CN116611274B - A visual numerical simulation method for groundwater pollution transport - Google Patents

Abstract

The embodiment of the present invention provides a visual numerical simulation method for groundwater pollution migration, which belongs to the field of data processing technology and specifically includes: obtaining groundwater pollution data in the target area; constructing a conceptual model based on the groundwater pollution data; and constructing a geological body structure based on the conceptual model. Model; construct a three-dimensional grid model based on spatial data, conceptual model and structural model; establish an attribute field model based on the three-dimensional grid model; establish a multi-solver type simulation numerical model based on the three-dimensional grid model and attribute field model, and use the simulation numerical model Carry out flow field simulation and pollutant diffusion simulation, and use the parameter correction module to modify parameters during the simulation process to build a variable parameter model; conduct simulation iteratively based on the simulation numerical model and variable parameter model, and generate analysis results and data reports . Through the solution of the present invention, the prediction efficiency, adaptability and accuracy are improved.

Description

A Visual Numerical Simulation Method for Groundwater Pollution Migration

Technical Field

The embodiments of the present invention relate to the field of data processing technology, and in particular to a method for visualizing numerical simulation of groundwater pollution migration.

Background Art

At present, with the rapid development of urbanization and the continuous increase in industrial production, groundwater pollution has received more and more attention. In order to better understand and control groundwater pollution, groundwater pollution visualization simulation technology is used, that is, the groundwater pollution situation and solute transport law are vividly presented through three-dimensional visualization technology. Groundwater pollution visualization simulation technology combines groundwater flow with three-dimensional visualization of groundwater pollution model, and combines the actual collected hydrogeological data with the calculation program to construct a complete groundwater flow and pollution transmission model. Through this simulation technology, the flow and pollution degree of groundwater can be observed in real time, so as to better predict and control the spread of groundwater pollution. Groundwater pollution visualization simulation technology requires the use of certain modeling methods and algorithms. The existing technology has cumbersome data input, and the existing technology lacks a hydrogeological basic data management system, which makes it impossible to save data in a centralized manner, causing inconvenience for later research, resulting in low data processing efficiency and low prediction accuracy.

It can be seen that there is an urgent need for a visual numerical simulation method for groundwater pollution migration with high processing efficiency, adaptability and accuracy.

Summary of the invention

In view of this, an embodiment of the present invention provides a method for visualizing numerical simulation of groundwater pollution migration, which at least partially solves the problems of poor prediction efficiency, adaptability and accuracy in the prior art.

The embodiment of the present invention provides a method for predicting efficiency, adaptability and accuracy, including:

Step 1, obtaining groundwater pollution data in the target area;

Step 2, construct a conceptual model based on groundwater pollution data;

The step 2 specifically includes:

Based on the hydrogeological plan and profile in the groundwater pollution data, the simulation scope is framed and the conceptual model is established on the basis of the subsequent construction of the three-dimensional model of the site;

Step 3, constructing a geological body structure model based on the conceptual model;

The step 3 specifically includes:

Based on the conceptual model, the geological structure model is established by combining the observation well distribution table, drilling lithology data, and geological profiles;

Step 4, constructing a three-dimensional grid model based on the spatial data, the conceptual model and the structural model;

The step 4 specifically includes:

Based on spatial data, conceptual model and structural model, the three-dimensional space is divided into unit bodies using a three-dimensional meshing algorithm, and all unit body collections constitute a three-dimensional mesh model;

Step 5, establishing an attribute field model based on the three-dimensional grid model;

The step 5 specifically includes:

An interpolation method is used to assign initial attributes to each grid cut from the three-dimensional grid model to form an attribute field model, wherein the initial attributes include initial water level, initial pollutant concentration, water storage rate, delay factor, effective porosity, diffusion coefficient, permeability coefficient and boundary conditions;

Step 6, establishing a multi-solver type simulation numerical model based on the three-dimensional grid model and the attribute field model, using the simulation numerical model to perform flow field simulation and pollutant diffusion simulation, and using the parameter correction module to perform parameter correction during the simulation process to construct a variable parameter model;

The step 6 specifically includes:

Based on the three-dimensional grid model and attribute field model, combined with the influencing factors of groundwater flow and solute transport, the finite difference method is used to establish the mass conservation equation and motion equation at the nodes and units, and a simulation numerical model using multiple solver types is established to perform flow field simulation and pollutant diffusion simulation. In the simulation process, the parameter correction model is used to correct the parameters and construct a variable parameter model.

Step 7, iteratively perform simulation based on the simulation numerical model and the variable parameter model, and generate analysis results and data reports.

According to a specific implementation of an embodiment of the present invention, the step of dividing the three-dimensional space into unit cells using a three-dimensional mesh generation algorithm includes:

Create a bounding box, divide the cube elements within the bounding box, and determine the function values of the eight vertices of each small cube, where the function values of the vertices are The scalar value of the ternary function The spatial position relationship between the identification point and the implicit surface defined by the ternary function is:

;

The corresponding implicit function expression is selected, and during the segmentation process, the implicit function value of the cubic element between every two stratigraphic layers is extracted according to the isosurface function as its stratigraphic identifier to form a unit body.

According to a specific implementation of the embodiment of the present invention, the expression of the initial attribute is:

in, Represents a grid cell The interpolation function at the center point, It represents the attribute value at the center point of the unit. The interpolation function adopts the inverse distance weighted method with the maximum search radius and the maximum number of search points as constraints.

According to a specific implementation of an embodiment of the present invention, the solver types include a conjugate gradient solver, a bistable conjugate gradient solver and a multi-grid solver.

According to a specific implementation of the embodiment of the present invention, the expression of the simulation numerical model is:

in, represents the number of finite difference volume element nodes, represents the voxel index subscript, express Moment The water head value of each node;

coefficient For water head The head change rate is expressed as:

in, for The permeability coefficient of the direction, is a set of three-dimensional directions, For the current The estimated water head value of the node is solved by this formula The rate of change in direction is the total head change rate of the node;

coefficient For water head The seepage term is expressed as:

Among them, the subscript represents the two-dimensional index of the node in the equation, is the water storage capacity, For the time stage, For the current Estimated hydraulic head at the node;

coefficient is the source-sink term, expressed as:

in, is the fluid source and sink term;

The seepage coupling equation and convection-diffusion coupling equation of the variable parameter model are expressed as:

in, is the permeability coefficient, is the source-sink term, For water head, is the effective porosity, is the blocking factor, is the diffusion coefficient, is the source-sink term, is the density of the adsorbate, For various chemical reactions, is the adsorption rate constant, is the kinetic adsorption rate coefficient, is the mass of the compound adsorbed per unit mass of the adsorbate, is the solute concentration.

The processing efficiency, adaptability and accuracy scheme in the embodiment of the present invention includes: step 1, obtaining groundwater pollution data of the target area; step 2, constructing a conceptual model based on the groundwater pollution data; step 3, constructing a geological body structure model based on the conceptual model; step 4, constructing a three-dimensional grid model based on the spatial data, the conceptual model and the structure model; step 5, establishing an attribute field model based on the three-dimensional grid model; step 6, establishing a multi-solver type simulation numerical model based on the three-dimensional grid model and the attribute field model, using the simulation numerical model to perform flow field simulation and pollutant diffusion simulation, and using the parameter correction module to perform parameter correction during the simulation process to construct a variable parameter model; step 7, iteratively performing simulation simulation based on the simulation numerical model and the variable parameter model, and generating analysis results and data reports.

The beneficial effects of the embodiments of the present invention are as follows: through the scheme of the present invention, data of different periods can be viewed and analyzed by setting and managing data during the modeling process, the model can be viewed and modified, different simulation parameters and solvers can be used for pollution migration under different circumstances, and a parameter feedback module can be established to update the parameter list in real time, thereby improving the efficiency and accuracy of numerical simulation of groundwater pollution; the data and parameters of groundwater pollution monitoring, collection and simulation are integrated and visualized, the simulation process links can be intuitively understood, and the comprehensive management and evaluation and prediction capabilities of site pollution can be improved.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

FIG1 is a schematic diagram of a flow chart of a method for visualizing numerical simulation of groundwater pollution migration provided by an embodiment of the present invention;

FIG2 is a schematic diagram of a specific implementation process of a method for visualizing numerical simulation of groundwater pollution migration provided by an embodiment of the present invention;

FIG3 is a schematic diagram of a conceptual model of a contaminated site established in an application example of a groundwater contamination simulation method provided by an embodiment of the present invention;

FIG4 is a schematic diagram of a geological body structure model established in an application example of a groundwater pollution simulation method provided by an embodiment of the present invention;

FIG5 is a schematic diagram of a three-dimensional grid model obtained by gridding in an application example of a groundwater pollution simulation method provided by an embodiment of the present invention;

6 is a schematic diagram of the attribute parameter assignment and numerical simulation solution process in an application example of a groundwater pollution simulation method provided by an embodiment of the present invention;

7 is a schematic diagram showing a three-dimensional display of a structure diagram and results in an integrated system in an application example of a groundwater pollution simulation method provided by an embodiment of the present invention.

DETAILED DESCRIPTION

The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and/or function described herein is merely illustrative. Based on the present invention, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and/or practice the method. In addition, other structures and/or functionalities other than one or more of the aspects described herein can be used to implement this device and/or practice this method.

It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention. The drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described may be practiced without these specific details.

An embodiment of the present invention provides a method for visualizing the numerical simulation of groundwater pollution migration, which can be applied to the analysis process of groundwater pollutant migration rules.

Referring to FIG1 , a flow chart of a method for visualizing the migration of groundwater pollution provided by an embodiment of the present invention is shown. As shown in FIG1 and FIG2 , the method mainly includes the following steps:

Step 1, obtaining groundwater pollution data in the target area;

In the specific implementation, various data collection methods are used to obtain relevant data such as groundwater pollution sources, groundwater pollutant content, groundwater flow rate, etc., and multiple data sources are preprocessed for the input of simulation data. The collection methods include site monitoring and sample experiments. The collected data are analyzed and preprocessed, and organized into multiple data sets according to data types and characteristics for subsequent processing and analysis. Design a variety of data structure input programs that support source data, including standard formats and custom formats, to ensure the integrity of data reading.

For example, obtain geological plan maps, geological profile maps, and observation well distribution maps: Taking a heavy metal contaminated site as an example, use a detailed site investigation report as the map source, and mark the distribution of observation well coordinates on the plan map and profile map.

Furthermore, the survey report was analyzed, and the information data obtained were classified, organized and standardized. Specifically, the spatial data was standardized in the form of coordinates plus stratigraphic marks and entered into data files, and the non-spatial data was stored in a sequential table format as a general text or CSV table file. A general index query table was established for both types of data to facilitate subsequent indexing.

Step 2, construct a conceptual model based on groundwater pollution data;

Furthermore, the step 2 specifically includes:

According to the hydrogeological plan and profile in the groundwater pollution data, the simulation scope is framed and the conceptual model is established on the basis of the subsequent construction of the three-dimensional model of the site.

During the specific implementation, the construction of the conceptual model requires defining the modeling objectives, including the spatial coordinate system and parameter units, groundwater type, solute adsorption type, reaction type, etc.; determining the spatial and temporal scope of the model and the basic model scale based on the collected and organized geological, hydrological, engineering, and environmental data and information; and preliminarily establishing a global element system that displays the distribution of rock formations, groundwater flow fields, and diffusion and migration trends of pollution sources, including boundary generalization and internal structure generalization.

For example, according to the site plan overview and the hydrogeological plan and profile in the investigation report, the approximate simulation range is framed on the basis of the subsequent construction of the site three-dimensional model, and the site conceptual model is established, as shown in Figure 3. In this example, as shown in Figure 3, the soil properties from the surface to the bottom are roughly divided into miscellaneous fill, silty clay, gravel, strongly weathered mudstone and moderately weathered mudstone. The groundwater layer is located in the third gravel layer below the surface. According to the characteristics of the groundwater flow field, the boundary range and the relative position of the pollution source and the diffusion and migration process in the soil and groundwater are roughly determined.

Step 3, constructing a geological body structure model based on the conceptual model;

Based on the above embodiment, step 3 specifically includes:

Based on the conceptual model, the geological structure model is established by combining the observation well distribution table, drilling lithology data and geological profile.

In specific implementation, based on the aforementioned conceptual model, the geological body structure model is established in combination with the collected borehole distribution table, borehole lithology data, geological profile, etc. The model includes a hierarchical structure and a vector model of a physical object, wherein the hierarchical structure is used to describe the spatial distribution law of the strata, and the vector model of the physical object is used to describe the physical form and vector attributes of the spatial elements. Based on the known stratum information and lithology data, the boundary part of the stratigraphic surface at the junction of all upper and lower strata in each vertical direction is judged to belong to, and a more reasonable structural model is constructed. As shown in FIG4, the geological body structure model in this example.

Furthermore, for the vertical structure of the structural model, the hierarchical structure is generally represented by stratigraphic sequences, each of which includes its number, name and aquifer label. Layer, The spatial range of the stratum on the plane is , the thickness in the vertical direction is , then the hierarchical structure of the stratum can be expressed as:

And satisfy:

.

Step 4, constructing a three-dimensional grid model based on the spatial data, the conceptual model and the structural model;

Based on the above embodiment, step 4 specifically includes:

Based on spatial data, conceptual model and structural model, the three-dimensional space is divided into unit cells using a three-dimensional meshing algorithm, and all unit cell collections constitute a three-dimensional mesh model.

Furthermore, the step of dividing the three-dimensional space into unit cells using a three-dimensional meshing algorithm includes:

Create a bounding box, divide the cube elements within the bounding box, and determine the function values of the eight vertices of each small cube, where the function values of the vertices are The scalar value of the ternary function The spatial position relationship between the identification point and the implicit surface defined by the ternary function is:

;

The corresponding implicit function expression is selected, and during the segmentation process, the implicit function value of the cubic element between every two stratigraphic layers is extracted according to the isosurface function as its stratigraphic identifier to form a unit body.

In the specific implementation, in this example, encryption points are set on the model boundary and structured grid division is performed. It is further explained that during the division process, each obtained grid element needs to be assigned a stratum label, which is achieved with the help of implicit functions. The three-dimensional model grid division includes structured grids and unstructured grids.

Structured meshing is based on the input site concept model and structural information to establish a bounding box space model, and the space model is cut regularly. The three directions follow a certain spatial step size ( ) controls the coordinates of the 3D points. Each 3D point can be abstracted as a cube (length, width and height are ), using regular grid data tables to store model information, including spatial geographic location and node information.

Unstructured grid subdivision performs spatial irregular cutting of the three-dimensional model according to the input site conceptual model and structural information, that is, dividing the three-dimensional model into a collection of irregular voxels. Each irregular voxel represents the spatial information of the voxel area. Data interaction between voxels is achieved through common corner points. Irregular grid data files are used to store model spatial information, including three-dimensional points (irregular voxel corner point coordinates) and three-dimensional bodies (irregular voxel corner point serial numbers).

The embodiment of the present invention takes structured mesh generation as an example. Specifically, a bounding box is first established, and cubic elements are divided within the bounding box. For each small cube, the function values of its eight vertices are determined. These vertex functions The scalar value of the ternary function The spatial position relationship between the identification point and the implicit surface defined by the ternary function (interior, surface, exterior):

Further, according to the above method, a suitable implicit function expression is selected, and during the segmentation process, the implicit function value of the cubic element between each two stratigraphic layers is extracted according to the isosurface function as its stratigraphic identification. In this embodiment, the stratigraphic identification is 3, which is marked as an aquifer.

Furthermore, in the present invention, the three-dimensional grid model can be established by specifying the side length of the structured grid or the number of grids in the three-dimensional direction to obtain the simulation accuracy that meets the requirements. In this embodiment, the subdivision plane resolution is selected as 10m and the vertical resolution is selected as 0.5m. FIG5 shows the established three-dimensional grid model.

Step 5, establishing an attribute field model based on the three-dimensional grid model;

Furthermore, the step 5 specifically includes:

An interpolation method is used to assign initial attributes to each grid cut from the three-dimensional grid model to form an attribute field model, where the initial attributes include initial water level, initial pollutant concentration, water storage rate, delay factor, effective porosity, diffusion coefficient, permeability and boundary conditions.

Furthermore, the expression of the initial attribute is

in, Represents a grid cell The interpolation function at the center point, It represents the attribute value at the center point of the unit. The interpolation function adopts the inverse distance weighted method with the maximum search radius and the maximum number of search points as constraints.

In a specific implementation, the initial properties include initial water level, initial pollutant concentration, water storage rate, delay factor, effective porosity, diffusion coefficient, permeability coefficient and boundary conditions.

Furthermore, due to the limited number of observation wells, it is impossible to obtain all the attribute values of the 10m high-precision grid. In this example, the interpolation method is used to extract the parameter value for each voxel:

Furthermore, for a certain attribute item, let the attribute value be , it can be described in a discrete way:

in, Represents a grid cell The interpolation function at the center point, represents the property value at the center of the unit. Similarly, the permeability , Porosity , groundwater level Hydrological indicators such as NH4+ and NH4+ can also be described using discrete methods.

Furthermore, The interpolation function uses the inverse distance weighted method (IDW) with the maximum search radius and the maximum number of search points For the restriction conditions:

in, is the attribute value of the i-th point, is the distance between the i-th point and the current point to be interpolated, is the spatial coordinate of the current point to be interpolated, is the spatial coordinate of the i-th interpolation point, is the inverse distance weight of the i-th interpolation point.

Furthermore, after the above steps, a three-dimensional grid model with formation labels, attribute values and initial conditions is established, and the model can be used as an initial iterative input source for numerical simulation.

At the same time, boundary conditions are divided into Dirichlet boundary conditions and Neumann boundary conditions according to their types. The setting of boundary conditions is derived from the actual site detection data and is divided into seepage field boundary conditions and concentration field boundary conditions.

Assume that the boundary of the simulation area is , then the boundary type parameter can be expressed as:

in is a set of regions that specify traffic boundaries, is the set of regions with specified head boundaries, is the set of regions that affect water level changes through isolation and poroelastic effects. Overflow area collection.

Furthermore, the above four types of boundary conditions can be specified according to the actual site.

Step 6, establishing a multi-solver type simulation numerical model based on the three-dimensional grid model and the attribute field model, using the simulation numerical model to perform flow field simulation and pollutant diffusion simulation, and using the parameter correction module to perform parameter correction during the simulation process to construct a variable parameter model;

Based on the above embodiment, step 6 specifically includes:

Based on the three-dimensional grid model and attribute field model, combined with the influencing factors of groundwater flow and solute migration, the finite difference method is used to establish the mass conservation equation and motion equation at the nodes and elements, and a simulation numerical model using multiple solver types is established to carry out flow field simulation and pollutant diffusion simulation. In the simulation process, the parameter correction model is used to correct the parameters and construct a variable parameter model.

Optionally, the solver types include a conjugate gradient solver, a bistable conjugate gradient solver and a multi-grid solver.

Furthermore, the expression of the simulation numerical model is:

in, represents the number of finite difference volume element nodes, represents the voxel index subscript, express Moment The water head value of each node;

coefficient For water head The head change rate is expressed as:

in, for The permeability coefficient of the direction, is a set of three-dimensional directions, For the current The estimated water head value of the node is solved by this formula The rate of change in direction is the total head change rate of the node;

coefficient For water head The seepage term is expressed as:

Among them, the subscript represents the two-dimensional index of the node in the equation, is the water storage capacity, For the time stage, For the current Estimated hydraulic head at the node;

coefficient is the source-sink term, expressed as:

in, is the fluid source and sink term;

The seepage coupling equation and convection-diffusion coupling equation of the variable parameter model are expressed as:

in, is the permeability coefficient, is the source-sink term, For water head, is the effective porosity, is the blocking factor, is the diffusion coefficient, is the source-sink term, is the density of the adsorbate, For various chemical reactions, is the adsorption rate constant, is the kinetic adsorption rate coefficient, is the mass of the compound adsorbed per unit mass of the adsorbate, is the solute concentration.

In specific implementation, as shown in Figure 6, it is a schematic diagram of the attribute parameter assignment and numerical simulation solution process in the application example according to the implementation scheme of the present invention. The finite difference simulator uses a regular grid space discretization and time approximation method to process the field model to form a large-scale sparse linear equation group. Taking the water head as an example, the solution is as follows:

set up is the solution of the Dirichlet problem of the seepage field model. According to the three-dimensional finite difference method, all water heads are taken as the value of the time order t based on the implicit difference method, then satisfy:

in, represents the water head value at the center point of the finite difference grid at time t+1, represents the water head value at the center point of the finite difference grid at time t-1, Indicates the water storage capacity per unit volume, and the water head Considering all finite difference grid nodes, the overall seepage field model can be approximated as:

in, represents the number of finite difference volume element nodes, represents the voxel index subscript, express Moment The water head value of each node;

Furthermore, the coefficient For water head The head change rate is expressed as:

in, for The permeability coefficient of the direction, is a set of three-dimensional directions, For the current The estimated water head value of the node is solved by this formula The rate of change in direction is the total head change rate of the node;

coefficient For water head The seepage term is expressed as:

Among them, the subscript represents the two-dimensional index of the node in the equation, is the water storage capacity, For the time stage, For the current Estimated hydraulic head at the node;

coefficient is the source-sink term, expressed as:

in, is the fluid source and sink term;

Furthermore, the final form of the sparse linear equations can be obtained, and the implicit solution of the equation is:

in, is the linear coefficient, for The linear system matrix of the sub-region, further, the finite difference volume element result set can be expressed as:

in, For the The hydraulic head value at the center point of the time-order finite difference grid.

As a further technical solution of the present invention, for solving the large sparse linear equations formed by the above numerical model, multiple solution types such as conjugate gradient, bistable conjugate gradient, and multi-grid can be selected for numerical calculation:

Alternatively, the conjugate gradient method can be used to solve the discretized water flow equations. The key numerical model is as follows:

For the matrix and vector , for The estimated head or pollution concentration value at time t, which satisfies the residual vector and , then the relationship is satisfied :

For the next iteration value With the initial iteration value The difference is:

;

in, For the The step size of the iteration, for The conjugate direction of the iteration continues to solve ,have;

Repeat the above steps until convergence or the maximum number of iterations is reached.

Alternatively, the stable biconjugate gradient method can be used for a resolution of The coefficient matrix of and vector , initialize the system of equations and parameters, calculate the new residual vector and the left-biased conjugate vector have:

in, is the iteration stop condition, is the relaxation coefficient, for The conjugate direction of the iteration, For The vector calculated for the conjugate direction;

Continue to calculate the new vector:

;

in, is the iteration step size, and the vector is updated using this step size and the residual vector :

Compute the iteration condition and relaxation coefficient simultaneously:

;

The solution is returned when the simulation iteration conditions are met .

Optionally, the multigrid method performs iterative solutions on multiple grids of different granularity to achieve better solution speed and accuracy. The basic mathematical equation is expressed as:

in, is the smoothing operator, is the residual vector, represents the number of iterations, Indicates The solution vector of the iteration, Represents the solution vector of the previous iteration.

Furthermore, constrain the grid to its parent grid:

in, Indicates the resolution of the parent grid, Indicates the resolution of the current grid. represents the restriction operator, represents the residual vector of the parent grid after the restriction operation, Represents the residual vector of the original grid.

As a further technical solution of the present invention, a variable parameter model is constructed by using a parameter correction module in the iterative process of numerical solution, a variable parameter data set of the parameter correction module and a model variable parameter identifier are read, and the simulation variable parameters are corrected. The updated model variable parameters participate in the simulation of the next time step, which is expressed as:

In the formula, is the parameter correction state, Indicates that parameter calibration has not been performed. To express implementation; is the model variable parameter dataset, is a unique identifier set of model variable parameters, is the total number of variable parameters of the model, is the model variable parameter matrix, A unique identifier for a model variable parameter.

Specifically, in this example, the simulation parameters are set to a simulation duration of 2 years, an iteration step of 15 days, Frenchque equilibrium adsorption as the solute adsorption type, Dissolve chemical equilibrium as the chemical reaction type, and different linear equation solvers are selected for numerical calculations for comparison. Reasonable absolute and relative error limits are set according to the iteration process.

Furthermore, the present invention establishes a variable parameter model, including a permeable reaction wall and a pumping well:

The seepage coupling equation and convection-diffusion coupling equation of the permeable reaction wall model are as follows:

in, Respectively The permeability coefficient in the direction, qs is the source and sink term, h is the hydraulic head, is the effective porosity, is the blocking factor, is the diffusion coefficient, is the source-sink term, is the density of the adsorbate, For various chemical reactions, is the adsorption rate constant, is the kinetic adsorption rate coefficient, is the mass of the compound adsorbed per unit mass of the adsorbate, and C is the solute concentration.

The seepage coupling equation and convection-diffusion coupling equation of the pumping well model are as follows:

in, is the pumping rate, Respectively The permeability coefficient in the direction, Respectively represent the unit cell in The length of the side in the direction, is the diffusion coefficient, is the mass of the compound adsorbed per unit mass of the adsorbate, and C is the solute concentration.

Furthermore, in the two variable parameter models described above:

Furthermore, in this example, a state machine management is set for the simulation solver, and the next step can be entered only when the current step is successfully completed, otherwise the simulation solution ends.

Step 7, iteratively perform simulation based on the simulation numerical model and the variable parameter model, and generate analysis results and data reports.

In specific implementation, after the above-mentioned model is constructed and the parameters are set, numerical simulation is carried out, and the simulation process and results are presented in a graphical manner through modeling technology, including static and dynamic demonstrations of pollution diffusion distribution, groundwater level, and three-dimensional terrain. According to the characteristics of pollutants and groundwater flow, the simulation of pollution diffusion is realized, and the migration path and risk of pollutants are analyzed. The visualization and analysis of the simulation process and results include scene interaction of three-dimensional models, three-view planes, vertical and horizontal sections of the model, stratum screening sections, contour lines, attribute classification display and dynamic changes.

At the same time, the simulation results are quantitatively analyzed and evaluated, that is, the accuracy test is performed on the sparse linear equations. The accuracy test scheme for the solution is to convert the sparse matrix and the solution vector The product and vector Do error check, when the vector relative error , vector absolute error , vector and relative error When all of them meet the threshold limit, the solution is considered correct.

When all the above conditions are met, Returns 0, which means the solution is correct at the current time step.

As a further technical solution of the present invention, the model is output as a data file in a grid organization form, and each attribute parameter or groundwater level and pollutant concentration result is output as a normalized or formatted data file.

The present solution will be specifically described below in conjunction with a specific embodiment. The present invention also provides a groundwater pollution visualization simulation system applied to the groundwater pollution migration visualization numerical simulation method, the structure of which is shown in FIG7 (a). By integrating data management, model management, modeling process graphics, scene visualization, and parameter setting linear flow in a simulation system, the site pollution assessment and prediction capabilities are intuitively and efficiently improved. As shown in FIG7 (b) and FIG7 (c), the simulation results of the groundwater level and pollutant concentration distribution in the simulation system are visualized. In this specific embodiment, the distribution and diffusion trend of pollutants can be clearly seen, and they have a strong correlation with the groundwater level.

The method for visualizing numerical simulation of groundwater pollution migration provided in this embodiment checks and analyzes data of different periods through setting and management of data in the modeling process, checks and modifies the model, adopts different simulation parameters and solvers for pollution migration under different circumstances, establishes a parameter feedback module, updates the parameter list in real time, and improves the efficiency and accuracy of numerical simulation of groundwater pollution; integrates and visualizes the data and parameters of groundwater pollution monitoring, collection and simulation, can intuitively understand the simulation process links, and improve the comprehensive management and evaluation and prediction capabilities of site pollution.

In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above-mentioned functions defined in the method of the embodiment of the present invention are executed.

It should be noted that the computer-readable medium of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can execute the relevant steps of the method embodiment.

Alternatively, the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device can perform the relevant steps of the method embodiment.

Computer program code for performing the operations of the present invention may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and/or flow chart, and the combination of the square boxes in the block diagram and/or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

The units involved in the embodiments of the present invention may be implemented in software or hardware.

It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof.

The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. A method for visualizing the migration of groundwater pollution, comprising: Step 1, obtaining groundwater pollution data in the target area; Step 2, construct a conceptual model based on groundwater pollution data; The step 2 specifically includes: Based on the hydrogeological plan and profile in the groundwater pollution data, the simulation scope is framed and the conceptual model is established on the basis of the subsequent construction of the three-dimensional model of the site; Step 3, constructing a geological body structure model based on the conceptual model; The step 3 specifically includes: Based on the conceptual model, the geological structure model is established by combining the observation well distribution table, drilling lithology data, and geological profiles; Step 4, constructing a three-dimensional grid model based on the spatial data, the conceptual model and the structural model; The step 4 specifically includes: Based on spatial data, conceptual model and structural model, the three-dimensional space is divided into unit bodies using a three-dimensional meshing algorithm, and all unit body collections constitute a three-dimensional mesh model; Step 5, establishing an attribute field model based on the three-dimensional grid model; The step 5 specifically includes: An interpolation method is used to assign initial attributes to each grid cut from the three-dimensional grid model to form an attribute field model, wherein the initial attributes include initial water level, initial pollutant concentration, water storage rate, delay factor, effective porosity, diffusion coefficient, permeability coefficient and boundary conditions; Step 6, establishing a multi-solver type simulation numerical model based on the three-dimensional grid model and the attribute field model, using the simulation numerical model to perform flow field simulation and pollutant diffusion simulation, and using the parameter correction module to perform parameter correction during the simulation process to construct a variable parameter model; The step 6 specifically includes: According to the three-dimensional grid model and attribute field model, combined with the influencing factors of groundwater flow and solute transport, the finite difference method is used to establish the mass conservation equation and motion equation at the nodes and units, and a simulation numerical model using multiple solver types is established to perform flow field simulation and pollutant diffusion simulation. In the simulation process, the parameter correction model is used to correct the parameters and construct a variable parameter model. The expression of the parameter correction model is: , , , In the formula, is the parameter correction state, Indicates that parameter calibration has not been performed. To express implementation; is the model variable parameter dataset, is a unique identifier set of model variable parameters, is the total number of variable parameters of the model, is the model variable parameter matrix, It is the unique identifier of the model variable parameter; The variable parameter model includes a permeable reaction wall model and a pumping well model; Step 7, iteratively perform simulation based on the simulation numerical model and the variable parameter model, and generate analysis results and data reports. 2. The method according to claim 1, characterized in that the step of dividing the three-dimensional space into unit volumes using a three-dimensional meshing algorithm comprises: Create a bounding box, divide the cube elements within the bounding box, and determine the function values of the eight vertices of each small cube, where the function values of the vertices are The scalar value of the ternary function The spatial position relationship between the identification point and the implicit surface defined by the ternary function is: ; The corresponding implicit function expression is selected, and during the segmentation process, the implicit function value of the cubic element between every two stratigraphic layers is extracted according to the isosurface function as its stratigraphic identifier to form a unit body. 3. The method according to claim 2, characterized in that the expression of the initial attribute is ; in, Represents a grid cell The interpolation function at the center point, It represents the attribute value at the center point of the unit. The interpolation function adopts the inverse distance weighted method with the maximum search radius and the maximum number of search points as constraints. 4. The method according to claim 3 is characterized in that the solver types include conjugate gradient solver, bistable conjugate gradient solver and multi-grid solver. 5. The method according to claim 4, characterized in that the expression of the simulation numerical model is in, represents the number of finite difference volume element nodes, represents the voxel index subscript, express Moment The water head value of each node; coefficient For water head The head change rate is expressed as: in, for The permeability coefficient of the direction, is a set of three-dimensional directions, For the current The estimated water head value of the node is solved by this formula The rate of change in direction is the total head change rate of the node; coefficient For water head The seepage term is expressed as: Among them, the subscript represents the two-dimensional index of the node in the equation, is the water storage capacity, For the time stage, For the current Estimated hydraulic head at the node; coefficient is the source-sink term, expressed as: in, is the fluid source and sink term; The seepage coupling equation and convection-diffusion coupling equation of the permeable reactive wall model are expressed as: in, is the permeability coefficient, is the source-sink term, For water head, is the effective porosity, is the blocking factor, is the diffusion coefficient, is the source-sink term, is the density of the adsorbate, For various chemical reactions, is the adsorption rate constant, is the kinetic adsorption rate coefficient, is the mass of the compound adsorbed per unit mass of the adsorbate, is the solute concentration; The seepage coupling equation and convection-diffusion coupling equation of the pumping well model are expressed as: , , in, is the pumping rate, , , Respectively , , The permeability coefficient in the direction, , , Respectively represent the unit cell in , , The length of the side in the direction, is the diffusion coefficient, is the mass of the compound adsorbed per unit mass of the adsorbate, is the solute concentration.