River pollution prediction method based on hydrology-hydrodynamic force-water quality coupling model

Abstract

The invention is applicable to the technical field of water pollution control, and provides a river pollution prediction method based on a hydrology-hydrodynamic force-water quality coupling model, comprising the following steps: acquiring hydrology and water quality related data of a river; establishing a one-dimensional hydrodynamic model according to the hydrology and water quality related data, and operating the one-dimensional hydrodynamic model to obtain one-dimensional dynamic change data of the river; establishing a two-dimensional hydrodynamic water quality model, performing joint solution on the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model, and simulating a pollutant migration and transformation process; and determining the water quality pollution condition of the river based on the simulation result. The water quality pollution condition of the river can be accurately simulated and predicted.

Description

Technical Field

[0001] The invention belongs to the technical field of water pollution control, and in particular relates to a river pollution prediction method based on a hydrology-hydrodynamics-water quality coupling model. Background Art

[0002] River water quality prediction is to infer the future water quality changes of river water bodies based on relevant river data, so as to provide guidance and data support for river water environment management.

[0003] The water environment of urban rivers has problems such as complex causes of pollution and difficulty in control, especially the different levels of water pollution in different areas of the river and the complex types of pollutants. The traditional method is to use manual sampling or online monitoring to judge the pollution of the river water environment. Manual sampling has a lag, and online monitoring requires the deployment of a large number of monitoring points, which greatly restricts the work of river water environment management. At present, the river water quality prediction model is an effective tool in the management of river water environment, but the complexity and variability of the actual application environment make it difficult for a single water quality model to meet comprehensive control needs. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a river pollution prediction method based on a hydrology-hydrodynamics-water quality coupling model to accurately simulate and predict the water pollution of a river.

[0005] A first aspect of an embodiment of the present invention provides a river pollution prediction method based on a hydrology-hydrodynamics-water quality coupling model, comprising:

[0006] Obtain river hydrological and water quality data;

[0007] A one-dimensional hydrodynamic model is established based on the hydrological and water quality related data, and the one-dimensional hydrodynamic model is run to obtain one-dimensional dynamic change data of the river;

[0008] Establishing a two-dimensional hydrodynamic water quality model, and jointly solving the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model to simulate the migration and transformation process of pollutants;

[0009] Based on the simulation results, the water pollution status of the river is determined.

[0010] As a possible implementation, the one-dimensional hydrodynamic model includes:

[0011]

[0012] Among them, Z is the water depth; x is the mileage; t is the time; Q is the flow rate of the water-passing section; h is the water level of the water-passing section; A is the area of ​​the water-passing section; g is the gravitational acceleration; α is the vertical velocity distribution coefficient; R is the hydraulic radius; and c is the Xie Cai coefficient.

[0013] As a possible implementation, the two-dimensional hydrodynamic and water quality model includes: a two-dimensional hydrodynamic model and a two-dimensional water quality model;

[0014] The two-dimensional hydrodynamic model is:

[0015]

[0016] Among them, U is the conservation vector; E adv , G adv are the convective flux vectors in the x and y directions respectively; E diff , G diff are the diffusion flux vectors caused by Reynolds stress in the x and y directions respectively; E dis , G dis are the diffusion flux vectors caused by the secondary flow in the x and y directions respectively; S is the source term vector;

[0017] The two-dimensional water quality model is:

[0018]

[0019] Where h is the water depth; u is the water velocity in the horizontal direction; v is the water velocity in the vertical direction; c is the vertical average concentration of the general material component; D x and D y are the diffusion coefficients of the substances in the x and y directions respectively; k is the degradation coefficient; q in and c in are the flow rate and substance concentration of the point source, respectively.

[0020] As a possible implementation method, the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model are jointly solved to simulate the pollutant migration and transformation process, including:

[0021] Establish constraints on water levels, flows, and total pollutant loads;

[0022] The "branch point water level prediction and correction method" is used to perform one-dimensional and two-dimensional longitudinal coupling, and the water level boundary conditions are iteratively corrected according to the constraints, until the net flow of the coupled boundary meets the preset calculation tolerance and the iteration is terminated.

[0023] As a possible implementation, the water level correction increment in the iterative process is:

[0024]

[0025] Where Δη is the water level correction increment; Q c is the net flow of coupled boundary; B c is the river width at the coupling boundary, h cis the water depth at the coupling boundary; α is a preset parameter; g is the gravitational acceleration.

[0026] As a possible implementation method, the water pollution status of the river is determined based on the simulation results, including:

[0027] Based on the simulation results, the point source pollution load and non-point source pollution load of the river are calculated to determine the water pollution status of the river; wherein,

[0028] The non-point source pollution load is calculated by the following formula:

[0029]

[0030] Where L is the pollutant load; λ is the watershed loss coefficient; n is the number of monitoring times or calculation periods; E i A is the pollutant emission intensity monitored for the i-th time, generally representing the pollutant emission per unit area or per unit time; i is the area of ​​the watershed monitored for the i-th time or the area of ​​the relevant calculation area; Δt is the time interval, representing the length of time for each monitoring or calculation;

[0031] The point source pollution load is calculated by the following formula:

[0032]

[0033] Where L is the pollutant load; ρ i is the pollutant mass concentration monitored for the i-th time; Q i is the i-th monitoring flow; Δ i is the i-th monitoring time period.

[0034] As a possible implementation method, the one-dimensional hydrodynamic model is established by SWMM; the two-dimensional hydrodynamic water quality model is established by Delft3D.

[0035] A second aspect of an embodiment of the present invention provides a river pollution prediction device based on a hydrology-hydrodynamics-water quality coupling model, comprising:

[0036] An acquisition module is used to obtain the hydrological and water quality related data of the river;

[0037] A first processing module is used to establish a one-dimensional hydrodynamic model according to the hydrological and water quality related data, and run the one-dimensional hydrodynamic model to obtain one-dimensional dynamic change data of the river;

[0038] The second processing module is used to establish a two-dimensional hydrodynamic water quality model, and jointly solve the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model to simulate the migration and transformation process of pollutants;

[0039] The determination module is used to determine the water pollution status of the river based on the simulation results.

[0040] A third aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method in the first aspect or any one of the implementations of the first aspect are implemented.

[0041] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method in the above-mentioned first aspect or any one of the implementation methods of the first aspect.

[0042] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0043] The embodiment of the present invention establishes a one-dimensional hydrodynamic model based on hydrological and water quality related data, and runs the one-dimensional hydrodynamic model to obtain one-dimensional dynamic change data of the river; establishes a two-dimensional hydrodynamic water quality model, and jointly solves the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model to simulate the migration and transformation process of pollutants. By coupling the one-dimensional model with the two-dimensional model, their respective advantages are brought into play to refine the hydrodynamic and water quality change processes in the areas connected by different types of water bodies. The coupling model can take into account the interaction between different hydrological and water quality processes, and realize high-resolution dynamic simulation of the whole process of hydrology, hydrodynamics, and water quality from urban basin drainage to river pollution diffusion, more accurately evaluate the migration and diffusion process of pollutants under different emission forms, and improve prediction accuracy. By predicting and evaluating the changes in pollutant load distribution caused by rainfall, technical and data support can be provided for optimizing pollution control and river management. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 It is a schematic diagram of the implementation process of the river pollution prediction method based on the hydrology-hydrodynamics-water quality coupling model provided by an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of a coupling model construction process provided by an embodiment of the present invention;

[0047] Figure 3It is a structural schematic diagram of a river pollution prediction device based on a hydrology-hydrodynamics-water quality coupling model provided by an embodiment of the present invention;

[0048] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0050] In order to illustrate the technical solution of the present invention, a specific embodiment is provided below for illustration.

[0051] Hydrological and hydrodynamic models can simulate the formation mechanism of river water pollution caused by one or more hydrological and environmental factors. By simulating the dynamic evolution of rainfall, runoff, surface runoff and groundwater in the basin, the migration, transformation and deposition of pollutants in the water body are calculated, and the effect of adaptation measures on the compliance of river water quality is evaluated. At present, hydrological models are mostly used for runoff calculations to provide boundary input conditions for hydrodynamic models, and it is difficult to simulate the diffusion process of pollutants from urban rainfall runoff entering river water bodies. Since hydrological models cannot describe complex urban underlying surface characteristics, pipe networks, hydraulic structures and other complex facilities, hydrodynamic models can make up for this deficiency and are therefore widely used.

[0052] The river water quality prediction model is an important tool in the management of urban river water environment. The complexity and variability of the actual application environment make it difficult for a single water quality model to meet comprehensive management and control needs. The embodiment of the present invention aims to solve the coupling problem of river hydrological model, hydrodynamic model and water quality model, and realize the accurate simulation of river water quality and the effective regulation of river pollution sources.

[0053] The following combination Figure 1 , the river pollution prediction method based on the hydrology-hydrodynamics-water quality coupling model provided in an embodiment of the present invention is described.

[0054] See also Figure 1 As shown, the method includes:

[0055] Step S101, obtaining river hydrological and water quality related data.

[0056] Here, hydrological and water quality related data may include but are not limited to:

[0057] (1) Hydrological data

[0058] River data (changes in the (average) depth and width of the river during the flood season, dry season and normal season, the total length of the river and topographic map); river flow station monitoring data (water volume, water level, water speed and flow in the river during the flood season, dry season and normal season, with a time resolution of no less than hourly units).

[0059] (2) Rainfall data

[0060] The basin rainfall data should be of a data series length of at least three months and a temporal resolution of not less than 0.5h.

[0061] (3) Water quality data

[0062] Water quality monitoring data of target river basin sections (chlorophyll, chemical dissolved oxygen, total nitrogen, total phosphorus, ammonia nitrogen, and nitrate concentrations in the river during the flood season, dry season, and off-peak season, with a time resolution accurate to the hour).

[0063] (4) Pipeline network data of the basin area

[0064] Information about sewers, stormwater collection systems and drainage facilities, including pipe size, shape, design flow, etc.

[0065] (5) Underlying surface data

[0066] Land use type, terrain data (such as digital elevation model), underlying surface properties of sponge facilities or land use type, etc.

[0067] (6) Pollution data

[0068] Point source pollution data, non-point source pollution data, etc.

[0069] (7) Basin point source emission information

[0070] Outlet flow and water quality data of all sewage treatment plants’ final discharge rivers.

[0071] (8) Background load information of the watershed

[0072] It mainly includes water quality data of upstream reservoirs and the starting point area of ​​the main stream.

[0073] Step S102, establishing a one-dimensional hydrodynamic model based on hydrological and water quality related data, and running the one-dimensional hydrodynamic model to obtain one-dimensional dynamic change data of the river.

[0074] In this embodiment, a one-dimensional hydrodynamic model can be established by SWMM based on water quality and bottom sediment parameters (such as pollution source strength, pollutant degradation coefficient, riverbed sediment pollutant release coefficient, scouring and sedimentation coefficient, etc.). When obtaining water quality and bottom sediment parameters, it is considered that the water environment of urban rivers has complex pollution causes and is difficult to control, especially the different degrees of water pollution in different areas of the river, the complex types of pollutants, and the migration and transformation laws of pollutants require a combination of systematic decomposition and comprehensive analysis to determine the water quality parameters and bottom sediment parameters of the river network.

[0075] Furthermore, the one-dimensional hydrodynamic model established in this embodiment is:

[0076]

[0077] Among them, Z is the water depth; x is the mileage; t is the time; Q is the flow rate of the water-passing section; h is the water level of the water-passing section; A is the area of ​​the water-passing section; g is the gravitational acceleration; α is the vertical velocity distribution coefficient; R is the hydraulic radius; and c is the Xie Cai coefficient.

[0078] Step S103, establish a two-dimensional hydrodynamic water quality model, and jointly solve the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model to simulate the pollutant migration and transformation process.

[0079] In this embodiment, a two-dimensional hydrodynamic and water quality model may be established by Delft3D, wherein the two-dimensional hydrodynamic and water quality model includes: a two-dimensional hydrodynamic model and a two-dimensional water quality model.

[0080] In the two-dimensional hydrodynamic model, an unstructured grid is used to divide the target two-dimensional calculation area. The control equations of the two-dimensional hydrodynamic model are a two-dimensional shallow water equation group, which mainly includes the Reynolds average equation group of incompressible fluids; the equation group is solved by the finite volume method based on the center of the grid unit, and the unit center normal vector is obtained by establishing a unit hydraulic model along the outer normal and solving the one-dimensional Riemann problem. The two-dimensional shallow water equations in the conservation form used are:

[0081]

[0082] Among them, U is the conservation vector; E adv , G adv are the convective flux vectors in the x and y directions respectively; E diff , G diff are the diffusion flux vectors caused by Reynolds stress in the x and y directions respectively; E dis , G dis are the diffusion flux vectors caused by secondary flow in x and y directions respectively; S is the source term vector.

[0083] The governing equation of the two-dimensional water quality model is the convection-diffusion equation:

[0084]

[0085] Where h is the water depth; u is the water velocity in the horizontal direction; v is the water velocity in the vertical direction; c is the vertical average concentration of the general material component; D x and D y are the diffusion coefficients of the substances in the x and y directions respectively; k is the degradation coefficient; q in and c in are the flow rate and substance concentration of the point source, respectively.

[0086] In this embodiment, the one-dimensional river network and the two-dimensional river section can realize the upstream and downstream joint solution of the one-dimensional-two-dimensional mathematical model through the upstream and downstream connection method.

[0087] The upstream and downstream joint solution interface needs to meet the constraints of water level, flow rate, and total amount of pollutants. That is, the water level of the one-dimensional section is equal to the average water level of the two-dimensional boundary grid; the flow rate of the one-dimensional section is equal to the total flow rate of the two-dimensional boundary grid; the pollutant transport volume of the one-dimensional section is equal to the total transport volume of the two-dimensional boundary grid.

[0088] Here, the "branch point water level prediction and correction method" can be used to perform one-dimensional and two-dimensional longitudinal coupling. In the one-dimensional model, the coupling interface is defined as the water level boundary; in the two-dimensional model, the coupling interface is also defined as the water level boundary. Assuming that the coupling interface has an initial water level (the water level value at the coupling boundary is estimated based on the known solution at the previous moment), the water level is used as the boundary value of the one-dimensional model and the two-dimensional model respectively, and the corresponding one-dimensional boundary section flow value and the two-dimensional boundary grid total flow value can be obtained respectively, and then the coupled boundary net flow Q can be obtained. c According to the branch point water level prediction and correction method, using Q c The water level boundary conditions are corrected until Qc meets the specified calculation tolerance and the iterative calculation is terminated. During the iterative calculation process, the water level correction increment is given by the following formula:

[0089]

[0090] Where Δη is the water level correction increment; Q c is the net flow of coupled boundary; B c is the river width at the coupling boundary, h c is the water depth at the coupling boundary; α is a preset parameter; g is the gravitational acceleration.

[0091] Step S104, determining the water pollution status of the river based on the simulation results.

[0092] In this embodiment, the point source and non-point source pollution loads of the river can be calculated and simulated through the established hydrology-hydrodynamics-water quality coupling model.

[0093] The non-point source pollution load can be calculated by the following formula:

[0094]

[0095] Where L is the pollutant load; λ is the watershed loss coefficient; n is the number of monitoring times or calculation periods; E i A is the pollutant emission intensity monitored for the i-th time, generally representing the pollutant emission per unit area or per unit time; i is the area of ​​the watershed monitored for the i-th time or the area of ​​the relevant calculation area; Δt is the time interval, representing the length of time for each monitoring or calculation.

[0096] The point source pollution load adopts the base flow segmentation straight line cut method in the hydrological segmentation method, that is, the average flow of the three months with the smallest total runoff in the year is used as the base flow flow for hydrological segmentation, and the base flow load is used as the point source pollution load. The point source pollution load can be calculated by the following formula:

[0097]

[0098] Where L is the pollutant load; ρ i is the pollutant mass concentration monitored for the i-th time; Q i is the i-th monitoring flow; Δ i is the i-th monitoring time period.

[0099] The river hydrology-hydrodynamics-water quality coupling model can also be further combined with the SCE-UA algorithm to simulate the river diversion scheduling scheme, and examine the impact of the scheduling scheme on the water quality changes of the river network; coupled with the basin climate model, it can predict the fluctuations in river network water quality changes when future climate disasters (such as heavy rain or drought events) occur. In order to ensure the accuracy of the constructed coupling model, based on the measured climate, hydrology, water quality and other data of the water environment, the Bayesian theory is introduced to evaluate the model parameters, such as parameter sensitivity, identifiability, model uncertainty and model accuracy.

[0100] The embodiment of the present invention establishes a one-dimensional hydrodynamic model based on hydrological and water quality related data, and runs the one-dimensional hydrodynamic model to obtain one-dimensional dynamic change data of the river; establishes a two-dimensional hydrodynamic water quality model, and jointly solves the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model to simulate the migration and transformation process of pollutants. By coupling the one-dimensional model with the two-dimensional model, their respective advantages are brought into play to refine the hydrodynamic and water quality change processes in the areas connected by different types of water bodies. The coupling model can take into account the interaction between different hydrological and water quality processes, and realize high-resolution dynamic simulation of the whole process of hydrology, hydrodynamics, and water quality from urban basin drainage to river pollution diffusion, more accurately evaluate the migration and diffusion process of pollutants under different emission forms, and improve prediction accuracy. By predicting and evaluating the changes in pollutant load distribution caused by rainfall, technical and data support can be provided for optimizing pollution control and river management.

[0101] The following, combined Figure 2 The application example illustrates the detailed establishment process of the hydrology-hydrodynamics-water quality coupling model.

[0102] In order to achieve a more accurate characterization of the hydrodynamic-water quality response of the river, this embodiment couples the Delft3D model, which has great advantages in hydrodynamic-water quality simulation, to the SWMM model. SWMM shows significant advantages in simulating the effects of rainfall on the water collection process, runoff changes, and the generation and transportation of corresponding pollutants within the urban watershed. Delft3D is more advantageous in simulating the diffusion, deposition, and migration of pollutants in large water bodies such as rivers and lakes. Therefore, the SWMM-Delft3D coupling model constructed based on the two can achieve high-resolution dynamic simulation of the entire hydrology-hydrodynamics-water quality process from urban watershed drainage to river pollution diffusion, and establish a high-resolution pollutant response simulation model.

[0103] The SWMM model simulates the runoff generation and confluence process and non-point source pollution load: sort out the basic data sources, including local hydrological data (mainly rainfall data), topographic data (mainly underlying surface data), drainage pipe network system data, and drainage confluence boundary data (the connection between the drainage outlet and the confluence water body). At the same time, sort out long-term data, especially the significant changes brought about by urbanization to the topography, drainage, and confluence data of the target area. A series of specific time points / segments with detailed measured data are selected as cases for model calculation.

[0104] Delft3D model couples river hydrodynamics and water quality processes: organizes basic data sources: hydrology, drainage, confluence data (consistent with SWMM data), water volume data (obtained from SWMM model), hydrodynamic equations, pollutant transport parameters (convection diffusion), pollutant consumption process (treatment system). Based on the water volume results obtained by SWMM model, the Delft3D model is coupled to simulate and calculate the migration and transformation process of target pollutants.

[0105] River flow and confluence process and pollutant response model: Coupled model optimization, combined with machine learning models (gradient boosting tree, random forest, support vector machine, etc.), using measured data to compare and verify the model, determine the key parameters that cause the difference, and calibrate the key influencing parameters to obtain the optimized model. Finally, predict the conditions that should be met for water quality to meet the standards, determine the corresponding adjustment and response strategies in the discharge and treatment system, and thus improve the hydrological-hydrodynamic-water quality coupling model.

[0106] The advantages of SWMM coupled with Delft3D model for river water quality simulation and prediction are mainly reflected in the following points:

[0107] (1) SWMM focuses on urban drainage systems and simulates processes such as rainfall runoff, pipe network transportation, and pollutant loads, while Delft3D excels at simulating water flow and quality, and is suitable for hydrodynamic and water quality simulation of large-scale water bodies such as rivers, lakes, and coastal waters. The combination of the two can simulate the impact of urban runoff on downstream water bodies at different spatial scales, and achieve comprehensive simulation from small urban watersheds to large water bodies.

[0108] (2) SWMM provides high-precision simulation of urban hydrological processes, and Delft3D provides detailed simulation of hydrodynamic and water quality processes. The coupled model can consider the interaction between different hydrological and water quality processes, more accurately evaluate the migration and diffusion process of pollutants under different emission forms, and improve prediction accuracy.

[0109] (3) The complex interactions of multiple emission sources in urban areas require a comprehensive model for effective analysis. By coupling SWMM with the Delft3D model, different water quality management strategies and scenarios can be adapted, different emission scenarios can be simulated, the effectiveness of management measures can be evaluated, and pollution control strategies can be optimized.

[0110] (4) This coupling method allows analysis at different spatial and temporal scales, making river management and water quality improvement measures more targeted.

[0111] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0112] Figure 3 : is a schematic diagram of the structure of a river pollution prediction device based on a hydrology-hydrodynamics-water quality coupling model provided by an embodiment of the present invention, comprising:

[0113] The acquisition module 31 is used to acquire the hydrological and water quality related data of the river.

[0114] The first processing module 32 is used to establish a one-dimensional hydrodynamic model according to the hydrological and water quality related data, and run the one-dimensional hydrodynamic model to obtain one-dimensional dynamic change data of the river.

[0115] The second processing module 33 is used to establish a two-dimensional hydrodynamic water quality model, and jointly solve the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model to simulate the migration and transformation process of pollutants.

[0116] The determination module 34 is used to determine the water pollution status of the river based on the simulation results.

[0117] As a possible implementation, the one-dimensional hydrodynamic model includes:

[0118]

[0119] Among them, Z is the water depth; x is the mileage; t is the time; Q is the flow rate of the water-passing section; h is the water level of the water-passing section; A is the area of ​​the water-passing section; g is the gravitational acceleration; α is the vertical velocity distribution coefficient; R is the hydraulic radius; and c is the Xie Cai coefficient.

[0120] As a possible implementation method, the two-dimensional hydrodynamic and water quality model includes: a two-dimensional hydrodynamic model and a two-dimensional water quality model;

[0121] The two-dimensional hydrodynamic model is:

[0122]

[0123] Among them, U is the conservation vector; E adv , G adv are the convective flux vectors in the x and y directions respectively; E diff , G diff are the diffusion flux vectors caused by Reynolds stress in the x and y directions respectively; E dis , G dis are the diffusion flux vectors caused by the secondary flow in the x and y directions respectively; S is the source term vector;

[0124] The two-dimensional water quality model is:

[0125]

[0126] Where h is the water depth; u is the water velocity in the horizontal direction; v is the water velocity in the vertical direction; c is the vertical average concentration of the general material component; D x and D y are the diffusion coefficients of the substances in the x and y directions respectively; k is the degradation coefficient; q in and c in are the flow rate and substance concentration of the point source, respectively.

[0127] As a possible implementation method, the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model are jointly solved to simulate the migration and transformation process of pollutants, including:

[0128] Establish constraints on water levels, flows, and total pollutant loads;

[0129] The “branch point water level prediction and correction method” is used to perform one-dimensional and two-dimensional longitudinal coupling, and the water level boundary conditions are iteratively corrected according to the constraints until the net flow at the coupled boundary meets the preset calculation tolerance and the iteration is terminated.

[0130] As a possible implementation, the water level correction increment in the iterative process is:

[0131]

[0132] Where Δη is the water level correction increment; Q c is the net flow of coupled boundary; B c is the river width at the coupling boundary, h c is the water depth at the coupling boundary; α is a preset parameter; g is the gravitational acceleration.

[0133] As a possible implementation method, the water pollution of the river is determined based on the simulation results, including:

[0134] Based on the simulation results, the point source pollution load and non-point source pollution load of the river are calculated to determine the water pollution situation of the river;

[0135] The non-point source pollution load is calculated by the following formula:

[0136]

[0137] Where L is the pollutant load; λ is the watershed loss coefficient; n is the number of monitoring times or calculation periods; E i A is the pollutant emission intensity monitored for the i-th time, generally representing the pollutant emission per unit area or per unit time; i is the area of ​​the watershed monitored for the i-th time or the area of ​​the relevant calculation area; Δt is the time interval, representing the length of time for each monitoring or calculation;

[0138] Point source pollution load is calculated by the following formula:

[0139]

[0140] Where L is the pollutant load; ρ i is the pollutant mass concentration monitored for the i-th time; Q i is the i-th monitoring flow; Δ i is the i-th monitoring time period.

[0141] As a possible implementation method, a one-dimensional hydrodynamic model is established through SWMM; a two-dimensional hydrodynamic water quality model is established through Delft3D.

[0142] The embodiment of the present invention establishes a one-dimensional hydrodynamic model based on hydrological and water quality related data, and runs the one-dimensional hydrodynamic model to obtain one-dimensional dynamic change data of the river; establishes a two-dimensional hydrodynamic water quality model, and jointly solves the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model to simulate the migration and transformation process of pollutants. By coupling the one-dimensional model with the two-dimensional model, their respective advantages are brought into play to refine the hydrodynamic and water quality change processes in the areas connected by different types of water bodies. The coupling model can take into account the interaction between different hydrological and water quality processes, and realize high-resolution dynamic simulation of the whole process of hydrology, hydrodynamics, and water quality from urban basin drainage to river pollution diffusion, more accurately evaluate the migration and diffusion process of pollutants under different emission forms, and improve prediction accuracy. By predicting and evaluating the changes in pollutant load distribution caused by rainfall, technical and data support can be provided for optimizing pollution control and river management.

[0143] Figure 4 FIG. 4 is a schematic diagram of an electronic device 40 provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 40 of this embodiment includes: a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable on the processor 41, such as a river pollution prediction program based on a hydrology-hydrodynamics-water quality coupling model. When the processor 41 executes the computer program 43, the steps in the above-mentioned river pollution prediction method embodiments based on a hydrology-hydrodynamics-water quality coupling model are implemented, such as Figure 1 Alternatively, when the processor 41 executes the computer program 43, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 3 The functions of the modules 31 to 34 are shown.

[0144] Exemplarily, the computer program 43 may be divided into one or more modules / units, which are stored in the memory 42 and executed by the processor 41 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 43 in the electronic device 40.

[0145] The electronic device 40 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 40 may include, but is not limited to, a processor 41 and a memory 42. Those skilled in the art will appreciate that Figure 4It is only an example of the electronic device 40 and does not constitute a limitation of the electronic device 40. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 40 may also include input and output devices, network access devices, buses, etc.

[0146] The processor 41 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0147] The memory 42 may be an internal storage unit of the electronic device 40, such as a hard disk or memory of the electronic device 40. The memory 42 may also be an external storage device of the electronic device 40, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 40. Further, the memory 42 may also include both an internal storage unit of the electronic device 40 and an external storage device. The memory 42 is used to store the computer program and other programs and data required by the electronic device 40. The memory 42 may also be used to temporarily store data that has been output or is to be output.

[0148] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0149] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0150] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0151] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0152] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0154] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0155] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A river pollution prediction method based on a hydrology-hydrodynamics-water quality coupling model, characterized in that: include: Obtain river hydrological and water quality data; A one-dimensional hydrodynamic model is established based on the hydrological and water quality related data, and the one-dimensional hydrodynamic model is run to obtain one-dimensional dynamic change data of the river; Establishing a two-dimensional hydrodynamic water quality model, and jointly solving the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model to simulate the migration and transformation process of pollutants; Based on the simulation results, the water pollution status of the river is determined.

2. The river pollution prediction method based on the hydrology-hydrodynamics-water quality coupling model according to claim 1 is characterized in that: The one-dimensional hydrodynamic model includes: Among them, Z is the first water depth; x is the mileage; t is the time; Q is the flow rate of the water-passing section; h is the water level of the water-passing section; A is the area of ​​the water-passing section; g is the gravitational acceleration; α is the vertical velocity distribution coefficient; R is the hydraulic radius; and c is the Xie Cai coefficient.

3. The river pollution prediction method based on the hydrology-hydrodynamics-water quality coupling model according to claim 1 is characterized in that: The two-dimensional hydrodynamic and water quality model includes: a two-dimensional hydrodynamic model and a two-dimensional water quality model; The two-dimensional hydrodynamic model is: Among them, U is the conservation vector; E adv , G adv are the convective flux vectors in the x and y directions respectively; E diff , G diff are the diffusion flux vectors caused by Reynolds stress in the x and y directions respectively; E dis , G dis are the diffusion flux vectors caused by the secondary flow in the x and y directions respectively; S is the source term vector; The two-dimensional water quality model is: Wherein, h is the second water depth; u is the water velocity in the horizontal direction; v is the water velocity in the vertical direction; c is the vertical average concentration of the general material component; D x and D y are the diffusion coefficients of the substances in the x and y directions respectively; k is the degradation coefficient; q in and c in are the flow rate and substance concentration of the point source, respectively.

4. The river pollution prediction method based on the hydrology-hydrodynamics-water quality coupling model according to claim 1 is characterized in that: The one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model are jointly solved to simulate the pollutant migration and transformation process, including: Establish constraints on water levels, flows, and total pollutant loads; The "branch point water level prediction and correction method" is used to perform one-dimensional and two-dimensional longitudinal coupling, and the water level boundary conditions are iteratively corrected according to the constraints until the net flow of the coupled boundary meets the preset calculation tolerance and then the iteration is terminated.

5. The river pollution prediction method based on the hydrology-hydrodynamics-water quality coupling model according to claim 4 is characterized in that: The water level correction increment during the iteration process is: Where Δη is the water level correction increment; Q c is the net flow of coupled boundary; B c is the river width at the coupling boundary, h c is the water depth at the coupling boundary; α is a preset parameter; g is the gravitational acceleration.

6. The river pollution prediction method based on the hydrology-hydrodynamics-water quality coupling model according to claim 1 is characterized in that: Based on the simulation results, the water pollution of the river is determined, including: Based on the simulation results, the point source pollution load and non-point source pollution load of the river are calculated to determine the water pollution status of the river; wherein, The non-point source pollution load is calculated by the following formula: Where L is the pollutant load; λ is the watershed loss coefficient; n is the number of monitoring times; E i is the pollutant emission intensity monitored for the i-th time; A i is the basin area of ​​the ith monitoring; Δt is the time interval; The point source pollution load is calculated by the following formula: Where L is the pollutant load; ρ i is the pollutant mass concentration monitored for the i-th time; Q i is the i-th monitoring flow; Δ i is the i-th monitoring time period.

7. The river pollution prediction method based on the hydrology-hydrodynamics-water quality coupling model according to any one of claims 1 to 6, characterized in that: The one-dimensional hydrodynamic model is established by SWMM; the two-dimensional hydrodynamic water quality model is established by Delft3D.

8. A river pollution prediction device based on a hydrology-hydrodynamics-water quality coupling model, characterized in that: include: An acquisition module is used to obtain the hydrological and water quality related data of the river; A first processing module is used to establish a one-dimensional hydrodynamic model according to the hydrological and water quality related data, and run the one-dimensional hydrodynamic model to obtain one-dimensional dynamic change data of the river; The second processing module is used to establish a two-dimensional hydrodynamic water quality model, and jointly solve the one-dimensional dynamic change data and the two-dimensional hydrodynamic water quality model to simulate the migration and transformation process of pollutants; The determination module is used to determine the water pollution status of the river based on the simulation results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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