A method and system for simulating monitoring of an aquatic environment for evaluating the impact of pollutants

Abstract

The present application relates to a method, device, computer equipment, storage medium and computer program product for monitoring aquatic environment simulation for evaluating the impact of pollutants. The method comprises: obtaining environmental parameters and dividing the range of a fine model based on the environmental parameters; constructing a simplified model based on environmental parameters within the range of a non-fine model; constructing a fine model based on environmental parameters within the range of a fine model; outputting and displaying corresponding simulation results based on the simplified model and the fine model. The present application adopts the above method, through the multi-scale coupling of simplified models and fine models, which can not only ensure the rapid simulation of the diffusion trend of pollutants in a large range, but also use high-precision models for detailed analysis in local key areas, thereby improving the efficiency and accuracy of the overall simulation.

Description

Technical Field

[0001] The present application relates to the technical field of environmental assessment, and in particular to an aquatic environment simulation monitoring method and system for assessing the impact of pollutants. Background Art

[0002] The aquatic environment is a complex ecosystem, and pollutants can affect plants and animals in the water through various pathways. Deterioration of water quality will not only endanger the health of aquatic organisms, but also lead to serious ecological problems such as reduced species diversity and disruption of the food chain.

[0003] The existing technology usually collects water samples or water quality parameters by deploying sensors, performs modeling analysis on the degree of water pollution based on the water samples or water quality parameters, and performs corresponding treatment operations on the water according to the modeling analysis results.

[0004] In the existing technology, most models are constructed under the assumption that the water body is uniform and stable. However, in the real environment, the complexity of water flow, nonlinear diffusion of pollutants and biological factors may cause deviations between the model and the actual situation. Summary of the invention

[0005] Based on this, it is necessary to provide an aquatic environment simulation monitoring method, device, computer equipment, computer-readable storage medium and computer program product for evaluating the impact of pollutants with increased prediction accuracy in response to the above technical problems.

[0006] In a first aspect, the present application provides an aquatic environment simulation monitoring system for evaluating the impact of pollutants.

[0007] The system includes:

[0008] Environmental parameter acquisition module: obtain environmental parameters through online monitoring equipment and divide the scope of the fine model based on the environmental parameters, the environmental parameters at least include environmental parameters, water quality index data, biological data and functional data, monitor various water quality parameters in the water body in real time through online monitoring equipment, monitor water quality indexes in real time through water quality sensors, monitor biological data in the water body in real time through biological monitoring equipment, and obtain the dynamic situation of the river and the functional data of the river through the river function recording log system;

[0009] Environmental parameter processing module: constructs a simplified model based on environmental parameters, water quality index data, biological data and functional data within the scope of the non-fine model; constructs a fine model based on environmental parameters, water quality index data, biological data and functional data within the scope of the fine model;

[0010] Simulation result display module: outputs and displays the corresponding simulation results based on the simplified model and the refined model.

[0011] In one embodiment, the environmental parameter processing module constructs a fine model based on the environmental parameters within the fine model range, including:

[0012] Obtaining simplified simulation results based on the simplified model, wherein the simplified simulation results include environmental parameters generated after simulation by the simplified model;

[0013] Based on the simplified simulation results and the environmental parameters within the refined model range, refined models are constructed separately and coupled to generate refined simulation results.

[0014] In one embodiment, the environmental parameter processing module constructs a simplified model based on environmental parameters within a non-fine model range, including:

[0015] The basic equation of the two-dimensional shallow water equation (SWE) is:

[0016]

[0017]

[0018] Among them, h is the water depth, v is the horizontal velocity vector, t is time, g is the gravitational acceleration, S is the source term (including wind stress, friction, etc.), and the functional data of the above rivers are obtained through the river function recording log system;

[0019] The simplified pollutant diffusion equation specifically includes:

[0020]

[0021] Among them, C is the pollutant concentration, v is the water flow velocity, D is the diffusion coefficient, t is the time, and the pollutant concentration is obtained by real-time monitoring of water quality indicators through water quality sensors.

[0022] In one embodiment, the environmental parameter processing module constructs a fine model based on the environmental parameters within the fine model range, including:

[0023] The three-dimensional hydrodynamic model (EFDC / MIKE 3) includes the mass conservation equation and momentum conservation equation (Navier-Stokes equation):

[0024] The mass conservation equation specifically includes:

[0025]

[0026]

[0027] Among them, v1 is the three-dimensional velocity field, ρ is the fluid density, p is the pressure, v is the viscosity coefficient, and F is the external force (such as gravity, wind stress, etc.). The three-dimensional velocity field, fluid density, pressure and viscosity coefficient are obtained through the river function recording log system;

[0028] The pollutant convection-diffusion equation specifically includes:

[0029]

[0030] Among them, C is the pollutant concentration, v is the water flow velocity, D is the diffusion coefficient, and t is the time. The pollutant concentration is obtained through the water quality sensor, and the water flow velocity and diffusion coefficient are obtained through the river function recording log system.

[0031] In one of the embodiments, the boundary transition optimization module: determines a refined outer boundary based on the refined model;

[0032] Comparing the refined outer boundary with the intersection area corresponding to the simplified model;

[0033] If the refined outer boundary overlaps with the intersection area corresponding to the simplified model, the overlapping area is transitioned based on the interpolation method.

[0034] In one of the embodiments, the environmental parameter optimization module: obtains the concentrations of different pollutants based on the environmental parameters and performs normalization processing;

[0035] Constructing an exposure matrix based on regions and pollutant types, wherein the exposure matrix is ​​used to represent the concentration distribution of different pollutants in different regions;

[0036] Construct synergistic effect models based on pollutant characteristics;

[0037] Based on the synergistic effect model, the spatial distribution of multiple pollutants and the prediction results of synergistic effects are output.

[0038] In a second aspect, the present application also provides a method for simulating monitoring of an aquatic environment for evaluating the impact of pollutants. The method comprises:

[0039] Acquire environmental parameters through online monitoring equipment and divide the scope of the fine model based on the environmental parameters, wherein the environmental parameters at least include environmental parameters, water quality index data, biological data and functional data, monitor various water quality parameters in the water body in real time through online monitoring equipment, monitor water quality index in real time through water quality sensors, monitor biological data in the water body in real time through biological monitoring equipment, and acquire the dynamic situation of the river and the functional data of the river through a river function recording log system;

[0040] Construct a simplified model based on environmental parameters, water quality index data, biological data and functional data within the scope of the non-fine model; construct a fine model based on environmental parameters, water quality index data, biological data and functional data within the scope of the fine model;

[0041] The corresponding simulation results are output and displayed based on the simplified model and the refined model.

[0042] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0043] Environmental parameter acquisition module: obtain environmental parameters through online monitoring equipment and divide the scope of the fine model based on the environmental parameters, the environmental parameters at least include environmental parameters, water quality index data, biological data and functional data, monitor various water quality parameters in the water body in real time through online monitoring equipment, monitor water quality indexes in real time through water quality sensors, monitor biological data in the water body in real time through biological monitoring equipment, and obtain the dynamic situation of the river and the functional data of the river through the river function recording log system;

[0044] Environmental parameter processing module: constructs a simplified model based on environmental parameters, water quality index data, biological data and functional data within the scope of the non-fine model; constructs a fine model based on environmental parameters, water quality index data, biological data and functional data within the scope of the fine model;

[0045] Simulation result display module: outputs and displays the corresponding simulation results based on the simplified model and the refined model.

[0046] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0047] Environmental parameter acquisition module: obtain environmental parameters through online monitoring equipment and divide the scope of the fine model based on the environmental parameters, the environmental parameters at least include environmental parameters, water quality index data, biological data and functional data, monitor various water quality parameters in the water body in real time through online monitoring equipment, monitor water quality indexes in real time through water quality sensors, monitor biological data in the water body in real time through biological monitoring equipment, and obtain the dynamic situation of the river and the functional data of the river through the river function recording log system;

[0048] Environmental parameter processing module: constructs a simplified model based on environmental parameters, water quality index data, biological data and functional data within the scope of the non-fine model; constructs a fine model based on environmental parameters, water quality index data, biological data and functional data within the scope of the fine model;

[0049] Simulation result display module: outputs and displays the corresponding simulation results based on the simplified model and the refined model.

[0050] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0051] Environmental parameter acquisition module: obtain environmental parameters through online monitoring equipment and divide the scope of the fine model based on the environmental parameters, the environmental parameters at least include environmental parameters, water quality index data, biological data and functional data, monitor various water quality parameters in the water body in real time through online monitoring equipment, monitor water quality indexes in real time through water quality sensors, monitor biological data in the water body in real time through biological monitoring equipment, and obtain the dynamic situation of the river and the functional data of the river through the river function recording log system;

[0052] Environmental parameter processing module: constructs a simplified model based on environmental parameters, water quality index data, biological data and functional data within the scope of the non-fine model; constructs a fine model based on environmental parameters, water quality index data, biological data and functional data within the scope of the fine model;

[0053] Simulation result display module: outputs and displays the corresponding simulation results based on the simplified model and the refined model.

[0054] The above-mentioned aquatic environment simulation monitoring method, device, computer equipment, storage medium and computer program product for evaluating the impact of pollutants obtain environmental parameters and divide the range of the fine model based on the environmental parameters; construct a simplified model based on environmental parameters within the range of the non-fine model; construct a fine model based on environmental parameters within the range of the fine model; output and display corresponding simulation results based on the simplified model and the fine model. This application adopts the above-mentioned method, through the multi-scale coupling of simplified models and fine models, which can not only ensure the rapid simulation of the diffusion trend of pollutants in a large range, but also use high-precision models for detailed analysis in local key areas, thereby improving the efficiency and accuracy of the overall simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A diagram of an application environment of an aquatic environment simulation monitoring method for evaluating the impact of pollutants in one embodiment;

[0056] Figure 2 A flowchart of a method for simulating monitoring of an aquatic environment for evaluating the impact of pollutants in one embodiment;

[0057] Figure 3 A structural block diagram of an aquatic environment simulation monitoring device for evaluating the impact of pollutants in one embodiment;

[0058] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The aquatic environment simulation monitoring method for evaluating the impact of pollutants provided in the embodiments of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or it can be placed on the cloud or other network servers. Among them, the terminal can be but not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0061] In one embodiment, Figure 2 As shown, in this embodiment, the method includes the following steps:

[0062] Step 202: Acquire environmental parameters and divide the range of the fine model based on the environmental parameters.

[0063] Among them, a multi-scale, multi-dimensional comprehensive model is constructed by combining hydrodynamic models, pollutant diffusion models and ecological models; refined models (such as three-dimensional hydrodynamic models) are used in local areas, and simplified models are used on a large scale to improve the adaptability of the model.

[0064] According to the characteristics of the problem and the distribution range of pollutants, the spatial scope of model simulation is divided, including:

[0065] Large-scale areas: These are usually areas where the diffusion of pollutants has a wide range and low mobility. Simplified models can be used for preliminary simulation to determine the approximate distribution of pollutants.

[0066] Local scope: Areas with high pollutant concentrations or complex hydrodynamics, such as reservoirs and harbors, require detailed simulation using refined models.

[0067] Step 204 : constructing a simplified model based on environmental parameters within the range of the non-fine model.

[0068] Among them, the simplified model usually chooses a two-dimensional hydrodynamic model or a simple mass conservation model in a large range; the two-dimensional hydrodynamic model or the simple mass conservation model has high computational efficiency and is suitable for rough simulation of a larger area;

[0069] Two-dimensional shallow water equations (SWE): suitable for simulating the flow of water surface and the horizontal diffusion of pollutants.

[0070] Step 206: construct a fine model based on environmental parameters within the fine model range.

[0071] Among them, the three-dimensional hydrodynamic model used at a local scale can more accurately capture the vertical changes of water flow and is suitable for simulating complex flow, mixing and sedimentation processes in water bodies.

[0072] Three-dimensional hydrodynamic models (such as EFDC, MIKE 3, Delft3D): Models simulate the three-dimensional flow of water bodies, taking into account vertical layers and complex boundary conditions.

[0073] Convection-Dispersion Equation (ADE): used to describe the convection and diffusion behavior of pollutants in three-dimensional space.

[0074] Step 208: Output and display corresponding simulation results based on the simplified model and the refined model.

[0075] After the model is integrated and run, the simulation results of the simplified model and the refined model are output respectively, and the results are displayed through the GIS system or other visualization tools. For local areas, three-dimensional visualization tools are used to display the complex distribution of pollutants and hydrodynamic processes; for large areas, the overall trend of pollutant diffusion is displayed.

[0076] Environmental parameters include: gravitational acceleration, source terms include wind stress and friction, external forces include wind stress and gravity, and time; water quality index data include: pollutant concentration; biological data include: median lethality; functional data include: water depth, horizontal flow velocity vector, three-dimensional velocity field, fluid density, pressure, viscosity coefficient, diffusion coefficient; environmental parameters are tested through corresponding sensors, water quality indicators are obtained through analysis by water sampling, biological data are also obtained through testing after water sampling, and functional data are obtained through observation data in the observation station.

[0077] In the above-mentioned aquatic environment simulation monitoring method for evaluating the impact of pollutants, parameters of simplified models and refined models are calibrated according to actual monitoring data to ensure the accuracy of the models. When the local model is coupled with the large-scale model, the boundary conditions are adjusted to ensure a smooth transition between models. Through the multi-scale coupling of simplified models and refined models, it is possible to ensure rapid simulation of the diffusion trend of pollutants over a large area, and to use high-precision models for detailed analysis in local key areas, thereby improving the efficiency and accuracy of the overall simulation.

[0078] In one embodiment, the boundary problem between the refined model and the simplified model also needs to be considered. The specific boundary processing includes:

[0079] A simplified simulation result is obtained based on the simplified model, wherein the simplified simulation result includes environmental parameters generated after the simplified model simulation; and a refined model is constructed based on the simplified simulation result and environmental parameters within the refined model range, and the refined simulation result is generated by coupling.

[0080] In this embodiment, boundary conditions are provided for the local fine model, and parameters such as pollutant concentration and flow rate simulated by the simplified model are used as input boundary conditions of the local model to ensure coupling between models of different scales, thereby further improving the accuracy of the simulation results.

[0081] In one embodiment, the specific steps of constructing the simplified model include:

[0082] Construct basic equations and pollutant diffusion equations; discretize pollutant diffusion equations based on environmental parameters; update environmental parameters based on time step calculations and output simplified simulation results.

[0083] Among them, the basic equation of the two-dimensional shallow water equation (SWE) is:

[0084]

[0085]

[0086] Where h is the water depth, v is the horizontal velocity vector, t is time, g is the gravitational acceleration, S is the source term (including wind stress, friction, etc.), is the divergence calculation;

[0087] The finite element method is used to discretize the system of equations, including:

[0088] In the finite element method, we first need to select the appropriate element type and shape function. The commonly used two-dimensional element is the triangle element, and its shape function can be expressed as a linear interpolation function.

[0089] Assume that we use linear shape functions (applicable to low-order finite element methods) to represent the water depth and flow velocity of each unit. On each unit, the shape function is:

[0090] h(x,y)=N 1 h 1 +N 2 h 2 +N 3 h 3 ;

[0091] Among them, N i is the shape function, h i is the water depth value of the unit node, and the velocity component is similarly expressed as:

[0092] u(x,y)=N 1 u 1+N 2 u 2 +N 3 u 3 ;

[0093] Among them, N i is the shape function, u i is the velocity vector at the node.

[0094] In order to solve the problem using the finite element method, the equations need to be converted from the strong form to the weak form. We can get the weak form by taking the inner product of the mass conservation equation and the momentum equation with the test function.

[0095] The mass conservation equation is multiplied by a test function v (optionally any function), and then integrated over space:

[0096]

[0097] Where Ω is the calculation area, h is the water depth value of the unit node, and u is the velocity vector at the node. For the divergence calculation, t is the time step. For the momentum conservation equation, multiply it by a test function w and then integrate it:

[0098]

[0099] Among them, the test function w is an arbitrary function of the velocity field, Ω is the calculation area, h is the water depth value of the unit node, and u is the velocity vector at the node. is the divergence calculation, t is the time step, p is the pressure of the fluid, g is the gravitational acceleration, and the integration operation is performed in each unit.

[0100] Through weak form integration, we finally get a linear system, including physical quantities such as water depth and flow velocity at the node. Through shape function interpolation, we get the stiffness matrix and load vector of each unit. Assembling the stiffness matrices and load vectors of all units into a global matrix, we get the discretized linear equation system.

[0101] According to the discretized linear equations, standard linear algebra methods (such as Gaussian elimination method, conjugate gradient method, etc.) are used to solve the equations to obtain the distribution of water depth h and flow velocity u.

[0102] The simplified pollutant diffusion equation specifically includes:

[0103]

[0104] Among them, C is the pollutant concentration, v is the water flow velocity, D is the diffusion coefficient, and t is the time;

[0105] Specifically, it includes: initializing the water depth h and pollutant concentration C; discretizing the equation using the finite difference method or the finite element method; solving the new water depth h and flow velocity v at each time step, then updating the pollutant concentration C, and outputting the boundary conditions of the local area corresponding to the simplified model.

[0106] In this embodiment, by simplifying the construction of the model, it is possible to ensure that the diffusion trend of pollutants can be quickly simulated over a large area.

[0107] In one embodiment, the specific steps of constructing the fine model include:

[0108] Construct the mass conservation equation and momentum conservation equation; discretize the momentum conservation equation based on environmental parameters; output detailed simulation results based on simplified simulation results and time step.

[0109] The three-dimensional hydrodynamic model (EFDC / MIKE 3) includes the mass conservation equation and momentum conservation equation (Navier-Stokes equation):

[0110] The mass conservation equation and momentum conservation equation (Navier-Stokes equation) specifically include:

[0111]

[0112]

[0113] Among them, v1 is the three-dimensional velocity field, ρ is the fluid density, p is the pressure, v is the viscosity coefficient, and F is the external force (such as gravity, wind stress, etc.);

[0114] First, select a suitable finite element space. For three-dimensional flow problems, three-dimensional elements (such as tetrahedral elements) are commonly used. The choice of shape function is similar to that of two-dimensional problems. You can choose linear interpolation functions or use higher-order interpolation functions.

[0115] Convert the three-dimensional Navier-Stokes equations to weak form. For the momentum equation, multiply by a test function v (same spatial dimension as the velocity field) and integrate over the entire region:

[0116]

[0117] Among them, the first term is the time derivative term, the second term is the convection term, the third term is the diffusion term including viscosity, and the fourth term is the pressure term.

[0118] The coupling of pressure and velocity fields is handled using pressure-velocity separation (such as the Stokes problem) or pressure-velocity correction method (such as PISO).

[0119] The obtained linear equations are solved by numerical solution methods (such as direct solution or iterative method). Due to the nonlinearity of the Navier-Stokes equations, it is usually necessary to use an iterative method (such as the Newton-Raphson method) to solve the nonlinear equations.

[0120] The pollutant convection-diffusion equation specifically includes:

[0121]

[0122] Among them, C is the pollutant concentration, v is the water flow velocity, D is the diffusion coefficient, and t is the time.

[0123] In this embodiment, a high-precision model is used to perform detailed analysis in local key areas, thereby improving the accuracy of the overall simulation.

[0124] In one embodiment, there is a case where data is discontinuous or sudden at the boundary between the simplified model and the refined model. Optimization is required for the case where data is discontinuous or sudden at the boundary. The specific optimization operation includes:

[0125] Determine a fine outer boundary based on a fine model; compare the fine outer boundary with the intersection area corresponding to the simplified model; if the fine outer boundary and the intersection area corresponding to the simplified model overlap, transition the overlapping area based on an interpolation method; if the fine outer boundary and the intersection area corresponding to the simplified model do not overlap, there is no need to optimize the boundary area.

[0126] Among them, determine the transition area and draw the boundary: First, it is necessary to clarify the spatial boundary of the large-scale simplified model and the local refined model. The transition area is usually set at the junction of the two to ensure a certain overlap area for smooth transition. Define the width: Determine the width of the transition area, which is usually determined by simulation requirements and computing resources. The transition area should not be too narrow to ensure good interpolation effect. Example: Assume that the large-scale model covers the entire water area, and the local model covers a harbor area. The transition area can be set within a certain distance outside the harbor, such as within 1 km around the harbor.

[0127] Collect data in the interface area and extract data: extract simulation results of the interface area from the large-scale simplified model and the local refined model, including key parameters such as pollutant concentration, water velocity, and water depth; synchronize time steps: ensure that the data time steps of the two models in the interface area are consistent, which is convenient for subsequent interpolation processing. Example: In the transition area, extract pollutant concentration and flow velocity data for each grid point from the large-scale model, and extract corresponding data for the same grid point from the local model.

[0128] Select an appropriate interpolation method. The interpolation methods include: Linear interpolation: simple and computationally efficient, suitable for parameters with slowly changing values; Spline interpolation: provides a smoother transition, suitable for parameters with complex changes; Kriging: a statistical method suitable for data with strong spatial correlation; Weighted average: a weighted average of the data from the two models based on distance or other weight factors.

[0129] Selection basis: Data characteristics: spatial variation and smoothness of parameters; Computational resources: complex interpolation methods (such as Kriging) require large amounts of computation, and computational resource limitations must be considered; Accuracy requirements: requirements for smoothness and accuracy of data in transition areas. Example: For pollutant concentration, spline interpolation can be selected to ensure smoothness of concentration changes; for flow velocity, a weighted average method can be used to adjust weights based on distance.

[0130] Execute the interpolation process and define the interpolation range: clarify the grid points or spatial positions that need to be interpolated; apply the interpolation algorithm: perform interpolation calculations on each grid point in the transition area according to the selected interpolation method; generate transition area data: obtain parameter data such as pollutant concentration and water flow velocity after smooth transition.

[0131] It is worth mentioning that data preparation: for each grid point that needs to be interpolated, obtain the corresponding data from the large-scale model and the local model; calculate weights (if applicable): calculate the interpolation weights based on the distance from the grid point to the boundaries of the two models. For example, the closer the distance to the large-scale model boundary, the higher the weight; interpolation calculation: use the selected interpolation method to fuse the data of the large-scale model and the local model to generate new parameter values. Example: For a grid point, if the weighted average interpolation method is used, set its distance from the large-scale model boundary to d1, and its distance from the local model boundary to d2, the weight can be set to w1 = d2 / (d1+d2), w2 = d1 / (d1+d2). The pollutant concentration after interpolation C = w1*C_large+w2*C_local.

[0132] Verify the interpolation results, smoothness check: check whether the parameter changes in the transition area are smooth and without obvious mutations; consistency verification: ensure that the interpolated data in the transition area is consistent or close to the boundary data of the large-scale and local models; error analysis: evaluate the accuracy of the interpolation by comparing the interpolation results with the actual monitoring data or high-precision model results. Example: Draw a profile of the pollutant concentration along the transition area, check whether the concentration changes smoothly, and compare it with the monitoring data to ensure that the interpolation effect is consistent with the actual situation.

[0133] Integrate interpolation results into the overall model and update model input: use the interpolated transition area data as part of the overall model to ensure seamless data connection between the large-scale and local models in the interface area; iterative optimization: adjust the interpolation method or parameters based on the verification results to optimize the data quality in the transition area. Example: Input the interpolated pollutant concentration and water velocity data into the overall aquatic environment simulation system to ensure data continuity and consistency in the transition area of ​​the model.

[0134] Dynamic adjustment and real-time update, real-time monitoring data: If the system has real-time data collection capabilities, it can dynamically adjust the interpolation parameters according to the latest monitoring data to improve the adaptability of the transition area; Adaptive interpolation: Dynamically select the most suitable interpolation method or adjust the interpolation weight according to environmental changes and simulation requirements. Example: When a sudden pollution incident occurs, the interpolation weight of the transition area is adjusted in real time, so that the influence of the local refined model is quickly extended to the transition area, ensuring the timeliness and accuracy of the pollutant diffusion simulation.

[0135] Optimization and iteration, model feedback: Based on simulation results and actual monitoring data, feedback on interpolation effects, and continuous optimization of interpolation methods and parameters; multiple iterations: Through multiple iterative interpolation processes, gradually improve the data quality of the transition area and the overall accuracy of the model. Example: Re-evaluate the interpolation effect and adjust the interpolation algorithm parameters every quarter based on newly collected monitoring data to cope with environmental changes and changes in simulation needs.

[0136] In this embodiment, through the above specific steps, the interpolation technology can effectively establish a smooth transition area between the large-scale simplified model and the local refined model, ensuring the data continuity and simulation accuracy of the overall aquatic environment simulation monitoring system. The multi-scale coupling method not only improves the computational efficiency of the model, but also ensures high-precision simulation of key areas, providing reliable technical support for water environment management and pollution control.

[0137] In one embodiment, the assessment model of a single pollutant usually only considers the impact of a single factor. However, in reality, multiple pollutants often exist at the same time and may interact in a synergistic (enhanced) or antagonistic (weakened) manner, thereby producing complex effects on water bodies and ecosystems. Therefore, multiple pollutants can be assessed, including:

[0138] The concentrations of different pollutants are obtained based on environmental parameters and normalized; an exposure matrix is ​​constructed based on regions and pollutant types, and the exposure matrix is ​​used to represent the concentration distribution of different pollutants in different regions; a synergistic effect model is constructed based on pollutant characteristics; and the spatial distribution of multiple pollutants and the prediction results of the synergistic effect are output based on the synergistic effect model.

[0139] Among them, synergistic effect: when multiple pollutants coexist, the combined impact of multiple pollutants is greater than the sum of their independent impacts. For example, some heavy metals and organic pollutants may increase their toxicity to organisms under the joint action; antagonistic effect: when multiple pollutants act together, their combined impact is less than the sum of their independent impacts. For example, some pollutants may produce chemical reactions to reduce their toxicity or biological effectiveness.

[0140] It is worth mentioning that environmental parameters can specifically include: water quality parameters: such as temperature, pH, dissolved oxygen and other environmental factors; pollutant concentration: concentration data of each pollutant, including heavy metals, nutrients (such as nitrogen, phosphorus), organic pollutants (such as pesticides, petroleum hydrocarbons), etc.; biological response data: the response of organisms to pollutants, including population mortality rate, bioaccumulation factor (BAF), bioaccumulation factor (BCF), etc.

[0141] The specific processing of pollutant normalization includes: converting the concentration of each pollutant into a standardized value, usually using the effect concentration value (such as LC50) for normalization:

[0142]

[0143] Among them, C i * is the concentration of pollutant i, and LC50 is the median lethal concentration of the pollutant to a specific organism;

[0144] The exposure matrix specifically includes:

[0145]

[0146] Among them, C i (j) represents the concentration of the i-th pollutant at the j-th location;

[0147] It is worth mentioning that the commonly used synergy matrix specifically includes:

[0148] Toxic Unit Model (TUM):

[0149] Applicable to situations where multiple pollutants have cumulative toxic effects on the same type of organisms.

[0150]

[0151] The above formula is the formula for calculating toxicity units, TU i is the unit toxicity, C is the pollutant concentration, and LC50 is the median lethal concentration of the pollutant to a specific organism;

[0152]

[0153] Among them, the above formula is the formula for calculating total toxicity, TU 总 is the total toxicity of the pollutant, C is the concentration of the pollutant, and LC50 is the median lethal concentration of the pollutant to a specific organism; if TU 总 >1, indicating that the pollutant is significantly toxic.

[0154] Concentration Addition (CA):

[0155] Applicable when pollutants have similar mechanisms of action.

[0156]

[0157] Among them, the above formula is the formula for calculating toxicity units, C i * is the unit toxicity, C is the pollutant concentration, and EC50 is the half-maximum effect concentration of the pollutant;

[0158]

[0159] Among them, the above formula is the formula for calculating the total toxicity, CA 总 is the total toxicity of the pollutant, C is the pollutant concentration, and EC50 is the half-maximum effect concentration of the pollutant; if CA 总 Exceeding the threshold indicates the presence of significant contamination effects.

[0160] Response Addition (RA):

[0161] Applicable when the pollutants have different mechanisms of action.

[0162] Independent effect probability: Calculate the effect probability P of each pollutant based on the pollutant concentration i :

[0163]

[0164] Among them, P i is the effect probability of the pollutant, k is the response coefficient of the pollutant to the biological effect;

[0165] Cumulative effect: The cumulative effect of multiple pollutants is calculated using the probability summation formula:

[0166]

[0167] Among them, Ptotal represents the summary of the overall effect of pollutants on organisms, P i is the effect probability of the pollutant.

[0168] Interaction Model:

[0169] Capturing the synergistic or antagonistic effects between pollutants through machine learning or statistical models is applicable to situations where there are complex interactions between pollutants.

[0170] Define interaction terms: The interaction effects of two or more pollutants are expressed through interaction terms. For example, for two pollutants C 1 and C 2 , introducing the interaction term:

[0171]

[0172] Among them, α, β and γ are setting coefficients, C is the pollutant concentration, C 交互 is the pollutant concentration after interaction.

[0173] Use regression models: Construct multiple linear regression or nonlinear regression models to fit the relationship between pollutants and biological effects:

[0174] E=θ 0 +θ 1 C 1 +θ 2 C 2 +θ 3 C 交互 +ε;

[0175] Among them, E is the effect value, ε is the error term, C is the pollutant concentration, C 交互 is the pollutant concentration after interaction.

[0176] In this embodiment, the spatial distribution of pollutants and the prediction results of synergistic effects are displayed through GIS or other visualization tools. For example, the comprehensive toxic effects of pollutants are displayed through color gradients; and a risk assessment report of the synergistic effects of pollutants is generated based on the model output to provide support for environmental management and decision-making.

[0177] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0178] Based on the same inventive concept, the embodiment of the present application also provides an aquatic environment simulation monitoring device for evaluating the impact of pollutants for implementing the aquatic environment simulation monitoring method for evaluating the impact of pollutants involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more aquatic environment simulation monitoring devices for evaluating the impact of pollutants provided below can be referred to the limitations of the aquatic environment simulation monitoring method for evaluating the impact of pollutants above, and will not be repeated here.

[0179] In one embodiment, Figure 2 and Figure 3 As shown, an aquatic environment simulation monitoring device for evaluating the impact of pollutants is provided, including: a model range division module, a simplified model construction module, a fine model construction module and a simulation result display module, wherein:

[0180] In one embodiment, the model range division module is also used to obtain simplified simulation results based on the simplified model, wherein the simplified simulation results include environmental parameters generated after simulation by the simplified model; and to construct refined models based on the simplified simulation results and environmental parameters within the refined model range, and to couple and generate refined simulation results.

[0181] In one embodiment, the simplified model construction module is further used to construct basic equations and pollutant diffusion equations; discretize the pollutant diffusion equations based on environmental parameters; update environmental parameters based on time step calculations and output simplified simulation results.

[0182] In one embodiment, the fine model construction module is further used to construct mass conservation equations and momentum conservation equations; discretize the momentum conservation equation based on environmental parameters; and output fine simulation results based on simplified simulation results and time steps.

[0183] In one embodiment, the fine model construction module is also used to determine a fine outer boundary based on the fine model; compare the fine outer boundary with the intersection area corresponding to the simplified model; if the fine outer boundary overlaps with the intersection area corresponding to the simplified model, transition the overlapping area based on the interpolation method.

[0184] In one embodiment, the model range division module is also used to obtain the concentrations of different pollutants based on environmental parameters and perform normalization processing; construct an exposure matrix based on regions and pollutant types, and the exposure matrix is ​​used to represent the concentration distribution of different pollutants in different regions; construct a synergistic effect model based on pollutant characteristics; and output the spatial distribution of multiple pollutants and the predicted results of the synergistic effect based on the synergistic effect model.

[0185] Each module in the above-mentioned aquatic environment simulation monitoring method and device for evaluating the impact of pollutants can be implemented in whole or in part by software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0186] In one embodiment, referring to Figure 4 , provides a computer device, which may be a server, and includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for coordinated investment in a power transmission and energy storage system is implemented.

[0187] Those skilled in the art will appreciate that a block diagram of only a portion of the structure related to the solution of the present application does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0188] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0189] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0190] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0191] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0192] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processors involved in each embodiment provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., but are not limited thereto.

[0193] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0194] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A water environment simulation monitoring system for evaluating the impact of pollutants, characterized in that: The system comprises: Parameter acquisition module: obtaining the parameters through online monitoring equipment and dividing the range of the fine model based on the parameters, wherein the parameters at least include environmental parameters, water quality index data, biological data and functional data, real-time monitoring of various water quality parameters in the water body through online monitoring equipment, real-time monitoring of water quality indexes through water quality sensors, real-time monitoring of biological data in the water body through biological monitoring equipment, and obtaining the dynamic situation of the river and the functional data of the river through the river function recording log system; Parameter processing module: constructs a simplified model based on environmental parameters, water quality index data, biological data and functional data within the scope of the non-fine model; constructs a fine model based on environmental parameters, water quality index data, biological data and functional data within the scope of the fine model; Simulation result display module: outputs and displays the corresponding simulation results based on the simplified model and the refined model; Boundary transition optimization module: determines the fine outer boundary based on the fine model; Comparing the refined outer boundary with the intersection area corresponding to the simplified model; If the refined outer boundary overlaps with the intersection area corresponding to the simplified model, the overlapping area is interpolated based on the method of interpolation; Parameter optimization module: obtaining the concentration of different pollutants based on the parameters and performing normalization processing; Constructing an exposure matrix based on regions and pollutant types, wherein the exposure matrix is ​​used to represent the concentration distribution of different pollutants in different regions; Construct synergistic effect models based on pollutant characteristics; Based on the synergistic effect model, the spatial distribution of multiple pollutants and the prediction results of synergistic effects are output.

2. The aquatic environment simulation monitoring system for evaluating the impact of pollutants according to claim 1 is characterized in that: The parameter processing module constructs a fine model based on environmental parameters within the fine model range, including: Obtaining simplified simulation results based on the simplified model, wherein the simplified simulation results include environmental parameters generated after simulation by the simplified model; Based on the simplified simulation results and the environmental parameters within the refined model range, refined models are constructed separately and coupled to generate refined simulation results.

3. The aquatic environment simulation monitoring system for evaluating the impact of pollutants according to claim 2 is characterized in that: The parameter processing module constructs a simplified model based on environmental parameters, water quality index data, biological data and functional data within the non-fine model range, including: The basic equations of the two-dimensional shallow water equation are: ； Where h is the water depth, v is the horizontal velocity vector, t is time, g is the gravitational acceleration, and S is the source term; The simplified pollutant diffusion equation specifically includes: ； Among them, C is the pollutant concentration, v is the horizontal flow velocity vector, D is the diffusion coefficient, and t is the time.

4. The aquatic environment simulation monitoring system for evaluating the impact of pollutants according to claim 3 is characterized in that: The parameter processing module constructs a fine model based on environmental parameters within the fine model range, including: The three-dimensional hydrodynamic model includes the mass conservation equation and the momentum conservation equation: The mass conservation equation and momentum conservation equation specifically include: ； ； in, is the three-dimensional velocity field, is the fluid density, p is the pressure, is the viscosity coefficient, F is the external force; The pollutant convection-diffusion equation specifically includes: ； Among them, C is the pollutant concentration, v is the horizontal flow velocity vector, D is the diffusion coefficient, and t is the time.

5. A method for simulating monitoring of an aquatic environment for evaluating the impact of pollutants, characterized in that: The method is implemented based on the aquatic environment simulation monitoring system for evaluating the impact of pollutants according to any one of claims 1 to 4, and the method comprises: Acquire environmental parameters and divide the range of the fine model based on the environmental parameters; Construct simplified models based on environmental parameters within the non-refined model range; Construct a detailed model based on environmental parameters within the detailed model range; The corresponding simulation results are output and displayed based on the simplified model and the refined model.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to claim 5 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 5 are implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to claim 5 are implemented.

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