A water environment monitoring method and system

Abstract

The present invention discloses a water environment monitoring method and system, which relates to the field of environmental monitoring technology, including obtaining water quality monitoring data, hydrodynamic monitoring data and water area geographic information data of water bodies at different depths and performing preprocessing; using the preprocessed water area geographic information data to construct a water body grid through a GIS tool; mapping the preprocessed water quality monitoring data and hydrodynamic monitoring data to the water body grid to obtain the water body grid after mapping the data; combining the water body grid after mapping the data, the Navier-Stokes equation and the continuity equation to construct a dynamic hydraulic model; coupling the pollutant transport and diffusion equation with the dynamic hydraulic model to simulate the spatial distribution characteristics of pollutants, and predict the diffusion path to form a monitoring report. The present invention provides an accurate and efficient water environment monitoring method by comprehensively analyzing water quality, hydrodynamics and water area geographic information.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a water environment monitoring method and system. Background Art

[0002] Water environment monitoring has become a crucial tool for ecological protection and pollution control, but existing technologies still face numerous deficiencies in accuracy, real-time performance, and sophistication. Traditional water environment monitoring methods often rely on point sampling or periodic monitoring, which struggles to fully capture the dynamic spatial and temporal characteristics of water bodies. Furthermore, the processing and analysis of monitoring data is often limited to a single dimension, focusing solely on water quality parameters, for example, while failing to effectively integrate hydrodynamic characteristics and pollutant diffusion patterns for comprehensive analysis.

[0003] This method struggles to accurately describe the three-dimensional diffusion paths and spatial distribution characteristics of pollutants in water, thus impacting the scientific nature of pollution source location and control measures. Existing technologies lack a comprehensive system, making it difficult to meet the needs of complex water environment management. Summary of the Invention

[0004] In view of the above existing problems, the present invention proposes a solution. The present invention provides a water environment monitoring method to solve the problem that the existing technology is insufficient in comprehensive analysis of water quality, hydrodynamics, water surface meteorology and water area geographical information.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a water environment monitoring method, which includes obtaining water quality monitoring data, hydrodynamic monitoring data, and water area geographic information data of water bodies at different depths, and performing preprocessing;

[0007] Using the pre-processed water area geographic information data, water body grids are constructed using GIS tools;

[0008] Map the pre-processed water quality monitoring data and hydrodynamic monitoring data to the water body grid to obtain a complete mapped water body grid;

[0009] Combine the fully mapped water grid, Navier-Stokes equations, and continuity equations to construct a dynamic hydraulic model;

[0010] Couple the pollutant transport and diffusion equations with the dynamic hydraulic model to simulate the spatial distribution characteristics of pollutants, predict the diffusion path, and form a monitoring report.

[0011] As a preferred embodiment of the water environment monitoring method described in the present invention, the water quality monitoring data includes pollutant type and three-dimensional coordinate position information; the hydrodynamic monitoring data includes flow velocity, water depth, flow direction, water pressure and three-dimensional coordinate position information; the water area geographic information data includes water body boundary data, underwater landform data, water area and water depth; the preprocessing includes data cleaning, data calibration, time series processing and format conversion.

[0012] As a preferred solution of the water environment monitoring method of the present invention, the water body grid is constructed by using the pre-processed water area geographic information data through GIS tools. The specific steps are as follows:

[0013] Based on the pre-processed water body boundary data, the water body boundary is clipped using GIS tools to obtain the clipped two-dimensional water body boundary;

[0014] According to the clipped two-dimensional water area boundary, the irregular grid generation algorithm in the GIS tool is used to generate a two-dimensional grid that fits the boundary;

[0015] Using the pre-processed underwater topography data, the terrain resolution is optimized by resampling to obtain optimized underwater topography data;

[0016] Obtain horizontal resolution and vertical stratification rules based on water area and water depth;

[0017] Combining the horizontal resolution, vertical stratification rules and optimized underwater topography data, the two-dimensional grid that fits the boundary is expanded into a three-dimensional grid, and three-dimensional coordinates are added to obtain a water body grid with three-dimensional coordinates.

[0018] As a preferred embodiment of the water environment monitoring method of the present invention, the pre-processed water quality monitoring data and hydrodynamic monitoring data are mapped to a water body grid to obtain a complete mapped water body grid. The specific steps are as follows:

[0019] According to the three-dimensional coordinate position information of the water quality monitoring data, the pre-processed water quality monitoring data at different time points are mapped to the corresponding units of the water body grid;

[0020] According to the three-dimensional coordinate position information of the hydrodynamic monitoring data, the pre-processed hydrodynamic monitoring data at different time points are mapped to the corresponding units of the water body grid to obtain a preliminary mapped water body grid;

[0021] The Kriging interpolation method is used to use the monitoring data on the preliminary mapped water body grid with the nearest three-dimensional coordinate position information to interpolate the cells of the unmapped water body grid to obtain a complete mapped water body grid.

[0022] As a preferred embodiment of the water environment monitoring method of the present invention, a dynamic hydraulic model is constructed by combining the fully mapped water body grid, the Navier-Stokes equation and the continuity equation. The specific steps are as follows:

[0023] Using the fully mapped water grid, the velocity field, pressure field and water depth distribution of the Navier-Stokes equations are initialized;

[0024] According to the water boundary of the fully mapped water grid, set the no-slip boundary condition and free water surface condition of the Navier-Stokes equation;

[0025] The dynamic hydraulic model is obtained by numerically discretizing the Navier-Stokes equations using the finite difference method and utilizing the continuity equation as a constraint.

[0026] As a preferred solution of the water environment monitoring method of the present invention, the existing pollutant transport and diffusion equation is optimized. The specific steps are as follows:

[0027] Initialize the concentration distribution of pollutants in the water body in the pollutant transport and diffusion equation based on the pre-processed water quality monitoring data;

[0028] Initialize the boundary conditions of the pollutant transport and diffusion equation to no flux conditions at the water body boundary;

[0029] The finite difference method is used to discretize the space and time of the pollutant transport and diffusion equation, and the discrete form of the pollutant transport and diffusion equation is obtained, which is expressed as:

[0030]

[0031] Among them, A is the convection term value of the pollutant in the pollutant algebraic equation, D is the diffusion term value in the pollutant algebraic equation, u is the velocity component of the fluid in the x direction, v is the velocity component of the fluid in the y direction, w is the velocity component of the fluid in the z direction, n is the index of the time step, i is the index of the grid in the x direction, j is the index of the grid in the y direction, and k is the index of the grid in the z direction. is the pollutant concentration at the grid point (i+1,j,k) at time step n, is the pollutant concentration at the grid point (i-1, j, k) at time step n, is the pollutant concentration at the grid point (i, j+1, k) at time step n, is the pollutant concentration at the grid point (i, j-1, k) at time step n, is the pollutant concentration at the grid point (i, j, k+1) at time step n, is the pollutant concentration at the grid point (i, j, k-1) at time step n, Δx is the spatial step size of the spatial grid in the x direction, Δy is the spatial step size of the spatial grid in the y direction, and Δz is the spatial step size of the spatial grid in the z direction. is the pollutant concentration at the grid point (i, j, k) at time step n+1, is the pollutant concentration at the grid point (i, j, k) at time step n, Δt is the time step, and S is the source and sink value.

[0032] As a preferred solution of the water environment monitoring method of the present invention, the following steps are used to couple the pollutant transport and diffusion equation with the dynamic hydraulic model to simulate the spatial distribution characteristics of pollutants and predict the diffusion path:

[0033] Use SIMPLE algorithm to calculate the velocity field and water depth distribution of the dynamic hydraulic model;

[0034] Using the calculated velocity field and water depth distribution, the convection and diffusion terms of pollutants in the discrete form of the pollutant algebraic equation are calculated to obtain the change in pollutant concentration in the grid cell;

[0035] According to the change of pollutant concentration in the grid cells, the three-dimensional distribution of pollutants is updated to obtain the spatial distribution characteristics of pollutants;

[0036] According to the changes in pollutant concentrations in grid cells, the propagation direction and speed of pollutants are analyzed, and the diffusion path and monitoring report of pollutants are generated by combining flow velocity and direction.

[0037] In a second aspect, the present invention provides a water environment monitoring system, comprising a data acquisition module, a grid construction module, a data mapping module, a model construction module and a diffusion simulation module;

[0038] The data acquisition module is used to acquire water quality monitoring data, hydrodynamic monitoring data and water area geographic information data of water bodies at different depths and perform preprocessing;

[0039] The grid construction module is used to construct a water body grid using the pre-processed water body geographic information data through GIS tools;

[0040] The data mapping module is used to map the pre-processed water quality monitoring data and hydrodynamic monitoring data to the water body grid to obtain a complete mapped water body grid;

[0041] The model building module is used to build a dynamic hydraulic model by combining the fully mapped water body grid, Navier-Stokes equations and continuity equations;

[0042] The diffusion simulation module is used to couple the pollutant transport and diffusion equation with the dynamic hydraulic model, simulate the spatial distribution characteristics of pollutants, predict the diffusion path, and form a monitoring report.

[0043] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the water environment monitoring method described in the first aspect of the present invention is implemented.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the water environment monitoring method described in the first aspect of the present invention is implemented.

[0045] The beneficial effects of the present invention are as follows: through comprehensive analysis of water quality, hydrodynamics, and water area geographic information, an accurate and efficient water environment monitoring method is provided. By using GIS tools to construct a three-dimensional water body grid and combining multi-source monitoring data, the spatial structure and dynamic characteristics of the water body are fully reflected, significantly improving the accuracy and efficiency of data integration. By coupling the dynamic hydraulic model with the pollutant transport and diffusion equation, the three-dimensional spatial distribution and diffusion path of pollutants can be accurately simulated, providing a scientific basis for pollution diffusion prediction. The Kriging interpolation method is used to supplement data in unmonitored areas, improving the coverage and accuracy of monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 This is a flow chart of the water environment monitoring method in Example 1.

[0048] Figure 2 This is a flowchart for obtaining a completely mapped water body grid in Example 1. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0052] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a water environment monitoring method, comprising the following steps:

[0053] S1. Acquire water quality monitoring data, hydrodynamic monitoring data and water area geographic information data of water bodies at different depths and perform preprocessing, including the following steps:

[0054] Water quality monitoring data includes pollutant types and three-dimensional coordinate location information. Specifically, intelligent buoys are used to obtain pollutant types (such as heavy metals and organic pollutants) and concentrations. It should be noted that real-time monitoring can be combined with fixed monitoring stations, unmanned vessels, or buoy systems. Detailed pollutant data can also be obtained through laboratory sample analysis.

[0055] Hydrodynamic monitoring data includes flow velocity, water depth, flow direction, water pressure, and three-dimensional coordinate position information. Specifically, data such as water velocity, flow direction, water depth, and water pressure are acquired using equipment such as ADCPs (Acoustic Doppler Current Profilers) and underwater pressure sensors. The corresponding three-dimensional coordinate position information is acquired using a positioning module installed on the monitoring equipment. It should be noted that monitoring can be performed periodically or continuously, depending on the needs of hydrodynamic changes.

[0056] Water surface meteorological monitoring data includes wind speed, wind direction, precipitation, and two-dimensional coordinate location information. Specifically, weather stations, anemometers, and rain gauges are used to collect real-time meteorological data such as wind speed, wind direction, and precipitation above the water surface. It should be noted that in addition to on-site equipment, satellite remote sensing data or meteorological data provided by regional weather stations may also be used.

[0057] Water body geographic information data includes water body boundary data, underwater topography data, water area, and water depth. Specifically, water body boundary data is extracted using remote sensing imagery (such as satellite imagery and drone aerial photography). Underwater topography data is acquired using multibeam sonar or depth sounders. Water area and depth information is obtained from existing geographic information databases.

[0058] Preprocessing includes data cleaning, data calibration, time series processing and format conversion. Specifically, check the outliers, missing values ​​and noise data in the data, and use statistical methods (such as mean substitution, interpolation) or machine learning algorithms (such as K-nearest neighbor filling) to fill in the missing data. Perform baseline adjustments on the collected data and compare them with standard reference values ​​to eliminate equipment errors. Corrections can be made through laboratory calibration or comparison with historical data. Time alignment and interpolation are performed on data collected at different times, and the timestamps are unified to construct continuous time series data. Convert data in different formats (such as CSV, JSON, remote sensing image files) into a unified standard format to facilitate subsequent analysis and processing. It should be noted that this lays the foundation for subsequent dynamic modeling and ensures the accuracy, consistency and availability of the data.

[0059] S2. Using the pre-processed water area geographic information data, construct a water body grid through GIS tools, including the following steps:

[0060] Based on the preprocessed water boundary data, use GIS tools (geographic information tools) to clip the water boundary to obtain a clipped 2D water boundary. Specifically, use a GIS tool (such as QGIS (Open Source Geographic Information System)) to load the water boundary data. Clip the water boundary based on the study area, removing irrelevant areas (such as land areas or other non-target water bodies). Use vector clipping tools to clip the 2D boundary of the target water area, ensuring that the boundary data matches the actual water shape.

[0061] Based on the clipped 2D water boundary, use an irregular grid generation algorithm within a GIS tool to generate a 2D grid that fits the boundary. Specifically, select an irregular grid generation algorithm within the GIS tool (such as one based on the Delaunay triangulation algorithm or the Voronoi polygon generation algorithm). Using the clipped 2D water boundary as a constraint, generate a 2D grid that fits the water boundary.

[0062] Using the preprocessed underwater topography data, we optimize the terrain resolution through resampling to obtain optimized underwater topography data. Specifically, we load the underwater topography data using a GIS tool. We optimize the terrain resolution through a resampling algorithm and ensure that the optimized terrain data is spatially aligned with the 2D water grid (e.g., consistent with the projected coordinate system).

[0063] Based on the water area and water depth, the horizontal resolution and vertical stratification rules are obtained. Specifically, the resolution of the horizontal grid of the water body is set according to the water area and research requirements, for example, the water area is divided into several regular or irregular units (the grid size can be determined based on the uniform division algorithm of the area). The vertical stratification rule is to set a larger stratification thickness in deep water areas and a smaller stratification thickness in shallow water areas based on the water depth and research requirements. It should be noted that it is necessary to ensure that the stratification rules match the accuracy requirements of the hydrodynamic model.

[0064] Combining horizontal resolution, vertical layering rules, and optimized underwater topography data, the two-dimensional grid that fits the boundary is expanded into a three-dimensional grid, and three-dimensional coordinates are added to obtain a water body grid with three-dimensional coordinates. Specifically, based on the horizontal resolution and vertical layering rules, the two-dimensional grid is layered vertically to generate three-dimensional grid cells. The height of each vertical layered cell can be calculated using the underwater topography data and water depth information. Three-dimensional coordinate position information is also attached to each three-dimensional grid cell. 3D coordinate data is generated using GIS tools or scripts. It should be noted that what is obtained is a water body grid with three-dimensional coordinates, which is used to fully describe the three-dimensional spatial structure of the water area.

[0065] S3, mapping the pre-processed water quality monitoring data and hydrodynamic monitoring data to the water body grid, and obtaining the water body grid after mapping the data, including the following steps:

[0066] Based on the three-dimensional coordinate location information of the water quality monitoring data, the pre-processed water quality monitoring data at different time points are mapped to the corresponding cells in the water body grid. Specifically, the corresponding cells in the water body grid are found based on the three-dimensional coordinates (longitude, latitude, depth) in the water quality monitoring data (grid cells are indexed by the three-dimensional coordinates). The pollutant data (such as concentration and type) of the monitoring point are assigned to the corresponding grid cells.

[0067] Based on the three-dimensional coordinate position information of the hydrodynamic monitoring data, the preprocessed hydrodynamic monitoring data at different time points are mapped to the corresponding cells in the water body grid to obtain a preliminary and complete mapped water body grid. Specifically, the corresponding cells in the water body grid are found based on the three-dimensional coordinates (longitude, latitude, depth) in the hydrodynamic monitoring data. The data of the hydrodynamic monitoring points (such as flow velocity, flow direction, water pressure, etc.) are assigned to the corresponding grid cells.

[0068] Using the Kriging interpolation method, the monitoring data on the initial mapped water grid with the closest three-dimensional coordinate location information is interpolated for the cells of the unmapped water grid to obtain a complete mapped water grid. Specifically, the Kriging interpolation method interpolates the unmapped grid cells based on the three-dimensional coordinates and values ​​of the mapped data points. It should be noted that Kriging interpolation is a statistical method that can accurately infer data from unknown points through spatial correlation.

[0069] S4. Combining the fully mapped water grid, Navier-Stokes equations and continuity equations, a dynamic hydraulic model is constructed, which includes the following steps:

[0070] Using the fully mapped water body grid, the velocity field, pressure field and water depth distribution of the Navier-Stokes equations are initialized. Specifically, based on the hydrodynamic monitoring data (water flow velocity and direction) mapped to the grid cells, an initial velocity value is assigned to each grid cell to form an initial velocity field. Based on the water depth and water area topography data, the initial pressure value of each grid cell is calculated using the hydrostatic pressure formula (P = ρgh, where ρ is the water density, g is the acceleration of gravity, and h is the water depth) to form an initial pressure field. The mapped water depth data is directly assigned to the grid cells to form an initial value representation of the water depth distribution. It should be noted that this step provides initial conditions for solving the Navier-Stokes equations.

[0071] Set the no-slip boundary conditions and free water surface conditions for the Navier-Stokes equations based on the water boundary of the fully mapped water mesh. Specifically, set the no-slip boundary condition at the solid boundary of the water (such as a riverbank, lakeshore, or the bottom of the water), that is, the relative velocity between the fluid and the solid boundary is zero (u = v = w = 0, where u is the velocity component of the fluid in the x-direction, v is the velocity component of the fluid in the y-direction, and w is the velocity component of the fluid in the z-direction). Set the flow velocity value of the boundary mesh cell to zero to ensure that the fluid has no relative motion at the solid boundary.

[0072] The finite difference method is used to numerically discretize the Navier-Stokes equations and the continuity equation is used as a constraint to obtain a dynamic hydraulic model. Specifically, the Navier-Stokes equations are equations that describe the velocity and pressure changes of fluid motion, and are expressed as,

[0073]

[0074] Where ρ is the fluid density, U is the velocity vector, P is the pressure, μ is the viscosity coefficient, and F is the external force.

[0075] The continuity equation (mass conservation) is used to describe the incompressibility of fluids and is expressed as,

[0076]

[0077] Where U is the velocity vector.

[0078] It should be explained that It is used to indicate that the divergence of the velocity vector field in all directions is zero, that is, the net volume flow rate of the fluid is zero.

[0079] The finite difference method is used to discretize the equation and transform the continuous partial differential equation into a discrete algebraic equation, which is convenient for computer solution.

[0080] The discretized Navier-Stokes equations can be expressed as:

[0081] u(i,j,k,n+1)=u(i,j,k,n)+Δt*F x+y+z ;

[0082] Where n is the index of the time step, i is the index of the grid in the x direction, j is the index of the grid in the y direction, k is the index of the grid in the z direction, u(i,j,k,n+1) is the velocity value of the grid unit (i,j,k) at the n+1 time step, u(i,j,k,n) is the velocity value of the grid unit (i,j,k) at the n time step, Δt is the time step, F x+y+z It is the sum of the forces in three directions acting on the flow velocity in the momentum equation of the fluid at the current grid cell (i, j, k).

[0083] S5. Couple the pollutant transport and diffusion equations with the dynamic hydraulic model to simulate the spatial distribution characteristics of pollutants, predict the diffusion path, and generate a monitoring report, including the following steps:

[0084] Initialize the pollutant concentration distribution in the water column for the pollutant transport and diffusion equation based on preprocessed water quality monitoring data. Specifically, assign the initial concentration distribution of the pollutants in the water column to each grid cell based on the preprocessed water quality monitoring data. This step provides the initial conditions for the pollutant transport and diffusion equation. It should be noted that this initial concentration distribution is the starting point for the pollutant transport and diffusion calculation, and subsequent concentration changes are cumulatively calculated based on this value.

[0085] Initialize the boundary conditions of the pollutant transport and diffusion equations to a zero-flux condition at the water boundary. Specifically, at the water boundary (such as a solid boundary or riverbank), pollutants cannot cross the boundary, so the zero-flux condition is set, meaning that the gradient of the pollutant concentration is zero.

[0086] In the pollutant transport and diffusion equation, the inflow boundary is initialized to a fixed pollutant concentration value, and the outflow boundary is initialized to a zero gradient condition. Specifically, a fixed pollutant concentration value is set at the inflow boundary (e.g., upstream), indicating that the concentration of pollutants in the inflow water is known. A zero gradient condition is set at the outflow boundary (e.g., downstream), indicating that the pollutant concentration does not change along the outflow direction. It should be noted that the initialization of boundary conditions is an important constraint in pollutant transport and diffusion simulations, ensuring that the calculations conform to actual physical conditions.

[0087] The finite difference method is used to discretize the space and time of the pollutant transport and diffusion equation, and the discrete form of the pollutant transport and diffusion equation is obtained, which is expressed as:

[0088]

[0089]

[0090] Among them, A is the convection term value of the pollutant in the pollutant algebraic equation, which is used to describe the flow changes of pollutants with the velocity field. D is the diffusion term value in the pollutant algebraic equation, which is used to describe the changes in the diffusion of pollutants due to the concentration gradient. u is the velocity component of the fluid in the x-direction, which is used to drive the convection diffusion of pollutants and determine the propagation direction and speed of pollutants. v is the velocity component of the fluid in the y-direction, which drives the convection diffusion of pollutants and determines the propagation direction and speed of pollutants. w is the velocity component of the fluid in the z-direction, which drives the convection diffusion of pollutants and determines the propagation direction and speed of pollutants. n is the index of the time step, i is the index of the grid in the x-direction, j is the index of the grid in the y-direction, and k is the index of the grid in the z-direction. is the pollutant concentration at the grid point (i+1,j,k) at time step n, is the pollutant concentration at the grid point (i-1, j, k) at time step n, is the pollutant concentration at the grid point (i, j+1, k) at time step n, is the pollutant concentration at the grid point (i, j-1, k) at time step n, is the pollutant concentration at the grid point (i, j, k+1) at time step n, is the pollutant concentration at the grid point (i, j, k-1) at time step n, Δx is the spatial step size of the spatial grid in the x direction, Δy is the spatial step size of the spatial grid in the y direction, and Δz is the spatial step size of the spatial grid in the z direction. is the pollutant concentration at the grid point (i, j, k) at time step n+1, is the pollutant concentration at grid point (i, j, k) at time step n, Δt is the time step length, and S is the source / sink term, which describes the external input or consumption of pollutants. It should be noted that the discretized equation can be directly solved numerically to calculate the pollutant concentration at each grid cell at the next time step.

[0091] The SIMPLE algorithm (semi-implicit pressure coupled equation method) is used to calculate the velocity field and water depth distribution of the dynamic hydraulic model. Specifically, the core idea of ​​the SIMPLE algorithm is to gradually correct the pressure field and velocity field in an iterative manner so that they satisfy the momentum equation and the continuity equation. Furthermore, first, based on the initial pressure field (or the pressure field at the previous moment), the momentum equation is used to predict the velocity field; secondly, the pressure correction equation is derived from the continuity equation to calculate the pressure correction value; then, the pressure field is updated through the pressure correction formula; finally, the velocity field is corrected using the corrected pressure field. It should be noted that the hydrodynamic model is the core driving force for the transport and diffusion of pollutants, providing flow velocity and direction.

[0092] The velocity field and water depth distribution obtained by calculation are used to calculate the convection and diffusion terms of pollutants in the discrete form of the pollutant algebraic equation to obtain the change of pollutant concentration in the grid unit. Specifically, the velocity field and water depth distribution calculated by the hydrodynamic model are used to calculate the convection and diffusion terms of the pollutant transport and diffusion equation. At each time step, the pollutant concentration C of the grid unit is updated. i,j,k .

[0093] According to the change of pollutant concentration in the grid unit, the three-dimensional distribution of pollutants is updated to obtain the spatial distribution characteristics of pollutants. Specifically, according to the pollutant concentration C of each grid unit, i,j,k , updates the three-dimensional distribution of pollutants at each time step, helping to analyze the impact range of pollution sources.

[0094] Based on the changes in pollutant concentrations within the grid cells, the pollutant propagation direction and speed are analyzed. Combined with flow velocity and direction, a pollutant diffusion path and monitoring report are generated. Specifically, the pollutant propagation direction is determined, the pollutant diffusion speed is calculated, and a pollutant diffusion path map and monitoring report are generated.

[0095] This embodiment also provides a water environment monitoring system, comprising: a data acquisition module, a grid construction module, a data mapping module, a model construction module and a diffusion simulation module;

[0096] The data acquisition module is used to obtain water quality monitoring data, hydrodynamic monitoring data and water area geographic information data of water bodies at different depths and perform preprocessing;

[0097] The grid construction module is used to construct water body grids using GIS tools using pre-processed water body geographic information data;

[0098] The data mapping module is used to map the pre-processed water quality monitoring data and hydrodynamic monitoring data to the water body grid to obtain a complete mapped water body grid;

[0099] The model building module is used to build a dynamic hydraulic model by combining a fully mapped water grid, the Navier-Stokes equations, and the continuity equations.

[0100] The diffusion simulation module is used to couple the pollutant transport and diffusion equations with the dynamic hydraulic model, simulate the spatial distribution characteristics of pollutants, predict the diffusion path, and form a monitoring report.

[0101] This embodiment also provides a computer device suitable for the water environment monitoring method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the water environment monitoring method proposed in the above embodiment.

[0102] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device 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 comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0103] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the water environment monitoring method proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0104] In summary, the present invention provides an accurate and efficient water environment monitoring method by comprehensively analyzing water quality, hydrodynamics, and water area geographic information. By using GIS tools to construct a three-dimensional water body grid and combining multi-source monitoring data, the spatial structure and dynamic characteristics of the water body are fully reflected, significantly improving the accuracy and efficiency of data integration. By coupling the dynamic hydraulic model with the pollutant transport and diffusion equation, the three-dimensional spatial distribution and diffusion path of pollutants can be accurately simulated, providing a scientific basis for pollution diffusion prediction. The Kriging interpolation method is used to supplement data in unmonitored areas, improving the coverage and accuracy of monitoring data.

[0105] Example 2, referring to Table 1, Table 2 and Table 3, is the second embodiment of the present invention. In order to further verify the technical solution of the present invention, experimental simulation data of the water environment monitoring method are provided.

[0106] To validate the effectiveness of the proposed water environment monitoring method, a river section (10 km long, 300 m wide, and with an average water depth of 4 m) was selected as the test area. The experiment involved collecting water quality, hydrodynamic, and surface meteorological data, preprocessing the data, building a grid, performing model calculations, and simulating pollutant dispersion. Based on the simulation results, recommendations for pollutant control were developed.

[0107] Fifty sampling points were deployed within the monitoring area to collect pollutant concentrations (e.g., COD, in mg / L) and their three-dimensional coordinates (x, y, z). An acoustic Doppler current meter (ADCP) was used to collect three-dimensional coordinate data for flow velocity (m / s), flow direction (°), water depth (m), and water pressure (Pa). Water body boundaries, bottom topography, water area, and depth were obtained based on remote sensing imagery and hydrological surveys.

[0108] The details are shown in Table 1 below:

[0109] Table 1 Original monitoring data

[0110]

[0111] After data cleaning, calibration, time series processing, and format conversion, a usable data set was formed. To simplify calculations, water quality monitoring data and hydrodynamic data were recorded at 1-hour intervals, and wind speed and precipitation were recorded as daily averages.

[0112] GIS tools were used to clip the water boundary, and an irregular grid generation algorithm was used to generate a 2D grid with a cell size of 50 m × 50 m. Based on preprocessed underwater topography data, the terrain resolution was optimized through resampling. A 10-layer vertical stratification rule was generated, combining water area and depth. The 2D grid was expanded to a 3D grid, forming a water body grid with 3D coordinates.

[0113] Water quality and hydrodynamic monitoring data are mapped to water body grids. Kriging interpolation is used to complete data for unmapped grid cells. Time series data are mapped to water body grids at each time point to form dynamic mapping data.

[0114] The details are shown in Table 2 below:

[0115] Table 2 Completely mapped water body grid

[0116]

[0117] No-slip boundary conditions and free water surface conditions were set, and the finite difference method was used to discretize the equations and calculate the velocity field and water depth distribution. The pollutant transport and diffusion equations were coupled to simulate the spatial distribution characteristics and diffusion paths of pollutants. The pollutant concentration at the inlet boundary was fixed at 20 mg / L, and the outflow boundary was set to zero gradient. The initial concentration field was initialized based on the sampled data.

[0118] The details are shown in Table 3 below:

[0119] Table 3 Pollutant diffusion simulation results

[0120]

[0121] It can be seen from the above experimental data that the water environment monitoring method can efficiently and accurately simulate the spatial distribution characteristics and diffusion paths of pollutants.

[0122] Comparing the original data with the mapped data (Tables 1 and 2), the mapped data uses kriging interpolation to complete the pollutant concentration and hydrodynamic data for unsampled areas, significantly improving data integrity. This shows a smooth spatial transition in pollutant concentration (for example, the transition between sampling points 1 and 2 is 15.5 mg / L), avoiding the discontinuity that can occur when the original data are used directly.

[0123] Comparison of simulated and mapped data (Tables 2 and 3) shows that pollutant concentrations gradually decrease over time. For example, the COD concentration in grid 1 dropped from 15.5 mg / L to 14.8 mg / L. The simulation results reflect pollutant diffusion paths and trends (such as dynamic changes in flow velocity and direction), providing a scientific basis for pollutant control.

[0124] By coupling a dynamic hydraulic model with pollutant transport and diffusion equations, this method can accurately predict the diffusion behavior of pollutants in complex water bodies. The three-dimensional water body grid constructed using GIS tools has a high resolution and can fully account for water boundaries and terrain characteristics. Compared with existing technologies, this method has significant advantages in data preprocessing, grid construction, and simulation accuracy. In particular, the Kriging interpolation method achieves higher prediction accuracy in interpolation calculations in areas without sampled data. The simulation results clearly define the key areas and diffusion directions of pollutants, providing data support for the precise governance of high-pollution risk areas.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A water environment monitoring method, characterized in that: include, Obtain water quality monitoring data, hydrodynamic monitoring data and water area geographic information data of water bodies at different depths and perform preprocessing; Using the pre-processed water area geographic information data, water body grids are constructed using GIS tools; Map the pre-processed water quality monitoring data and hydrodynamic monitoring data to the water body grid to obtain a complete mapped water body grid; Combine the fully mapped water grid, Navier-Stokes equations, and continuity equations to construct a dynamic hydraulic model; Couple pollutant transport and diffusion equations with dynamic hydraulic models to simulate the spatial distribution characteristics of pollutants, predict diffusion paths, and generate monitoring reports; The specific steps for transforming the existing pollutant transport and diffusion equation are as follows: Initialize the concentration distribution of pollutants in the water body in the pollutant transport and diffusion equation based on the pre-processed water quality monitoring data; Initialize the boundary conditions of the pollutant transport and diffusion equation to no flux conditions at the water body boundary; The finite difference method is used to discretize the space and time of the pollutant transport and diffusion equation, and the discrete form of the pollutant transport and diffusion equation is obtained, which is expressed as: Among them, A is the convection term value of the pollutant in the pollutant algebraic equation, D is the diffusion term value in the pollutant algebraic equation, u is the velocity component of the fluid in the x direction, v is the velocity component of the fluid in the y direction, w is the velocity component of the fluid in the z direction, n is the index of the time step, i is the index of the grid in the x direction, j is the index of the grid in the y direction, and k is the index of the grid in the z direction. is the pollutant concentration at the grid point (i+1,j,k) at time step n, is the pollutant concentration at the grid point (i-1, j, k) at time step n, is the pollutant concentration at the grid point (i, j+1, k) at time step n, is the pollutant concentration at the grid point (i, j-1, k) at time step n, is the pollutant concentration at the grid point (i, j, k+1) at time step n, is the pollutant concentration at the grid point (i, j, k-1) at time step n, Δx is the spatial step size of the spatial grid in the x direction, Δy is the spatial step size of the spatial grid in the y direction, and Δz is the spatial step size of the spatial grid in the z direction. is the pollutant concentration at the grid point (i, j, k) at time step n+1, is the pollutant concentration at the grid point (i, j, k) at time step n, Δt is the time step, and S is the source and sink value.

2. The water environment monitoring method according to claim 1, wherein: The water quality monitoring data includes pollutant type and three-dimensional coordinate location information; the hydrodynamic monitoring data includes flow velocity, water depth, flow direction, water pressure and three-dimensional coordinate location information; the water area geographic information data includes water body boundary data, underwater landform data, water area and water depth; the preprocessing includes data cleaning, data calibration, time series processing and format conversion.

3. The water environment monitoring method according to claim 2, wherein: Using the pre-processed water area geographic information data, the water body grid is constructed through GIS tools. The specific steps are as follows: Based on the pre-processed water body boundary data, the water body boundary is clipped using GIS tools to obtain the clipped two-dimensional water body boundary; According to the clipped two-dimensional water area boundary, the irregular grid generation algorithm in the GIS tool is used to generate a two-dimensional grid that fits the boundary; Using the pre-processed underwater topography data, the terrain resolution is optimized by resampling to obtain optimized underwater topography data; Obtain horizontal resolution and vertical stratification rules based on water area and water depth; Combining the horizontal resolution, vertical stratification rules and optimized underwater topography data, the two-dimensional grid that fits the boundary is expanded into a three-dimensional grid, and three-dimensional coordinates are added to obtain a water body grid with three-dimensional coordinates.

4. The water environment monitoring method according to claim 3, wherein: Map the pre-processed water quality monitoring data and hydrodynamic monitoring data to the water body grid to obtain a complete mapped water body grid. The specific steps are as follows: According to the three-dimensional coordinate position information of the water quality monitoring data, the pre-processed water quality monitoring data at different time points are mapped to the corresponding units of the water body grid; According to the three-dimensional coordinate position information of the hydrodynamic monitoring data, the pre-processed hydrodynamic monitoring data at different time points are mapped to the corresponding units of the water body grid to obtain a preliminary mapped water body grid; The Kriging interpolation method is used to use the monitoring data on the preliminary mapped water body grid with the nearest three-dimensional coordinate position information to interpolate the cells of the unmapped water body grid to obtain a complete mapped water body grid.

5. The water environment monitoring method according to claim 4, wherein: Combining the fully mapped water grid, Navier-Stokes equations and continuity equations, a dynamic hydraulic model is constructed. The specific steps are as follows: Using the fully mapped water grid, the velocity field, pressure field and water depth distribution of the Navier-Stokes equations are initialized; According to the water boundary of the fully mapped water grid, set the no-slip boundary condition and free water surface condition of the Navier-Stokes equation; The dynamic hydraulic model is obtained by numerically discretizing the Navier-Stokes equations using the finite difference method and utilizing the continuity equation as a constraint.

6. The water environment monitoring method according to claim 5, wherein: Couple the pollutant transport and diffusion equations with the dynamic hydraulic model to simulate the spatial distribution characteristics of pollutants, predict the diffusion path, and form a monitoring report. The specific steps are as follows: Use SIMPLE algorithm to calculate the velocity field and water depth distribution of the dynamic hydraulic model; Using the calculated velocity field and water depth distribution, the convection and diffusion terms of pollutants in the discrete form of the pollutant algebraic equation are calculated to obtain the change in pollutant concentration in the grid cell; According to the change of pollutant concentration in the grid cells, the three-dimensional distribution of pollutants is updated to obtain the spatial distribution characteristics of pollutants; According to the changes in pollutant concentrations in grid cells, the propagation direction and speed of pollutants are analyzed, and the diffusion path and monitoring report of pollutants are generated by combining flow velocity and direction.

7. A water environment monitoring system based on the water environment monitoring method according to any one of claims 1 to 6, characterized in that: Including data acquisition module, grid construction module, data mapping module, model construction module and diffusion simulation module; The data acquisition module is used to acquire water quality monitoring data, hydrodynamic monitoring data and water area geographic information data of water bodies at different depths and perform preprocessing; The grid construction module is used to construct a water body grid using the pre-processed water body geographic information data through GIS tools; The data mapping module is used to map the pre-processed water quality monitoring data and hydrodynamic monitoring data to the water body grid to obtain a complete mapped water body grid; The model building module is used to build a dynamic hydraulic model by combining the fully mapped water body grid, Navier-Stokes equations and continuity equations; The diffusion simulation module is used to couple the pollutant transport and diffusion equation with the dynamic hydraulic model, simulate the spatial distribution characteristics of pollutants, predict the diffusion path, and form a monitoring report.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the water environment monitoring method according to any one of claims 1 to 6 are implemented.

9. 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 water environment monitoring method according to any one of claims 1 to 6 are implemented.

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