Simulation method, device, medium and equipment for integrated model of source network, plant, river and lake

Abstract

The invention discloses a source network, plant, river and lake integrated model simulation method and device, a medium and equipment in the technical field of regional water environment pollution treatment. The method comprises the following steps: inputting a boundary condition and an initial condition of a first model into a pre-constructed surface runoff production and convergence model of a target area, and outputting a flow rate of surface runoff and a water quality index time sequence; inputting the boundary condition and the initial condition of the second model into a pre-constructed pipe network model of a target area, and outputting a flow and water quality index time sequence of key nodes in a pipe network system; and inputting the boundary condition and the initial condition of the third model into a pre-constructed riverway and lake grid model of a target area, and simulating the river and lake hydrodynamics and water quality change process of the target area. According to the invention, coupling simulation of surface runoff, pipe networks, rivers and lakes is realized, and a propagation path and an influence mechanism of pollutants are comprehensively reflected; and high precision and reliability of the model are ensured through parameter calibration.

Description

Technical Field

[0001] This invention relates to a method, apparatus, medium, and equipment for simulating an integrated model of water sources, networks, plants, rivers, and lakes, belonging to the field of regional water environment pollution control technology. Background Technology

[0002] In urban water environment management, hydrodynamic and water quality simulation of surface runoff generation and runoff, pipe network systems, rivers, and lakes are important means of assessing and controlling the impact of pollution sources. In existing technologies, these simulations are typically conducted using independent models. For example, surface runoff generation and runoff simulations often use models such as SWMM, HSPF, and SWAT, focusing on the runoff generation and runoff processes of rainfall runoff and non-point source pollution; pipe network simulations use models such as SWMM and InfoWorks to analyze water flow and pollutant transport in urban drainage networks; and river and lake simulations often employ models such as EFDC and MIKE 21 to study hydrodynamic and water quality changes in natural water bodies.

[0003] However, existing technologies have the following shortcomings: First, each water system model operates independently. Surface runoff, pipe network, and river / lake models are typically simulated and calculated independently, making it difficult to reflect the interactions between them and the continuous propagation paths of pollutants. For example, the direct impact of pipe network overflows on river water quality is often overlooked. Second, there is a lack of comprehensiveness: existing models cannot comprehensively assess the complete transport process of pollution sources from their origin to the water body from a watershed perspective. Especially in the process of urbanization, the combined impact of surface runoff and pipe network overflows on river and lake water quality is becoming increasingly significant, making it essential to couple water body models with land-based models for simulation. Finally, in terms of simulation accuracy, independent models are disconnected from upstream and downstream water systems in terms of parameter settings and boundary conditions, failing to dynamically reflect the actual water environment conditions and affecting the simulation accuracy of the models and subsequent scientific decision-making. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method for establishing an integrated model of pollution sources, networks, plants, rivers and lakes, so as to realize the simulation and control of the whole process of pollution source transmission and improve the efficiency and accuracy of urban water environment management.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution.

[0006] In a first aspect, this invention discloses an integrated model simulation method for power sources, grids, plants, rivers, and lakes, comprising: Based on the collected rainfall data, water quality data, topographic data and land cover type of the target area, the boundary conditions and initial conditions of the first model are determined. The boundary conditions and initial conditions of the first model are then input into the pre-constructed surface runoff generation and runoff model of the target area, and the time series of surface runoff flow and water quality indicators are output. Based on the time series of surface runoff flow and water quality indicators and the time series of flow and water quality indicators collected at the confluence of the main pipes of the pipeline network, the boundary conditions and initial conditions of the second model are determined. The boundary conditions and initial conditions of the second model are then input into the pre-constructed pipeline network model of the target area, and the time series of flow and water quality indicators at the outlet of the pipeline network system are output. Based on the time series of surface runoff flow and water quality indicators, and the time series of flow and water quality indicators at the outlets of the pipeline network system, the boundary conditions and initial conditions of the third model are determined. The boundary conditions and initial conditions of the third model are then input into the pre-constructed river and lake grid model of the target area to simulate the river and lake hydrodynamics and water quality change process in the target area.

[0007] Furthermore, the construction of the surface runoff generation and runoff model includes: constructing a surface runoff generation and runoff model for the target area using the SWMM model based on the acquired regional runoff surface data of the target area.

[0008] Furthermore, the construction of the pipeline network model of the target area includes: constructing the pipeline network model of the target area using the SWMM model based on the obtained pipeline network topology, outlet location and outlet design parameters of the target area.

[0009] Furthermore, the construction of the river and lake grid model in the target area includes: constructing an initial river and lake grid model using the EFDC model based on the acquired river and lake geographic data of the target area; and performing local densification of the source strength area of ​​the initial river and lake grid model based on the acquired pollution source strength data of the target area to obtain the river and lake grid model.

[0010] Furthermore, the coupling process between the SWMM model and the EFDC model is as follows: The SWMM model is simulated by calling the SWMM5.dll file using Python software; the binary results of the SWMM model are parsed using the inflow function in the PySWMM library; the runoff and pollutant concentration output by the SWMM model are converted into boundary files that can be recognized by the EFDC model, and the EFDC model is driven to simulate the hydrodynamic and water quality processes of rivers and lakes.

[0011] Furthermore, before outputting the surface runoff generation and drainage model and pipeline model of the target area, the following steps are also included: Collect measured flow, water level and water quality data of the target area, and calibrate the surface runoff generation and confluence model and the pipeline network model based on the measured flow, water level and water quality data, including the parameters of surface roughness and pollutant scour index. Adjust the parameters of the surface runoff generation and confluence model and the pipeline network model until the output results of the surface runoff generation and confluence model and the pipeline network model match the measured data. Before outputting the river and lake grid model of the target area, the following is also included: Collect measured flow, water level, and water quality data of the target area. Based on the measured flow, water level, and water quality data, calibrate the parameters of the river and lake grid models, including the roughness coefficient and pollutant attenuation coefficient. Adjust the parameters of the river and lake grid models until the simulation results of the river and lake grid models meet the preset accuracy requirements of the measured data.

[0012] Furthermore, the pollution source intensity data for the target area includes: point source water-related data and non-point source water-related data; The point source water-related data includes discrete pollution sources such as wastewater treatment plant effluent, industrial emissions, and overflow emissions. The non-point source water-related data includes precipitation runoff, agricultural non-point source pollution, and urban non-point source pollution.

[0013] Secondly, the present invention also discloses an integrated model simulation device for power generation, grid, plant, river, and lake systems, comprising: The first model processing module is used to determine the boundary conditions and initial conditions of the first model based on the collected rainfall data, water quality data, topographic data and land cover type of the target area, input the boundary conditions and initial conditions of the first model into the pre-constructed surface runoff generation and runoff model of the target area, and output the flow rate and water quality index time series of surface runoff. The second model processing module is used to determine the boundary conditions and initial conditions of the second model based on the time series of surface runoff flow and water quality indicators and the time series of flow and water quality indicators collected at the confluence of the main pipes of the pipeline network, input the boundary conditions and initial conditions of the second model into the pre-constructed pipeline network model of the target area, and output the time series of flow and water quality indicators at the outlet of the pipeline network system. The third model processing module is used to determine the boundary conditions and initial conditions of the third model based on the time series of surface runoff flow and water quality indicators, and the time series of flow and water quality indicators at the outlets of the pipeline network system. The boundary conditions and initial conditions of the third model are then input into the pre-constructed river and lake grid model of the target area to simulate the river and lake hydrodynamics and water quality change process of the target area.

[0014] Thirdly, the present invention also discloses a computer-readable storage medium for storing one or more programs, characterized in that the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method described in the first aspect.

[0015] Fourthly, the present invention also discloses a computer device, comprising, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing the method described in the first aspect.

[0016] The beneficial effects achieved by this invention are as follows: This invention enables coupled simulation of surface runoff, pipe networks, rivers, and lakes, comprehensively reflecting the propagation paths and impact mechanisms of pollutants; through parameter calibration, it ensures the high accuracy and reliability of the model; and it provides a scientific basis for urban water environment management, supporting the formulation of targeted pollution control measures. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the coupling between the SWMM and EFDC models; Figure 2 This is a schematic diagram of a surface runoff generation and confluence model simulation; Figure 3 This is a schematic diagram of the pipeline network model; Figure 4 This is a schematic diagram of the results of determining the flow rate at wastewater nodes; Figure 5 This is a schematic diagram of the flow rate calibration results from the heavy rain event model; Figure 6 This is a schematic diagram illustrating the verification results of the water flow rate from the light rain event model. Figure 7 This is a schematic diagram of the outflow water quality determination results from the heavy rain event model; Figure 8 This is a schematic diagram of the water quality verification results from the light rain event model. Figure 9 This is a schematic diagram of grid division; Figure 10 This is a schematic diagram of the underwater terrain layout; Figure 11 This is a diagram illustrating the setting of flow boundary conditions; Figure 12 This is a schematic diagram of meteorological boundary conditions. Figure 13 This is a schematic diagram of the lake water level determination results; Figure 14 This is a schematic diagram of the water quality determination results. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0019] Example 1, as shown in the figure, introduces an integrated model simulation method for source-network-plant-river-lake systems. By coupling the SWMM and EFDC models, it achieves comprehensive simulation of surface runoff generation and runoff, pipeline networks, rivers, and lakes' hydrodynamics and water quality. The specific steps of this method are as follows: Data Acquisition: 1) Collect point source and non-point source pollution intensity data for the study area. Point sources include wastewater treatment plant effluent, industrial emissions, and overflow discharges, while non-point sources include rainfall runoff, agricultural non-point sources, and urban non-point source pollution. Data sources include water quality monitoring stations, remote sensing images, and records provided by local water authorities; 2) Rainfall data, water quality data (total nitrogen, total phosphorus, ammonia nitrogen, COD, etc.), topographic data, land cover types, flow rates at the confluence of main pipes, and time series of water quality indicators for the study area.

[0020] Surface runoff generation and runoff model construction: The SWMM model is used to simulate surface runoff generation and runoff and water quality processes. The specific steps are as follows: Based on topographic data (such as DEM) and land cover type, sub-catchment areas are divided; boundary conditions and initial conditions such as hourly rainfall, ground elevation, roughness, and impervious area ratio are input; model parameters such as ground roughness and pollutant scour index are calibrated through measured runoff and water quality data.

[0021] Pipeline network model construction: The SWMM model is used to simulate the flow rate, velocity and water quality changes in rainwater pipes, sewage pipes and combined sewer systems. The specific steps are as follows: input the pipeline network topology, outlet location and design parameters (such as interceptor weir height, width and overflow capacity); set the inlet flow rate and pollutant concentration boundary conditions; simulate the water flow and pollutant transport process in the pipeline.

[0022] River and lake model construction: The EFDC model is used to simulate the hydrodynamics and water quality changes of rivers and lakes. The specific steps are as follows: construct the computational grid of the river and lake, in which the grid nesting function of the EFDC model can be used to locally refine the grid for source strength or key areas to accurately depict the migration and transport process of pollutants; input the initial conditions and boundary conditions such as water level, flow rate, and pollutant concentration; simulate the water velocity field, flow direction field, water level changes and pollutant concentration distribution.

[0023] Model Coupling: The simulation results of the SWMM model (such as surface runoff and pipe network outflow) are used as boundary conditions for the EFDC model to achieve coupling between surface runoff, pipe network, and rivers and lakes. Specifically, the SWMM simulation is performed using Python software by calling the SWMM5.dll file; the binary results of the SWMM model are parsed using the inflow function in the PySWMM library; and the runoff and pollutant concentrations output by SWMM are converted into boundary files recognizable by EFDC to drive the EFDC model in simulating the hydrodynamic and water quality processes of rivers and lakes.

[0024] Parameter calibration: Perform parameter calibration on the SWMM and EFDC models. The specific steps are as follows: collect measured flow rate, water level and water quality data; adjust parameters such as ground roughness and pollutant scour index in the SWMM model; adjust parameters such as roughness coefficient and pollutant attenuation coefficient in the EFDC model; until the simulation results and measured data reach the preset accuracy requirements.

[0025] Applications of Results: Based on the model output, the impact of different pollution sources on river and lake water quality is assessed, and water quality management strategies are developed. For example, by adjusting the permeability and storage capacity of LID (Light Isolation Device) facilities, their effect on reducing runoff and pollution is simulated; source control projects, such as green roofs and rainwater bins, are designed to manage urban stormwater runoff and overflow pollution.

[0026] Example 2, based on the same inventive concept as Example 1, introduces a method for simulating an integrated source-grid-plant-river-lake model, including the following steps: 1.1 Obtain water-related data on pollution source strength in the study area, including point sources and non-point sources. Point sources include discrete pollution sources such as wastewater treatment plant effluent, industrial emissions, and overflow discharges. Non-point sources include precipitation runoff, agricultural non-point sources, and urban non-point source pollution. The boundary conditions of the surface runoff model include hourly rainfall, and the output results include the flow rate and water quality indicators (such as COD, ammonia nitrogen, total nitrogen, and total phosphorus concentrations) time series. These results will also be used as inputs to the pipeline network model. The boundary conditions of the pipeline network model include inlet flow (including runoff output from the surface runoff model), and the output results include the flow rate and water quality indicator time series of key nodes in the pipeline network system (such as main pipe confluence and outlet outlets). These results can be used as pollution source input boundary conditions for river and lake models. The water quantity and quality results output by the surface runoff and pipeline network models can be used as input water quantity and quality boundary conditions, along with meteorological data (wind speed, temperature, etc.) as boundary conditions for river and lake models.

[0027] 1.2 Surface runoff simulation: The SWMM (Storm Water Management Model) model is used to simulate the runoff process and water quality process line of the source area. Hourly rainfall data and spatial distribution of underlying surface types are obtained. By collecting and analyzing various hydrological and water quality data, including but not limited to rainfall, flow data, groundwater level data, pollutant concentration, dissolved oxygen content, and temperature, the parameters of the SWMM model are calibrated to ensure the accuracy and reliability of the model output results.

[0028] 1.3 The pipeline network model uses the SWMM (Storm Water Management Model) model to simulate the changes in velocity, flow rate, and water quality in stormwater pipes, sewage pipes, and combined sewer systems.

[0029] 1.4 The river and lake models use the EFDC (Environmental Fluid Dynamics Code) model to simulate the hydrodynamics and water quality changes of rivers and lakes, and analyze the water flow, pollutant distribution and their spatiotemporal changes, including hydrodynamics (velocity field, flow direction field and water level changes) and water quality (pollutant concentration distribution, dissolved oxygen, temperature distribution, dynamic changes of algae and plankton).

[0030] 1.5 Based on the hydrodynamic and water quality simulation results output by the EFDC model, assess the impact of water pollution from different sources on the river and lake water environment, and provide decision support for water quality management and optimization.

[0031] The water-related data from point sources and area sources include water flow and pollutant discharge data from sources such as precipitation, sewage, and agricultural runoff. These data are obtained through water quality indicator monitoring (including automatic online monitoring and manual laboratory testing of chemical oxygen demand, ammonia nitrogen, and total phosphorus), surface hydrological monitoring (covering on-site measurements of parameters such as flow rate and water level), and remote sensing inversion techniques.

[0032] The framework for constructing the SWMM surface runoff generation and runoff model includes dividing the sub-catchment areas (based on topographic data (such as DEM) and land cover type, dividing the study area into sub-catchment areas), inputting boundary conditions (inlet flow, pollutant concentration curves, rainfall boundary conditions, etc.) and initial conditions (ground elevation, ground roughness, surface infiltration parameters, pollutant scour index, etc.), and setting relevant parameters (initial soil moisture content, impervious area ratio, pollutant attenuation coefficient, etc.).

[0033] Constructing the SWMM pipeline network model framework includes building the pipeline network topology and outflow conditions (the outflow conditions should clearly define the location of the outlet and its design parameters, especially when there is a weir at the outlet, the design parameters of the weir (such as height, width, overflow capacity, etc.) need to be described in detail), inputting boundary conditions (inlet flow rate and water quality, outlet conditions, rainfall boundary conditions, etc.) and initial conditions (reservoir / pool water level, pipeline / node status, pollutant concentration, pollutant attenuation coefficient, etc.).

[0034] The simulation results of the SWMM model are used as the boundary conditions of the EFDC model, and the point source and the river baseflow boundary are combined to construct a coupled SWMM and EFDC model.

[0035] Construct an EFDC (Enhanced Ecosystem Design) grid for rivers and lakes, inputting boundary conditions (inlet and outlet flow rates, water quality, meteorological boundaries, etc.) and initial conditions (water level, pollutant concentration, etc.). The hydrodynamic simulation in the EFDC model includes the flow velocity field, flow direction field, and water level changes; the water quality simulation includes pollutant concentration distribution, dissolved oxygen, temperature distribution, and dynamic changes in algae and plankton.

[0036] When performing EFDC grid delineation for rivers and lakes, the grid nesting function of the EFDC model can be used to locally refine the source strength or key area range in order to accurately depict the migration and transport process of pollutants.

[0037] Before extracting the water quantity, hydrodynamics, and water quality results from the target pipeline and surface runoff, the process includes parameter calibration of the surface runoff model and pipeline network model. This process involves adjusting model parameters (such as surface roughness, pollutant scour index, etc.) until the model output results match the measured data.

[0038] The coupling process between SWMM and EFDC is implemented using Python software. SWMM simulation is performed through the SWMM5.dll file, and the inflow number in the PySWMM public library is used to interpret the binary results of the SWMM model. The extracted water quantity and water quality simulation data of the SWMM model are adjusted to the boundary file that EFDC can recognize, and the EFDC is driven to run to simulate the hydrodynamic and water quality processes of rivers and lakes.

[0039] Before extracting the hydrodynamic and water quality results of the target river and lake, the process also includes parameter calibration of the hydrodynamic and water quality model. This process involves adjusting model parameters (such as roughness coefficient, pollutant attenuation coefficient, etc.) until the model output results match the measured data.

[0040] The simulation results assess the impact of water pollution from different sources on the river and lake water environment and provide decision support for water quality management and optimization, covering the following aspects: Based on the hydrodynamic and water quality simulation results obtained from the integrated source-network-plant-river-lake model, the sources of water pollution and their transmission pathways are analyzed. Based on the constructed integrated source-network-plant-river-lake model, the effects of different pollution source control measures on improving river and lake water quality are simulated and evaluated. Based on the model output, water quality management strategies can be formulated, such as: by adjusting LID attributes, such as permeability and storage capacity, the impact of different LID measures on runoff can be simulated, thereby effectively reducing runoff; by changing LID attributes, the effects of different LID facility layouts on the urban water environment can be evaluated, etc.

[0041] The pollution source control measures include the combined application of point source emission reduction projects, non-point source interception facilities, and ecological restoration projects, and the synergistic effect of multiple measures is verified through model simulation.

[0042] The model is implemented based on a high-efficiency numerical computing platform, enabling real-time simulations across multiple time and spatial scales, and is applicable to water quality analysis of different watersheds and water body types. Based on the model output, source control projects such as green roofs, rainwater bins, and infiltration paving, as well as stormwater retention projects such as rain gardens, infiltration channels, and bioretention ponds, are designed to effectively manage urban stormwater runoff and overflow pollution.

[0043] Example 3: Taking the construction of an integrated factory-network-lake model in a certain city as an example, the method of the present invention is specifically implemented as follows: Data Acquisition: Collect point source data (effects and concentration of wastewater effluent from sewage treatment plants, etc.), non-point source data (such as average annual rainfall and land use type), and pipeline layout map for City A in 2023.

[0044] Surface runoff generation and runoff model construction: A surface runoff generation and runoff model was constructed using the SWMM model, generalizing the regional runoff-generating surface into three types: roads, rooftops, and green spaces. The areas and proportions of roads, rooftops, and green spaces were statistically obtained. Several sub-catchments were divided based on DEM data, with input rainfall (hourly scale), surface roughness, and impervious area proportions. Parameter calibration was performed using measured runoff data. Rainwater catchment areas were divided using the Thiessen polygon method based on road blocks and storm drain locations, resulting in 313 rainwater catchment areas. Wastewater catchment areas were manually drawn in ArcGIS software using satellite imagery, topographic maps, and on-site pipeline survey data. After completion, the polygon features were imported into the model network, resulting in 287 wastewater catchment areas. Figure 2 As shown.

[0045] Pipeline network model construction: The pipeline structure and outlet locations were input into SWMM, and inlet flow rates and pollutant concentrations were set. A total of 1041 nodes were generalized, including 361 sewage inspection wells, 649 stormwater inspection wells, and 31 drainage outlets; the total pipeline length was 76.2 km, including 684 sections of municipal stormwater pipelines (48.7 km long) and 328 sections of municipal sewage pipelines (27.5 km long). Additionally, there are 6 drainage outlets along the lake that are intercepting overflow outlets for combined stormwater and sewage flows. Hourly variation curves for population density, per capita comprehensive water consumption, and drainage volume within the sub-catchment area were set in the sewage system. Based on actual water supply data and population density in the study area, per capita comprehensive water consumption data for the sewage sub-catchment area were determined. Using the actual monitored characteristics of sewage discharge and pollutant concentration changes from source dischargers, the time variation coefficient of sewage discharge from source plots was determined, and measured sewage curves were set. Water quality simulation, using COD as an example, set pollutant process lines for inflow events, with time steps matching the inflow events, such as... Figure 3 As shown.

[0046] Surface-Pipeline Model Calibration and Validation: Based on field monitoring data such as liquid level and flow rate, the dry-day water volume of key nodes in the drainage pipeline network was verified. Dry-day sewage volume calibration was performed on the end node YS06 of the stormwater pipeline and the end catchment point AB06 in the study area. The calibration results are as follows: Figure 4 As shown, the relative errors between the simulated wastewater flow rate at fixed points and the measured data are 6.4% and 6.9%, respectively. This indicates that the current wastewater model has good accuracy and can be further studied in rainy weather simulation.

[0047] Based on the satisfactory performance of the wastewater system flow rate calibration, two representative measured rainfall events were selected for the calibration and verification of the hydrodynamic model of the pipe network outflow during rainy weather. These two rainfall events differed significantly in rainfall amount, duration, average intensity, and preceding drought duration. The measured flow rate and the simulated value were both measured at the same location: the manhole upstream of the drainage outlet. The model outflow flow rate calibration and verification results are as follows: Figure 5 and Figure 6 As shown. Calibration scenario, outlet E NS All coefficients are >0.9, verifying the discharge port E in the scenario. NS All coefficients are >0.8.

[0048] like Figure 7 and Figure 8 As shown, the simulation error E of the two discharge outlets for COD simulation concentration in the heavy rain scenario is... NS All coefficients are >0.8, and the relative errors between simulated and measured values ​​are all controlled within 30%. Model validation was performed using monitoring data from a light rain event scenario. Simulation results show that the water quality simulation error E... NS All > 0.68. The consistency between calibration and validation results indicates that the model simulation error is small and E NS The coefficients are relatively high. The parameter values ​​during the calibration process are shown in Table 1.

[0049] Table 1. Calibration values ​​of drainage model parameters Parameter type Model values Parameter type Model values Bus parameters 0.015 Pipeline Manning coefficient 0.013 Initial loss value of impermeable surface 1.7 Pipe slope measured data Impermeable surface fixed runoff coefficient 0.9 Surface pollutant accumulation coefficient 16 Initial loss value of permeable surface 1.5 Pipeline pollutant degradation coefficient 1.8 Initial infiltration rate 76 Wastewater diurnal variation factors 1.45 Limit Infiltration 6.3 Accumulation time of surface pollutants 24 Attenuation factor 2 Dao and Lake Model Construction: such as Figures 9-12 As shown, an EFDC model was used to construct a lake model. The lake was divided into grids, with local mesh refinement within 500 meters of the outlet using the EFDC model's mesh nesting function to finely depict the migration and transport process of pollution sources after entering the lake. Initial conditions included underwater topography, initial water level, and initial concentration field. Boundary conditions included meteorological boundaries, flow boundaries, and water quality boundaries. The flow and water quality boundary data for the EFDC lake model were derived from the overflow outlet flow and water quality simulation results of the SWMM model. A Python script was used to automatically parse the SWMM simulation results and convert them into a data format compatible with the EFDC model, achieving automated coupling.

[0050] Parameter calibration and validation: Adjust the roughness and pollutant attenuation coefficient of the EFDC model to make the simulated lake water level and COD concentration consistent with the measured data, such as... Figure 13 and Figure 14 As shown.

[0051] Table 2. Parameter values ​​for the lake model Parameter type Model values Parameter type Model values Bottom roughness 0.015 COD attenuation coefficient 0.3 / day Reoxygenation rate constant 3.0 COD decay oxygen half-saturation constant <![CDATA[1 mg / LO 2 ]]> Results and applications: By constructing an integrated source-plant-network-river model, the water quantity and quality connection from the shore to the water can be realized. It can be applied to the improvement measures for surface water quality exceeding the standard caused by shore pipe network or non-point source pollution, such as adding regulating reservoirs, optimizing the layout of LID facilities (such as increasing the infiltration paving area by 10%), and optimizing interception facilities.

[0052] Through the above implementation, an integrated model of water source, network, plant, river and lake was successfully constructed, providing scientific support for the city's water environment management.

[0053] Example 4: This example introduces an integrated source-grid-plant-river-lake model simulation device, characterized in that it includes: The first model processing module is used to determine the boundary conditions and initial conditions of the first model based on the collected rainfall data, water quality data, topographic data and land cover type of the target area, input the boundary conditions and initial conditions of the first model into the pre-constructed surface runoff generation and runoff model of the target area, and output the flow rate and water quality index time series of surface runoff. The second model processing module is used to determine the boundary conditions and initial conditions of the second model based on the time series of surface runoff flow and water quality indicators and the time series of flow and water quality indicators collected at the confluence of the main pipes of the pipeline network, input the boundary conditions and initial conditions of the second model into the pre-constructed pipeline network model of the target area, and output the time series of flow and water quality indicators at the outlet of the pipeline network system. The third model processing module is used to determine the boundary conditions and initial conditions of the third model based on the time series of surface runoff flow and water quality indicators, and the time series of flow and water quality indicators at the outlets of the pipeline network system. The boundary conditions and initial conditions of the third model are then input into the pre-constructed river and lake grid model of the target area to simulate the river and lake hydrodynamics and water quality change process of the target area.

[0054] Example 5: This example describes a computer-readable storage medium storing one or more programs, characterized in that the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method described in Example 1.

[0055] Example 6: A computer device, characterized in that it includes, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing the method described in Embodiment 2.

[0056] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for simulating an integrated model of power sources, grids, plants, rivers, and lakes, characterized in that, include: Based on the collected rainfall data, water quality data, topographic data and land cover type of the target area, the boundary conditions and initial conditions of the first model are determined. The boundary conditions and initial conditions of the first model are then input into the pre-constructed surface runoff generation and runoff model of the target area, and the time series of surface runoff flow and water quality indicators are output. Based on the time series of surface runoff flow and water quality indicators and the time series of flow and water quality indicators collected at the confluence of the main pipes of the pipeline network, the boundary conditions and initial conditions of the second model are determined. The boundary conditions and initial conditions of the second model are then input into the pre-constructed pipeline network model of the target area, and the time series of flow and water quality indicators at the outlet of the pipeline network system are output. Based on the time series of surface runoff flow and water quality indicators, and the time series of flow and water quality indicators at the outlets of the pipeline network system, the boundary conditions and initial conditions of the third model are determined. The boundary conditions and initial conditions of the third model are then input into the pre-constructed river and lake grid model of the target area to simulate the river and lake hydrodynamics and water quality change process in the target area.

2. The integrated source-grid-plant-river-lake model simulation method according to claim 1, characterized in that, The construction of the surface runoff generation and runoff model includes: constructing a surface runoff generation and runoff model for the target area using the SWMM model based on the acquired regional runoff surface data of the target area.

3. The integrated source-grid-plant-river-lake model simulation method according to claim 2, characterized in that, The construction of the pipeline network model of the target area includes: constructing the pipeline network model of the target area using the SWMM model based on the obtained pipeline network topology, outlet location and outlet design parameters of the target area.

4. The integrated source-grid-plant-river-lake model simulation method according to claim 3, characterized in that, The construction of the river and lake grid model in the target area includes: constructing an initial river and lake grid model using the EFDC model based on the acquired river and lake geographic data of the target area; and performing local densification of the source strength area of ​​the initial river and lake grid model based on the acquired pollution source strength data of the target area to obtain the river and lake grid model.

5. The integrated source-grid-plant-river-lake model simulation method according to claim 4, characterized in that, The coupling process between the SWMM model and the EFDC model is as follows: The SWMM model is simulated by calling the SWMM5.dll file using Python software; the binary results of the SWMM model are parsed using the inflow function in the PySWMM library; the runoff and pollutant concentration output by the SWMM model are converted into boundary files that can be recognized by the EFDC model, and the EFDC model is driven to simulate the hydrodynamic and water quality processes of rivers and lakes.

6. The integrated source-grid-plant-river-lake model simulation method according to claim 1, characterized in that, Before using the surface runoff generation and drainage model and pipeline network model of the target area for output, the following is also included: Collect measured flow, water level and water quality data of the target area, and calibrate the surface runoff generation and confluence model and the pipeline network model based on the measured flow, water level and water quality data, including the parameters of surface roughness and pollutant scour index. Adjust the parameters of the surface runoff generation and confluence model and the pipeline network model until the output results of the surface runoff generation and confluence model and the pipeline network model match the measured data. Before outputting the river and lake grid model of the target area, the following is also included: Collect measured flow, water level, and water quality data of the target area. Based on the measured flow, water level, and water quality data, calibrate the parameters of the river and lake grid models, including the roughness coefficient and pollutant attenuation coefficient. Adjust the parameters of the river and lake grid models until the simulation results of the river and lake grid models meet the preset accuracy requirements of the measured data.

7. The integrated source-grid-plant-river-lake model simulation method according to claim 1, characterized in that, The pollution source intensity data for the target area includes: point source water-related data and non-point source water-related data; The point source water-related data includes discrete pollution sources such as wastewater treatment plant effluent, industrial emissions, and overflow emissions. The non-point source water-related data includes precipitation runoff, agricultural non-point source pollution, and urban non-point source pollution.

8. A source-grid-plant-river-lake integrated model simulation device, characterized in that, include: The first model processing module is used to determine the boundary conditions and initial conditions of the first model based on the collected rainfall data, water quality data, topographic data and land cover type of the target area, input the boundary conditions and initial conditions of the first model into the pre-constructed surface runoff generation and runoff model of the target area, and output the flow rate and water quality index time series of surface runoff. The second model processing module is used to determine the boundary conditions and initial conditions of the second model based on the time series of surface runoff flow and water quality indicators and the time series of flow and water quality indicators collected at the confluence of the main pipes of the pipeline network, input the boundary conditions and initial conditions of the second model into the pre-constructed pipeline network model of the target area, and output the time series of flow and water quality indicators at the outlet of the pipeline network system. The third model processing module is used to determine the boundary conditions and initial conditions of the third model based on the time series of surface runoff flow and water quality indicators, and the time series of flow and water quality indicators at the outlets of the pipeline network system. The boundary conditions and initial conditions of the third model are then input into the pre-constructed river and lake grid model of the target area to simulate the river and lake hydrodynamics and water quality change process of the target area.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1 to 7.

10. A computer device, characterized in that, include, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the method of any of claims 1 to 7.

Images