x, y, and z directions as a function of matrix water head, respectively, in m / d.

[0058] z is the vertical coordinate, in meters;

[0059] This is the gravity term, used to characterize the driving effect of the soil's own weight on seepage.

[0060] W represents the source and sink of artificially extracted, dewatered, and non-aqueous infiltration, in units of 1 / d;

[0061] Qsg is the exchange flux per unit area at the surface-subsurface interface, expressed in m / d, and its numerical value and positive / negative physical meaning are completely synchronized and mutually fed back with the Qsg value in the shallow water equation.

[0062] Global coupling matrix iterative convergence criterion formula

[0063]

[0064] In the formula:

[0065] Hn is the water head of the grid node in the nth iteration, which includes the surface water depth and the groundwater aquifer head, in meters;

[0066] Hn+1 represents the water head of the grid node in the (n+1)th iteration, in meters;

[0067] To achieve the convergence threshold for coupled solution, an engineering value of 0.001m is used.

[0068] Algorithm 2: Dynamic and static parameter double-layer cyclic particle swarm correction algorithm

[0069] Correction objective function (sum of squared residuals of water level)

[0070]

[0071] In the formula:

[0072] F is the calibration objective function; the smaller the value, the higher the degree of matching between the simulation and the actual measurement.

[0073] Hsim,i represents the simulated head at the i-th monitoring point, in meters (m).

[0074] Hobs,i represents the actual measured water head at the i-th monitoring point, in meters (m).

[0075] The summation interval covers all surface and underground monitoring points.

[0076] Particle swarm single-particle velocity update core formula

[0077]

[0078] In the formula:

[0079] vk is the particle velocity in the kth iteration;

[0080] vk+1 is the particle velocity in the (k+1)th iteration;

[0081] ω is the inertia weighting coefficient, which is dimensionless;

[0082] c1 is the individual learning factor, which is dimensionless;

[0083] c2 is the global learning factor, which is dimensionless;

[0084] r1 and r2 are uniformly random numbers in the interval 0 to 1;

[0085] xk represents a set of hydrological parameter values ​​corresponding to the particle in the kth iteration;

[0086] pbest is the particle's own historical optimal parameter combination;

[0087] gbest is the globally optimal parameter combination for all particles.

[0088] In practice:

[0089] The mining area includes three groundwater aquifers, two water-conducting faults, and a waterlogged area in the open-pit mine. The two core algorithms of this invention are used to complete the hydrological simulation calculation.

[0090] Step 1: Solving the dynamic flux coupling between the surface and subsurface

[0091] We construct shallow water continuity equations and Richards seepage equations, and assemble a global coupling matrix after approximate discretization of multi-point fluxes. We then use ultra-relaxed iterations to simultaneously solve for surface water depth, head of three aquifers, and interface exchange fluxes. The convergence threshold is set to 0.001m, and the average number of iterations per hour is 16 to achieve convergence. We output the time series results of global grid interaction fluxes, head, and hydraulic gradients.

[0092] Step 2: Dynamic and static parameter double-layer cyclic particle swarm correction

[0093] The outer layer optimizes static parameters such as permeability coefficient and specific yield of loose layer, sandstone, and limestone; the inner layer optimizes dynamic parameters such as precipitation infiltration coefficient, well drainage intensity, and evaporation intensity; the particle swarm optimization converges after 50 iterations in the inner layer and fine-tunes the static parameters after 30 iterations in the outer layer. After repeating the double-layer loop for 8 rounds, the residual of the objective function meets the threshold requirement, and the correction convergence is determined; after locking all parameters, the coupled algorithm is continuously executed to complete the annual hydrological projection.

[0094] Comparison of Implementation Results

[0095] The traditional unidirectional coupling + single-layer parameter correction scheme has a water level simulation error of 0.42m, while the simulation error of the two core algorithms combined in this invention is 0.07m. The residual increase of the traditional scheme after 12 months of continuous simulation is 47.1%, while that of this invention is only 4.2%, showing a significant improvement in accuracy.

[0096] Algorithm parameter value description

[0097] The convergence threshold ε1 for coupled solution is typically set to 0.001m.

[0098] The particle swarm inertia weight ω is set to 0.7, and the learning factors c1 and c2 are both set to 2.

[0099] The inner particle swarm iterations are 30 to 80 times, and the outer static parameter iterations are 20 to 50 times.

[0100] In summary, this method:

[0101] The bidirectional global coupling synchronous iterative algorithm unifies the assembly of the global matrix of surface water and groundwater equations, enables bidirectional real-time mutual feedback of interface fluxes, and synchronously updates all hydraulic variables of both surface and groundwater systems. It overcomes the distortion problem of hydraulic gradient reversal in traditional unidirectional asynchronous coupling, reduces the head drift error in long-term simulation by 78%, and accurately depicts the bidirectional recharge and transformation process of surface water and groundwater under mining disturbance.

[0102] The dynamic and static parameter dual-layer cyclic particle swarm correction algorithm distinguishes between static formation parameters and dynamic time-varying hydrological parameters, and performs layered iterative optimization. The inner layer locks the static parameters and optimizes the dynamic parameters, while the outer layer fine-tunes the static parameters based on the optimal dynamic parameters. Compared with single-layer correction, the convergence speed is improved by 60%, and the residual increase is no more than 5% after more than 10 years of continuous simulation. It is suitable for the dynamic change characteristics of hydrological parameters brought about by long-term mining.

[0103] The two algorithms work together in a coupled solution to provide simulated water level data for parameter correction, while the parameter correction optimizes the input parameters of the coupled model in reverse. The two form a closed-loop linkage. Neither algorithm alone can achieve high-precision long-term simulation results. The combination produces unexpected technical effects.

[0104] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas, characterized in that, It includes two core steps: two-way dynamic flux coupling solution of surface and groundwater and two-layer dynamic correction of coupled model parameters. It simultaneously outputs dynamic results of surface water and groundwater interaction flux, time-series water level and hydraulic gradient of the entire mine area.

2. The method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas according to claim 1, characterized in that, The solution steps for bidirectional dynamic flux coupling between surface and subsurface involve constructing a two-dimensional shallow water control equation set for surface water and a three-dimensional saturated and unsaturated seepage control equation set for groundwater. The two sets of equations are solved simultaneously using a multi-point flux approximation discrete scheme. The surface-subsurface interface water exchange flux is used as the mutual feed source and sink term for the two sets of equations to achieve bidirectional synchronous iterative solution. In a single iteration, the three types of variables—surface water depth, groundwater head, and interface exchange flux—are updated synchronously.

3. The method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas according to claim 2, characterized in that, The two-dimensional shallow water control equations for surface water use the continuous equations as the core calculation formulas, while the three-dimensional saturated and unsaturated seepage control equations for groundwater use the Richards equations. The two sets of equations are discretized and then assembled into a global coupling matrix. The global coupling matrix is ​​solved step by step using the over-relaxation iterative method. The iteration termination condition is that the difference in water head between two consecutive calculation periods is less than the convergence threshold.

4. The method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas according to claim 3, characterized in that, The global coupling matrix synchronously couples the hydraulic changes of surface water bodies and underground aquifers. The numerical values ​​and positive and negative physical meanings of the interface exchange flux are completely unified in the two sets of equations, and the dynamic switching calculation of bidirectional flux of groundwater recharge to the surface and surface water seepage into the ground is realized synchronously.

5. The method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas according to claim 1, characterized in that, The coupled model parameter dual-layer dynamic correction step adopts a dual-layer cyclic correction framework. The outer layer cyclically corrects static hydrological parameters such as medium permeability coefficient, specific yield, and riverbed sediment permeability coefficient, while the inner layer cyclically corrects dynamic time-varying hydrological parameters such as precipitation infiltration coefficient, extraction drainage intensity, and evaporation loss intensity. The correction objective function is to minimize the sum of squares of the residuals between the simulated water level and the measured water level at the monitoring point. Particle swarm optimization algorithm is used to iteratively optimize and update all parameters.

6. The method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas according to claim 5, characterized in that, The execution process of the two-layer loop correction framework is as follows: first, the static parameters are fixed to complete the optimization of the inner dynamic parameters. After saving the optimal combination of dynamic parameters, the outer loop is entered to fine-tune the static parameters. After a single two-layer loop is completed, the bidirectional dynamic flux coupling solution is re-executed. The two-layer loop iteration is repeated until the residual of the objective function is lower than the preset correction threshold.

7. The method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas according to claim 6, characterized in that, The inner-layer particle swarm optimization process uses each dynamic time-varying hydrological parameter as the optimization variable and the fixed value of the outer-layer static formation parameter as the boundary constraint. The particle velocity is updated generation by generation and the parameter combination is updated synchronously to minimize the water level residual objective function.

8. The method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas according to claim 7, characterized in that, The outer loop is based on the optimal dynamic parameters after the inner loop converges, and various static hydrological parameters are slightly perturbed. After each adjustment of the static parameters, the coupled solution steps are run again to obtain a new simulated water level sequence.

9. The method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas according to claim 8, characterized in that, After a complete iteration of the double-loop loop, the objective function values ​​of the current and previous iterations are compared. If the decrease in residual is less than a set threshold, the parameter correction is considered to have converged.

10. The method for dynamic coupling monitoring and calculation of surface water and groundwater in mining areas according to claim 9, characterized in that, After correction and convergence, all static and dynamic parameters are locked, and the bidirectional dynamic flux coupling solution steps are continuously run to complete the long-term hydrological process extrapolation of the mining area.

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