Wetland pollutant treatment method based on lattice Boltzmann method and random displacement method

Abstract

The invention provides a wetland pollutant treatment method based on a lattice Boltzmann method and a random displacement method. The wetland pollutant treatment method comprises the following steps: step 1, acquiring wetland hydrometeorological data and geographic space data; 2, constructing a wetland hydrodynamic database and a wetland pollutant treatment simulation database; 3, constructing a wetland hydrodynamic simulation model; 4, simulating movement and diffusion of pollutant particles in water flow to obtain pollutant removal effects of different vegetation configuration schemes; and 5, carrying out parameter calibration on the wetland hydrodynamic simulation model, evaluating pollutant effects of different vegetation configuration schemes, and obtaining an optimal wetland design scheme. According to the method, the lattice Boltzmann method and the random displacement method are combined, the dynamic behaviors of the water flow and the pollutants in the wetland are comprehensively simulated, the influence of the vegetation density and distribution on the diffusion of the water flow and the pollutants is considered, the simulation result is more accurate, and a scientific wetland treatment basis is provided.

Description

Technical Field

[0001] The invention relates to a wetland pollutant treatment method based on a lattice Boltzmann method and a random displacement method, and belongs to the technical field of environmental engineering and water resource management. Background Art

[0002] The hydrodynamic characteristics and vegetation configuration of wetlands have a significant impact on their pollutant removal efficiency. However, the existing technology lacks a method to accurately simulate the effects of different vegetation configurations on wetland hydrodynamics and pollutant removal efficiency. Most simulation technologies have difficulty in fully considering the combined effects of vegetation density and distribution on water flow and pollutant diffusion when dealing with complex wetland environments. Existing wetland simulation methods mainly rely on simplified hydrodynamic models, which usually ignore the complex effects of vegetation on water flow. These models often lack accuracy and flexibility when dealing with multi-level and multi-density vegetation configurations, resulting in large deviations between simulation results and actual conditions. In addition, traditional monitoring and assessment methods rely on on-site sampling and laboratory analysis, which are costly, inefficient, and cannot reflect the dynamic changes of the wetland environment in real time.

[0003] As a natural water treatment system, wetlands effectively remove pollutants from water through the combined action of vegetation, matrix and microorganisms, and also affect the overall hydrodynamic characteristics and hydrological conditions. In this ecological process, submerged plants play a vital role in wetland purification, and their planting density directly affects the growth space and resource allocation of plants, which in turn affects the purification function of the entire wetland ecosystem. If the initial planting density of submerged plants is low, the risk of death of individual plants increases, and the plant community lacks stability; if the initial density is high, the plants are inhibited or even die due to competition for space and resources. In addition, it will increase construction costs and cause waste of resources. Therefore, studying the planting density of plants in wetlands is the key to constructing wetland ecosystems and is of great significance to water ecological restoration projects.

[0004] In order to overcome the limitations of the above-mentioned technologies, the present invention proposes a numerical model for simulating the transport of pollutants in wetlands, focusing on the effects of different vegetation density configurations in wetlands on flow velocity and pollutant concentration distribution. Based on the lattice Boltzmann method and the random displacement method, this model can comprehensively simulate the effects of vegetation density and its complex interactions with water flow and pollutants. By incorporating the resistance of vegetation into the model, according to the distribution of different vegetation layers, this model can accurately predict the flow velocity distribution in wetlands under different vegetation densities. Combined with the turbulent diffusion and mechanical diffusion effects caused by vegetation, the model can also effectively simulate the effects of multi-layer vegetation on pollutant transport and removal. By studying the effects of vegetation density on wetland hydrodynamics and pollutant removal, a scientific basis can be effectively provided for wetland environmental management and pollution control. Summary of the invention

[0005] The present invention aims to solve the technical problems in the prior art of lacking precision and flexibility, high cost and low efficiency when dealing with multi-level and multi-density vegetation configurations, and further proposes a wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method.

[0006] The technical solution adopted by the present invention to solve the above problems is: the present invention proposes a wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method, comprising:

[0007] Step 1: Obtain wetland hydrometeorological data and geospatial information;

[0008] Step 2: Preprocess the wetland hydrometeorological data and geospatial data, and build a wetland hydrodynamic database and a wetland pollutant treatment simulation database based on the preprocessed wetland hydrometeorological data and geospatial data;

[0009] Step 3: Based on the lattice Boltzmann method, a wetland hydrodynamic simulation model is constructed in combination with the preprocessed wetland hydrodynamic database;

[0010] Step 4: Use the random displacement method to simulate the movement and diffusion of pollutant particles in the water flow in the wetland hydrodynamic simulation model to obtain the pollutant removal effects of different vegetation configuration schemes;

[0011] Step 5: Calibrate the parameters of the wetland hydrodynamic simulation model, simulate and evaluate the pollutant effects of different vegetation configuration schemes based on the calibrated wetland hydrodynamic simulation model, and obtain the optimal wetland design scheme.

[0012] Optionally, the wetland hydrodynamic database in step 2 includes the spatial geometric structure, hydrological characteristics, and distribution and density of vegetation of the wetland, and the wetland pollutant treatment simulation database includes the movement and diffusion paths of pollutants in the water flow.

[0013] Optionally, the wetland hydrodynamic simulation model in step 3 includes: a three-dimensional grid module, a pollutant transport module, and a pollutant removal simulation module;

[0014] The three-dimensional grid module is used to simulate the water flow state of the wetland according to the wetland hydrodynamic database;

[0015] The pollutant transport module is used to simulate the movement path and diffusion process of pollutants;

[0016] The pollutant removal simulation module is used to simulate the pollutant removal effects under different vegetation configurations based on the wetland pollutant treatment simulation database.

[0017] The wetland hydrodynamic simulation model constructed by the present invention takes into account the impact of vegetation density and distribution on water flow and pollutant diffusion, making the simulation results more accurate and providing a scientific basis for wetland management.

[0018] Optionally, the steps of constructing the three-dimensional network module include:

[0019] Step 3.1: Construct a three-dimensional grid module based on the spatial geometric structure, hydrological characteristics, and distribution and density of vegetation of the wetland;

[0020] Step 3.2: Initialize the hydrodynamic properties of all grids in the three-dimensional grid module and set the corresponding resistance coefficients of vegetation density and distribution, wherein the hydrodynamic properties include but are not limited to flow velocity, pressure and density;

[0021] Step 3.3: Set the inlet and outlet boundary conditions of the wetland, and set the special boundary conditions of the corresponding grid according to the density and height of the vegetation;

[0022] Step 3.4: Based on the initialized flow velocity and density conditions, assign an initial particle distribution function f to each grid. i (x, t = 0), the initial distribution function of each grid is processed by the lattice Boltzmann method to calculate the water flow speed and direction at each time step. After each time step, the water density and the velocity vector of the water flow in each grid are calculated to update the water flow state of each grid in the wetland in real time;

[0023] Step 3.5: Adjust the water flow resistance coefficient C according to the distribution density of different vegetation D , complete the simulation of water flow conditions in the wetland.

[0024] Optionally, the step of obtaining the pollutant removal effects of different vegetation configuration schemes in step 4 includes:

[0025] Step 4.1: Set the pollutant types and initial concentration distribution of the pollutants in the wetland, where the pollutant types include but are not limited to heavy metals, nutrients and organic pollutants;

[0026] Step 4.2: Setting the initial conditions of the pollutant transport module and the pollutant removal simulation module. The initial conditions of the pollutant transport module and the pollutant removal simulation module include the physical and chemical properties of each pollutant.

[0027] Step 4.3: Use random displacement to simulate the random movement path of particles in the three-dimensional network module, and simulate the diffusion process of pollutants through the random movement path of particles;

[0028] Step 4.4: By adjusting the diffusion coefficient of pollutants, the pollutant removal effect of different vegetation configuration schemes is calculated.

[0029] Optionally, the steps for obtaining the optimal wetland design and management plan in step 5 include:

[0030] Step 5.1: Collect water quality samples and vegetation samples from the wetland site, obtain key data, and calibrate the parameters of the wetland hydrodynamic simulation model based on the key data; the key data include but are not limited to water flow velocity and pollutant concentration, and the model parameters include but are not limited to the resistance coefficient of vegetation and the diffusion coefficient of pollutants;

[0031] Step 5.2: Calculate the pollutant removal effect of different vegetation configuration schemes in the wetland hydrodynamic simulation model after parameter calibration, and use the optimization algorithm to evaluate and obtain the optimal wetland design scheme.

[0032] Optionally, the optimization algorithm used in 5.2 to evaluate and obtain the optimal wetland design solution specifically includes:

[0033] The rates of pollutant adsorption by vegetation, degradation by microorganisms and filtration by plant roots of different vegetation configuration schemes are compared, and an optimization algorithm is used to automatically search for the optimal wetland design scheme; wherein the optimization algorithm includes but is not limited to a genetic algorithm.

[0034] The beneficial effects of the present invention are:

[0035] 1. Comprehensively consider the interaction between wetland hydrodynamics and pollutant diffusion: This paper combines the lattice Boltzmann method (LBM) and the random displacement method (RWM) to comprehensively simulate the dynamic behavior of water flow and pollutants in wetlands. The model considers the impact of vegetation density and distribution on water flow and pollutant diffusion, making the simulation results more accurate and providing a scientific basis for wetland management.

[0036] 2. Dynamic adjustment and real-time update: The present invention has a real-time data update function, and can dynamically optimize the model under different environmental conditions by adjusting the simulation parameters. This feature greatly improves the accuracy of wetland hydrodynamics and pollutant removal simulation, especially in the treatment of heavy metals and nutrients.

[0037] 3. High efficiency in treating multiple pollutants: The present invention can simulate the diffusion and removal process of multiple pollutants (such as heavy metals, nutrients and organic pollutants), and significantly improve the pollutant removal efficiency of wetlands by combining adsorption, degradation and filtration mechanisms. The system can evaluate the treatment effects under different vegetation configurations and optimize the design and management of wetlands.

[0038] 4. Broad application prospects: The present invention provides a practical tool for wetland design, management and optimization. Compared with traditional methods, the present invention has significantly improved simulation technology, accuracy and pollutant treatment effect, providing a reliable technical means for environmental engineers and water resource management experts. Through this system, scientists and managers can more effectively track the migration and spread of pollutants, optimize environmental protection measures, and thus better protect wetland ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method provided by the present invention;

[0040] Figure 2 A simulation diagram of the effect of different vegetation distribution densities on water flow velocity provided by the present invention;

[0041] Figure 3 A simulation diagram of the effects of different vegetation distribution densities on pollutant diffusion and removal efficiency provided by the present invention;

[0042] Figure 4 This is a schematic diagram of the effects of different vegetation densities and wetland water flow velocities on changes in pollutant concentrations provided by the present invention. DETAILED DESCRIPTION

[0043] Combination Figure 1 To illustrate this embodiment, Figure 1 As shown, the steps of the wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method described in this embodiment include:

[0044] S1: Collect basic data;

[0045] S101: Collect data on the location, area, surrounding terrain and hydrological conditions of the wetland to understand the natural environment background of the wetland; record the types, density, distribution patterns and growth status of vegetation in the wetland to facilitate the assessment of the hydrodynamics and ecological effects of vegetation; collect meteorological information in the area where the wetland is located, such as rainfall, temperature, wind speed, etc. Then pre-process the hydrological and meteorological data and geographic spatial information obtained in the wetland environment to establish a wetland hydrodynamics and pollutant treatment simulation database.

[0046] S2: Construct a wetland hydrodynamic simulation model;

[0047] The wetland hydrodynamic simulation model includes: three-dimensional grid module, pollutant transport module and pollutant removal simulation module;

[0048] The three-dimensional grid module is used to simulate the water flow state of the wetland according to the wetland hydrodynamic database;

[0049] The pollutant transport module is used to simulate the movement path and diffusion process of pollutants;

[0050] The pollutant removal simulation module is used to simulate the pollutant removal effects under different vegetation configurations based on the wetland pollutant treatment simulation database.

[0051] This implementation method constructs a three-dimensional grid model of wetland hydrodynamics, sets boundary conditions, calculates flow fields and updates macro variables based on the lattice Boltzmann method (LBM), and adjusts the water flow resistance coefficient in combination with the physical property of vegetation density to achieve a comprehensive simulation of wetland hydrodynamics, thereby optimizing the design and management of wetlands. The specific steps include:

[0052] S201: Establishment of three-dimensional grid module and initial parameter setting: Based on the spatial geometric structure, hydrological characteristics, and basic parameters such as the distribution and density of the input wetland, a three-dimensional grid model of the wetland is constructed, and the hydrodynamic properties of all grids are initialized, including velocity u, pressure, and density ρ, etc. Among them, the physical properties such as the density and distribution of vegetation are used to set the corresponding resistance coefficient, so that the model can more accurately reflect the obstruction of vegetation to water flow in the wetland.

[0053] S202: Define the boundary conditions of the model: set boundary conditions at the entrance and exit of the wetland, including the flow velocity at the entrance and the pressure at the exit; for the grid where the vegetation is located, set special boundary conditions according to its density and height. The solid boundary is used to simulate the completely impermeable situation, while the partially permeable boundary is used to simulate the vegetation area through which the water can partially pass, so as to more realistically reflect the impact of different vegetation configurations on the water flow in the wetland.

[0054] S203: Flow field calculation: In this step, the initial particle distribution function f is assigned to each grid according to the flow velocity and density conditions preset in S201. i (x, t = 0), and perform collision processing and fluid distribution function calculation at each time step. The distribution function of each grid applies the lattice Boltzmann equation for collision processing, and calculates the water flow speed and direction at each time step to capture the instantaneous changes in the wetland flow field and provide necessary data support for the subsequent simulation of pollutant movement.

[0055] The lattice Boltzmann equation is expressed as:

[0056]

[0057] In formula (1), f i is the particle distribution function in direction i, which indicates the distribution probability of water flow particles in different directions in the wetland model; e iis the discrete velocity vector in direction i, representing the direction of movement of water flow in the grid in the wetland; Δt is the time step; τ is the relaxation time, which is related to the viscosity of the fluid and controls the diffusion rate of the water flow; is the equilibrium distribution function, which represents the distribution of water particles when they reach a local equilibrium state.

[0058] Balanced distribution function The calculation formula is:

[0059]

[0060] In formula (2), ρ is the macroscopic density, which represents the water density of each grid point in the wetland; ω i is the weight coefficient, which changes according to different directions and affects the weight of the distribution function; u is the macro velocity, which represents the velocity vector of the water flow in the wetland; e i u is the dot product of the water particle velocity and the directional velocity, which represents the change in momentum of the water flow in a certain direction.

[0061] In a complex wetland environment, different vegetation densities have a significant impact on the flow field. Therefore, at the end of each time step, the macroscopic density and velocity of each grid point in the wetland model should be calculated, and the fluid state of each grid point in the wetland should be updated in real time. The specific calculation formulas are as follows:

[0062] The calculation formula of density ρ is:

[0063]

[0064] In formula (3), ρ is the macroscopic density, which represents the water density of each grid point in the wetland; f i is the particle distribution function in direction i, representing the distribution probability of water particles in different directions.

[0065] The calculation formula of speed u is:

[0066]

[0067] In formula (4), u is the macro velocity, which represents the velocity vector of water flow in the wetland; e i is the discrete velocity vector in direction i, representing the direction of water flow in the grid.

[0068] like Figure 2 As shown, Figure 2 This is a graph showing the change in flow velocity under different vegetation densities. It can be seen that as the vegetation distribution density increases, the water flow velocity will gradually decrease.

[0069] S204 applies appropriate boundary conditions according to the different physical properties of vegetation, and adjusts the resistance coefficient C of the water flow. DRealize the simulation of the physical obstruction of vegetation to water flow and complete the simulation of the water flow state in the wetland.

[0070] The calculation formula of the vegetation resistance model is:

[0071]

[0072] In formula (5), F is the vegetation resistance; C D is the resistance coefficient, which is related to the density and characteristics of vegetation; ∥u∥ is the flow velocity mode; d p is the characteristic length of vegetation.

[0073] Through the above steps, the lattice Boltzmann method is used to synchronously iteratively solve the wetland hydrodynamics, calculate the water flow velocity and direction at each time step, provide accurate flow field data, and provide a scientific basis for wetland design and management.

[0074] S3: Use random displacement method to simulate pollutant movement and diffusion and effect evaluation;

[0075] Based on the water flow simulation in the previous step, the pollutant transport and removal simulation module of this implementation method uses the random displacement method to simulate the movement and diffusion of pollutants in the wetland, while considering the impact of the biological and physical effects of vegetation on the pollutant diffusion coefficient, simulating the pollutant removal efficiency under different vegetation configurations, and evaluating its treatment effect on heavy metals, nutrients and organic pollutants. The specific steps include:

[0076] S301: Initial condition setting: According to the pollutant types and initial concentration distribution in the wetland, the initial simulation conditions are set to provide basic data for subsequent simulations, including setting the physical and chemical properties of different pollutants (such as heavy metals, nutrients and organic pollutants).

[0077] S302: Pollutant diffusion process simulation: In this stage, the present invention uses the random displacement method (RWM) to simulate the movement and interaction of pollutant particles in the wetland water body. By simulating the movement trajectory of pollutant particles and considering the influence of water flow speed and direction, the pollutant removal effect under different vegetation density configurations is evaluated;

[0078] The equation for modeling the particle position is:

[0079]

[0080] In formula (6), x(t) is the position of the pollutant particle in the wetland at time t; u is the velocity distribution of the water flow in the wetland; Δt is the time step; D eff is the effective diffusion coefficient of pollutants; ξ is a random variable of standard normal distribution, which is used to simulate the random motion of particles.

[0081] S30201: The diffusion coefficient of pollutants can be used to determine the diffusion law of pollutants at different levels. The diffusion of pollutants in vegetation channels is more complicated than that in conventional channels. It is necessary to consider the impact of vegetation interference on pollutant diffusion. By adjusting the effective diffusion coefficient, the impact of vegetation density and distribution on pollutant diffusion can be calculated.

[0082] The effective diffusion coefficient is calculated as follows:

[0083]

[0084] In formula (7), D water is the diffusion coefficient in wetland water, which indicates the diffusion rate of pollutants without the influence of vegetation; α is the influence factor of vegetation on the diffusion coefficient, which is used to quantify the overall impact of vegetation on the diffusion of pollutants; D veg is the influence coefficient of vegetation on pollutant diffusion; β is the adjustment factor, describing the influence intensity of vegetation density; ρ veg The distribution density of vegetation in the wetland.

[0085] S30202: To further quantify the removal effect of pollutants in wetlands and evaluate the removal effect under different plant density configurations, the present invention comprehensively considers the three mechanisms of adsorption, degradation and filtration, and calculates the effect of vegetation density and microbial concentration on pollutant removal through the following formula:

[0086] R = k ads ρ veg +k deg C micro +k fil A roots (8);

[0087] In formula (8), R is the pollutant removal efficiency in the wetland; k ads is the adsorption rate constant, which indicates the rate at which pollutants are adsorbed by vegetation; k deg k is the degradation rate constant, which indicates the rate at which pollutants are degraded by microorganisms; fil is the filtration rate constant, which indicates the rate at which pollutants are filtered by plant roots; ρ veg is the distribution density of vegetation in the wetland; C micro is the microbial concentration, A roots It is the root area of ​​plants in the wetland.

[0088] like Figure 3 As shown in the figure, this embodiment compares the pollutant diffusion and removal efficiency of different vegetation distribution densities. It can be seen that when the vegetation distribution density is 15cm / m 2 The removal effect of pollutants is best when

[0089] S4: Model validation and design optimization;

[0090] S401: Model verification and design optimization: Collect water quality samples and vegetation samples from the wetland, use the data collected on site (water flow velocity, pollutant concentration) to calibrate model parameters, such as the resistance coefficient of vegetation and the diffusion coefficient of pollutants, and verify the accuracy and reliability of the model by comparing the simulation results with the actual observation data. Through simulation tests of vegetation configurations of different densities, use optimization algorithms (such as genetic algorithms) to evaluate the optimal wetland design plan to achieve the best water purification effect.

[0091] like Figure 4 As shown in the figure, this embodiment comprehensively compares the changes in pollutant concentration caused by different vegetation densities and wetland water flow velocities. It can be seen that when the vegetation distribution density is 15 cm / m 2 , and when the water flow velocity is 0.04m / s, the treatment effect on pollutant concentration is best.

[0092] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. A wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method, characterized in that: The wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method comprises the following steps: Step 1: Obtain wetland hydrometeorological data and geospatial information; Step 2: preprocessing the wetland hydrometeorological data and geospatial data, and constructing a wetland hydrodynamic database and a wetland pollutant treatment simulation database based on the preprocessed wetland hydrometeorological data and geospatial data; Step 3: Based on the lattice Boltzmann method, a wetland hydrodynamic simulation model is constructed in combination with the preprocessed wetland hydrodynamic database; Step 4: Use the random displacement method to simulate the movement and diffusion of pollutant particles in the water flow in the wetland hydrodynamic simulation model to obtain the pollutant removal effects of different vegetation configuration schemes; Step 5: Calibrate the parameters of the wetland hydrodynamic simulation model, simulate and evaluate the pollutant effects of different vegetation configuration schemes based on the calibrated wetland hydrodynamic simulation model, and obtain the optimal wetland design scheme.

2. The wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method according to claim 1 is characterized in that: The wetland hydrodynamic database in step 2 includes the spatial geometric structure, hydrological characteristics, and distribution and density of vegetation of the wetland, and the wetland pollutant treatment simulation database includes the movement and diffusion paths of pollutants in the water flow.

3. The wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method according to claim 1 is characterized in that: The wetland hydrodynamic simulation model in step 3 includes: a three-dimensional grid module, a pollutant transport module, and a pollutant removal simulation module; The three-dimensional grid module is used to simulate the water flow state of the wetland according to the wetland hydrodynamic database; The pollutant transport module is used to simulate the movement path and diffusion process of pollutants; The pollutant removal simulation module is used to simulate the pollutant removal effects under different vegetation configurations based on a wetland pollutant treatment simulation database.

4. The wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method according to claim 3 is characterized in that: The steps of constructing the three-dimensional network module include: Step 3.1: construct a three-dimensional grid module based on the spatial geometric structure, hydrological characteristics, and distribution and density of vegetation of the wetland; Step 3.2: Initialize the hydrodynamic properties of all grids in the three-dimensional grid module, and set the corresponding resistance coefficients of vegetation density and distribution, wherein the hydrodynamic properties include but are not limited to flow velocity, pressure and density; Step 3.3: Set the inlet and outlet boundary conditions of the wetland, and set the special boundary conditions of the corresponding grid according to the density and height of the vegetation; Step 3.4: Based on the initialized flow velocity and density conditions, assign an initial particle distribution function f to each grid. i (x, t = 0), the initial distribution function of each grid is processed by the lattice Boltzmann method to calculate the water flow speed and direction at each time step. After each time step, the water density and the velocity vector of the water flow in each grid are calculated to update the water flow state of each grid in the wetland in real time; Step 3.5: Adjust the water flow resistance coefficient C according to the distribution density of different vegetation D , complete the simulation of water flow conditions in the wetland.

5. The wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method according to claim 1 is characterized in that: The steps for obtaining the pollutant removal effects of different vegetation configuration schemes in step 4 include: Step 4.1: Set the pollutant types and initial concentration distribution of pollutants in the wetland, where the pollutant types include but are not limited to heavy metals, nutrients and organic pollutants; Step 4.2: setting initial conditions of the pollutant transport module and the pollutant removal simulation module, wherein the initial conditions of the pollutant transport module and the pollutant removal simulation module include the physicochemical properties of each pollutant; Step 4.3: using random displacement to simulate the random movement path of particles in the three-dimensional network module, and simulating the diffusion process of pollutants through the random movement path of particles; Step 4.4: By adjusting the diffusion coefficient of pollutants, the pollutant removal effect of different vegetation configuration schemes is calculated.

6. The wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method according to claim 1 is characterized in that: The steps for obtaining the optimal wetland design and management plan in step 5 include: Step 5.1: Collect water quality samples and vegetation samples from the wetland site to obtain key data, and calibrate the parameters of the wetland hydrodynamic simulation model based on the key data; wherein the key data include but are not limited to water flow velocity and pollutant concentration, and the model parameters include but are not limited to the resistance coefficient of vegetation and the diffusion coefficient of pollutants; Step 5.2: Calculate the pollutant removal effect of different vegetation configuration schemes in the wetland hydrodynamic simulation model after parameter calibration, and use the optimization algorithm to evaluate and obtain the optimal wetland design scheme.

7. The wetland pollutant treatment method based on the lattice Boltzmann method and the random displacement method according to claim 6 is characterized in that: The optimization algorithm used in 5.2 to evaluate and obtain the optimal wetland design solution specifically includes: The rates of pollutant adsorption by vegetation, degradation by microorganisms and filtration by plant roots of different vegetation configuration schemes are compared, and an optimization algorithm is used to automatically search for the optimal wetland design scheme; wherein the optimization algorithm includes but is not limited to a genetic algorithm.

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