Regional waterlogging early warning method and device based on multi-water professional model, and medium

Abstract

The invention discloses a regional waterlogging early warning method and device based on a multi-water professional model and a medium, and belongs to the technical field of hydrodynamic models. The method comprises the following steps: selecting a research area, and collecting meteorological data, geographic data and hydrological data of the research area; constructing four basic models of the research area based on the data, and coupling the four basic models to construct a waterlogging forecast simulation overall model; importing an input value of historical measured data of the research area into the waterlogging forecasting simulation overall model to output calculation data, and determining parameters of the waterlogging forecasting simulation overall model when the deviation between the calculation data and the output value of the historical measured data meets the requirement; and based on preset rainfall data, performing regional waterlogging forecasting simulation through the waterlogging forecasting simulation overall model, and outputting a drainage pipe network water flow condition, an open channel water flow condition and a waterlogging ponding submerging condition. According to the method, the multi-water professional model is integrated, and the technical effect of co-simulation of the waterlogging evolution process is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of hydrodynamic models, and in particular to a regional waterlogging early warning method, equipment, and medium based on multiple water professional models. Background Art

[0002] Current urban flooding early warning technologies primarily rely on single hydrodynamic models or simple cascaded combinations of models. Mainstream approaches include using numerical meteorological forecasts to drive surface runoff models to predict surface runoff, and then using network hydrodynamic models (such as SWMM) to simulate drainage system loads. Surface waterlogging simulations typically use simplified methods to calculate inundation depths or incorporate GIS spatial analysis to delineate risk areas.

[0003] However, the existing technology has the following technical problems. First, the physical interactions between models are simplified. For example, the impact of river water levels on pipeline drainage is often estimated through preset empirical coefficients, which cannot dynamically reflect the phenomenon of top-up and backflow in real scenarios. Second, the parameter optimization method has a lag, and the model parameters calibrated with historical data are not adaptable enough when encountering complex rainfall patterns, resulting in the water level simulation deviation value of the monitoring station exceeding the error threshold allowed by industry specifications. Finally, key indicators such as pipeline network load status, open channel overflow risk and surface inundation evolution belong to independent calculation modules, and lack a unified temporal and spatial collaborative expression mechanism.

[0004] Therefore, how to integrate multiple water-related professional models to achieve collaborative simulation of the urban waterlogging evolution process has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The embodiments of the present application provide a regional waterlogging early warning method, device, and medium based on multiple water professional models, to solve the following technical problem: how to integrate multiple water professional models to achieve collaborative simulation of the waterlogging evolution process.

[0006] In a first aspect, an embodiment of the present application provides a regional waterlogging warning method based on a multi-water professional model, characterized in that the method includes: selecting a study area and collecting meteorological data, geographic data and hydrological data of the study area; wherein the meteorological data includes meteorological numerical forecast data, the geographic data includes high-precision DEM elevation data, and the hydrological data includes regional rainfall runoff data, external river measured water level data, water system map, river topography data, pipe network flow data and surface data; based on the meteorological data, geographic data and hydrological data, a runoff generation and confluence model, a one-dimensional open channel hydrodynamic model, a one-dimensional pipe network hydrodynamic model and a two-dimensional surface hydrodynamic model of the study area are constructed; wherein the runoff generation and confluence model is used to simulate the process of rainfall being converted into surface or underground runoff, including runoff generation and confluence, the one-dimensional open channel hydrodynamic model is used to simulate the dynamic changes of open channel water flow, and the one-dimensional pipe network hydrodynamic model is used to predict the flow in the pipe network. Water flow parameters, the surface two-dimensional hydrodynamic model is used to simulate the movement of surface water flow; the runoff generation and confluence model, the open channel one-dimensional hydrodynamic model, the pipe network one-dimensional hydrodynamic model and the surface two-dimensional hydrodynamic model are coupled to construct an overall model for waterlogging forecast simulation; the input values ​​of the historical measured data of the study area are imported into the overall model for waterlogging forecast simulation to output the calculated data. When the deviation between the calculated data and the output value of the historical measured data meets the preset requirements, the parameters of the overall model for waterlogging forecast simulation are determined; based on the preset rainfall data, the overall model for waterlogging forecast simulation is used to perform regional waterlogging forecast simulation, and output the drainage network water flow conditions, open channel water flow conditions and waterlogging flooding conditions; among them, the drainage network water flow conditions include pipe liquid level, flow rate and full pipe rate, the open channel water flow conditions include the comparison of open channel water level, flow rate and embankment height, and the waterlogging flooding conditions include surface flooding range, flooding depth and flooding duration.

[0007] In one implementation of the present application, a runoff model, a one-dimensional open channel hydrodynamic model, a one-dimensional pipe network hydrodynamic model and a two-dimensional surface hydrodynamic model of the study area are constructed based on meteorological data, geographic data and hydrological data, specifically including: generating a regional rainwater dataset based on regional rainfall runoff data and high-precision DEM elevation data, dividing the study area into sub-basins through high-precision DEM elevation data, processing the sub-basins through preset rainstorm calculation charts to construct a runoff model; constructing a river network topology and cross-sectional topography based on a water system map and river terrain data, and setting boundary conditions and roughness coefficients to construct a one-dimensional open channel hydrodynamic model; solving the pipe network flow data by simultaneously solving the preset mass balance equation and momentum equation to construct a one-dimensional pipe network hydrodynamic model; and calculating the inundation evolution of the study area through a preset shallow water equation to construct a two-dimensional surface hydrodynamic model.

[0008] In one implementation of the present application, a regional rainwater dataset is generated based on regional rainfall runoff data and high-precision DEM elevation data, the sub-basins of the study area are divided by the high-precision DEM elevation data, and the sub-basins are processed by a preset rainstorm calculation chart to construct a runoff generation and confluence model, specifically including: associating regional rainfall runoff data and high-precision DEM elevation data to generate a regional rainwater dataset; performing depression-filling processing on the high-precision DEM elevation data to determine the surface runoff overflow path; determining the flow direction based on the surface runoff overflow path; extracting the boundaries of the regional water system and sub-basins of the study area based on the flow direction; identifying the watershed characteristic parameters of the sub-basin; wherein the fluid characteristic parameters include average elevation, area, average slope, river length and river gradient; determining the total water volume and unit line of the watershed in the study area based on the rainstorm calculation chart, and processing the characteristic parameters of the sub-basin to determine the unit line lag time, thereby constructing a runoff generation and confluence model.

[0009] In one implementation of the present application, a river network topology and cross-sectional topography are constructed based on a water system map and river channel topography data, and boundary conditions and roughness coefficients are set to construct a one-dimensional hydrodynamic model of an open channel, specifically including: constructing a river network topology based on a water system map; wherein the river network topology includes cross-sectional locations and river section attributes; performing data interpolation on the river channel topography data and cross-sectional locations to generate cross-sectional topography; setting the cross-sectional topography and flow state in the river network topology at the initial moment as initial conditions; initializing the river section roughness coefficient in the river network topology to construct a one-dimensional hydrodynamic model of an open channel.

[0010] In one implementation of the present application, a runoff generation and confluence model, a one-dimensional open channel hydrodynamic model, a one-dimensional pipe network hydrodynamic model and a two-dimensional surface hydrodynamic model are coupled to construct an overall model for waterlogging forecast simulation, specifically including: configuring a data channel between the output end of the runoff generation and confluence model and the boundary input end of the one-dimensional open channel hydrodynamic model, and using the uncontrolled section flow calculated by the runoff generation and confluence model as the flow boundary input of the one-dimensional open channel hydrodynamic model; constructing a levee coupling mechanism between the one-dimensional open channel hydrodynamic model and the two-dimensional surface hydrodynamic model, and simulating the evolution process of the breach flood through water level exchange; constructing a drainage outlet connection channel between the one-dimensional open channel hydrodynamic model and the one-dimensional pipe network model, and dynamically calculating the pipe network support or backflow effect based on the river water level; setting a rainwater outlet data exchange interface between the two-dimensional surface hydrodynamic model and the one-dimensional pipe network model, and realizing the coupling of the overflow and inundation process through the interaction of the surface unit and the pipe network discharge. In one implementation of the present application, historical measured data of the study area are input into the overall model of waterlogging forecast simulation to output calculated data. When the deviation between the calculated data and the output value of the historical measured data meets the preset requirements, the parameters of the overall model of waterlogging forecast simulation are determined, specifically including: inputting historical measured data at the boundary of the overall model of waterlogging forecast simulation to obtain the calculated water level and calculated flow of the monitoring site; comparing the water level deviation value between the calculated water level and the measured water level of the monitoring site; comparing the flow deviation value between the calculated flow and the measured flow; when the water level deviation value and the flow deviation value exceed the preset threshold value, adjusting the riverbed roughness coefficient and the grid infiltration rate parameters and recalculating until the water level deviation value and the flow deviation value are lower than the preset threshold value, so as to output the parameters of the overall model of waterlogging forecast simulation.

[0011] In one implementation of the present application, based on preset rainfall data, regional waterlogging forecast simulation is performed through the overall waterlogging forecast simulation model, and the water flow conditions of the drainage network, the water flow conditions of the open channel and the waterlogging flooding conditions are output, specifically including: inputting the spatiotemporal distribution data of the set rainfall scenario into the overall waterlogging forecast simulation model, and starting the dynamic rolling calculation process; based on the one-dimensional hydrodynamic model of the pipe network, the pipe level change process, flow fluctuation process and full pipe rate status data of the drainage network are output in real time; the one-dimensional hydrodynamic model of the open channel is synchronously run to generate the open channel water level fluctuation process, flow change process and real-time embankment height comparison data set; and the spatial distribution map of the surface inundation range, the contour map of the inundation depth and the heat map of the inundation duration are calculated through the two-dimensional surface hydrodynamic model.

[0012] In one implementation of the present application, the spatiotemporal distribution data of the set rainfall scenario is input into the overall model of the waterlogging forecast simulation, and a dynamic rolling calculation process is started, which specifically includes: dividing the rainfall input data according to a preset time step to construct a continuous calculation time window; synchronously executing the calculation threads of the runoff generation and confluence model, the one-dimensional open channel hydrodynamic model, the one-dimensional pipe network hydrodynamic model and the two-dimensional surface hydrodynamic model in each time window; exchanging coupling interface data and updating boundary conditions; and accumulating and outputting the pipe network status, open channel status and surface inundation data of each time step.

[0013] In a second aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned methods for regional waterlogging warning based on multiple water professional models in the first aspect. In a third aspect, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the regional waterlogging warning method based on multiple water professional models according to any one of the first aspects above is implemented.

[0014] The embodiments of the present application provide a regional waterlogging early warning method, device, and medium based on multiple water professional models, which have at least the following technical effects: By organically coupling a runoff generation and confluence model, a one-dimensional open channel hydrodynamic model, a one-dimensional pipe network hydrodynamic model, and a two-dimensional surface hydrodynamic model, a complete closed-loop simulation of the physical process was constructed. The uncontrolled cross-sectional flow output from the runoff generation and confluence model directly drives the open channel model calculations. The open channel water level and the pipe network outlets interact in real time to provide feedback on the backflow effect, while the surface model simultaneously receives pipe network overflow data and embankment breach information. This fully coupled mechanism overcomes the water balance errors caused by traditional step-by-step calculations, ensuring that the accuracy of pipe network fill rate predictions, the accuracy of identifying risks of over-limit open channel embankment height, and the confidence level of simulated spatial distribution of submerged water depths all meet industry standards.

[0015] A collaborative calibration strategy for the entire model is employed, dynamically adjusting the riverbed roughness coefficient and surface infiltration rate parameters using historical flood data. When the deviation between the calculated and measured water levels at a monitoring station exceeds a preset threshold, a multi-parameter joint optimization process is triggered until the deviations at all monitoring points meet the required accuracy. This approach effectively resolves the parameter combination conflicts associated with traditional single-model calibration, ensuring the spatial rationality of flood range predictions in complex scenarios such as excessive rainfall or sudden pipe blockage.

[0016] Based on a dynamic rolling calculation framework, three types of key early warning data sets are generated simultaneously: drainage network water flow conditions, open channel water flow conditions, and urban waterlogging and inundation conditions, providing full-factor visualization support for urban flood control decision-making.

[0017] During the model validation phase, a reverse testing mechanism based on historical flood events is employed, automatically returning to the parameter optimization phase when the validation error exceeds the specified limit. This closed-loop validation process ensures the technical solution's fault tolerance in the event of data loss or abnormal boundary conditions, avoids the risk of system crashes caused by error propagation, and enhances the long-term operational stability of the business system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flow chart of a regional waterlogging early warning method based on multiple water professional models provided in an embodiment of the present application; Figure 2 A schematic diagram of the internal structure of a regional waterlogging warning device based on multiple water professional models provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The embodiments of the present application provide a regional waterlogging early warning method, device, and medium based on multiple water professional models, to solve the following technical problem: how to integrate multiple water professional models to achieve collaborative simulation of the waterlogging evolution process.

[0021] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This is a flowchart of regional waterlogging warning based on multiple water professional models provided in the embodiment of this application. Figure 1 As shown, the embodiment of the present application provides a regional waterlogging early warning method based on multiple water professional models, which specifically includes the following steps: Step 1: Select the study area and collect meteorological data, geographic data and hydrological data of the study area; meteorological data include meteorological numerical forecast data, geographic data include high-precision DEM elevation data, and hydrological data include regional rainfall runoff data, external river measured water level data, water system map, river topography data, pipe network flow data and surface data.

[0023] Step 1.1: Select the study area The study area is the specific geographical area where waterlogging forecast simulation is to be performed. This area is usually a city, urban area, watershed, or specific project area, and its boundaries need to be clearly defined.

[0024] Based on the needs of urban flooding forecasting, the geographic area for simulation analysis can be clearly defined on a geographic information system (GIS) platform by drawing polygons or importing administrative boundary files. For example, the main urban area of ​​City A can be selected as the study area.

[0025] Step 1.2: Collect weather data Meteorological data refers to data that reflects the state and changes of the atmosphere in the study area.

[0026] Numerical meteorological forecast data refers to data products generated using numerical weather forecast models that predict meteorological elements (such as rainfall, temperature, wind speed and direction) for a period of time in the future (e.g., the next 1-3 days). This data is typically provided by meteorological departments or professional meteorological service agencies.

[0027] Obtain numerical meteorological forecast data covering the study area and a certain range of its surroundings from authoritative meteorological data sources (e.g., meteorological bureaus or weather service companies). This data is typically provided in gridded formats (e.g., GRIB, NetCDF) or as site-specific forecasts. Collected data should include hourly or minute-by-minute rainfall data for the forecast period, which will serve as the primary driving input for subsequent waterlogging simulations.

[0028] Step 1.3: Collect geographic data Geographic data refers to data that describes the surface morphology, location and spatial relationships of the study area.

[0029] High-precision DEM elevation data refers to high-resolution Digital Elevation Model (DEM) data. It records surface elevation information using a regular grid of points and serves as the foundation for topography, flow analysis, and watershed delineation. High precision typically refers to a spatial resolution better than 5 meters (e.g., 1, 2, or 5 meters).

[0030] Obtain high-precision DEM data covering the entire study area. This data can come from aerial photogrammetry, LiDAR scanning, or high-resolution satellite stereo image processing. The data is typically in a raster format (such as GeoTIFF). Before use, the DEM data requires necessary preprocessing, such as coordinate system alignment, format conversion, and outlier handling.

[0031] Step 1.4: Collect hydrological data Hydrological data refers to data that describes the distribution and movement of water bodies in the study area and is related to the water cycle.

[0032] Regional rainfall runoff data refers to historical or measured rainfall events and their corresponding surface or underground runoff (flow) data, which are used to calibrate runoff production and runoff models.

[0033] External river measured water level data refer to the historical or real-time water level data observed at specific locations (such as river estuaries, control stations) of the main rivers connected to the study area, which serve as the model boundary conditions.

[0034] A water system map refers to a vector map that depicts the distribution and connectivity of surface water bodies such as rivers, lakes, and reservoirs within the study area.

[0035] River channel topography data refers to the measurement data that describes the cross-sectional shape of the river channel, riverbed elevation, riverbank location and other information, usually in the form of section point coordinates or section line files.

[0036] Pipeline network flow data refers to the flow monitoring data (historical or real-time) of key nodes (such as pumping stations and inspection wells) in the urban drainage network system, which is used to calibrate the pipeline network model.

[0037] Surface data refers to data describing the surface cover type, land use conditions, soil type, underlying surface parameters (such as roughness, infiltration rate), etc. of the study area. These data affect the runoff generation and runoff process of rainfall.

[0038] For example: obtain historical rainfall and runoff data and external river water level data in the study area and surrounding related sites from hydrological monitoring stations and water management departments.

[0039] Obtain the latest, high-precision water system vector maps (such as Shapefile format) from urban planning, water affairs, or surveying and mapping departments.

[0040] Collect measured river section data, or use high-precision DEM combined with water system maps to extract preliminary river sections, and conduct supplementary field measurements when necessary.

[0041] Obtain drainage network GIS data (including topological information such as pipe diameter, pipe length, elevation, flow direction, etc.) and flow monitoring data of key nodes from the urban drainage management department.

[0042] Collect land use / land cover (LULC) maps, soil type maps, etc., and based on these data or field surveys, determine the model parameters corresponding to different surface types (such as Manning's roughness coefficient and infiltration parameter).

[0043] Step 2: Based on meteorological data, geographic data and hydrological data, construct a runoff generation and confluence model, a one-dimensional open channel hydrodynamic model, a one-dimensional pipe network hydrodynamic model and a two-dimensional surface hydrodynamic model of the study area; among them, the runoff generation and confluence model is used to simulate the process of rainfall converting into surface or underground runoff, including runoff generation and confluence, the one-dimensional open channel hydrodynamic model is used to simulate the dynamic changes of open channel water flow, the one-dimensional pipe network hydrodynamic model is used to predict the water flow parameters in the pipe network, and the two-dimensional surface hydrodynamic model is used to simulate the movement of surface water flow.

[0044] Step 2.1: Generate a regional rainwater dataset based on regional rainfall runoff data and high-precision DEM elevation data. Divide the study area into sub-basins using the high-precision DEM elevation data. Process the sub-basins using a preset rainstorm calculation chart to construct a runoff generation and confluence model.

[0045] Runoff generation and confluence models are process models used to simulate how rainfall is converted into surface runoff (runoff generation) and how runoff is collected and flows on slopes and in rivers (confluence).

[0046] A sub-basin refers to the smallest hydrological unit divided based on terrain and with an independent water flow path.

[0047] Rainstorm calculation charts refer to charts or calculation methods produced based on regional hydrological analysis or specifications (such as design rainstorm intensity formulas, hydrological manuals), which are used to estimate the basin flow rate (or design flood process line) under rainstorms of a specific frequency.

[0048] The unit line refers to the outlet section flow process line formed by the unit net rainfall (such as 1 mm) falling evenly on the basin within a unit time (such as 1 hour). It is the core tool for confluence calculation.

[0049] Step 2.1.1 Associate regional rainfall-runoff data with high-precision DEM elevation data to generate a regional rainwater dataset.

[0050] In GIS software, the collected regional rainfall runoff data (such as rain gauge station locations and rainfall processes) are spatially associated with high-precision DEM data. For example, the rain gauge data are interpolated onto the DEM grid or rain gauges are assigned to each sub-basin to form a regional rainfall dataset that can be used for model calculations.

[0051] Step 2.1.2: Perform depression-filling processing on the high-precision DEM elevation data to determine the overland flow path of surface runoff.

[0052] Performing a fill-sink process on high-precision DEM data. Real terrain contains depressions (depressions). Filling is the process of correcting these tiny depressions to ensure continuous water flow, thereby accurately determining the overland flow path of surface runoff.

[0053] Step 2.1.3 determines the flow direction based on the overland flow path.

[0054] Based on the DEM after filling, the flow direction algorithm (such as D8 and D∞ algorithms) is used to calculate the flow direction (FlowDirection) of each grid cell, that is, the flow direction of the water.

[0055] Step 2.1.4 extracts the boundaries of regional water systems and sub-watersheds in the study area based on water flow direction.

[0056] Based on the flow direction data, the FlowAccumulation algorithm was used, with a set flow accumulation threshold, to extract the river network (regional river system) of the study area. Then, based on the river network and flow direction, the WatershedDelineation algorithm was used to divide the study area into several sub-watersheds and determine the boundaries of each sub-watershed.

[0057] Step 2.1.5 identifies the watershed characteristic parameters of the sub-watershed; wherein the flow characteristic parameters include average elevation, area, average slope, river channel length, and river channel gradient.

[0058] For each divided sub-basin, its basin characteristic parameters are calculated based on DEM and water system data. These parameters include: Mean elevation: The average of the elevations of all grid cells within a sub-basin.

[0059] Area: The total catchment area of ​​the sub-basin.

[0060] Average slope: The average slope of all grid cells in a sub-basin.

[0061] River channel length: the length of the main river channel in the sub-basin.

[0062] River channel gradient: the ratio of the elevation difference between the upstream and downstream of the main river channel in a sub-basin to the length of the river channel.

[0063] Step 2.1.6: Determine the total water volume and unit line of the study area based on the rainstorm calculation chart, and process the characteristic parameters of the sub-basin to determine the unit line lag time, thereby constructing the runoff generation and confluence model.

[0064] Based on the hydrological zoning or relevant regulations for the study area, select an appropriate rainstorm calculation chart (or design rainstorm formula). Using this chart and the selected design rainstorm (or frequency), calculate the design net rainfall (total water volume) and design flood hydrograph (or unit hydrograph) for the study area (or each sub-basin). Combined with the sub-basin characteristic parameters calculated in Step 2.1.5 (particularly area, channel length, and channel gradient), apply empirical formulas (such as the delay formula in the SCS method) to calculate the unit hydrograph delay for each sub-basin. Input the sub-basin delineation, unit hydrograph (or unit hydrograph parameters), and delay into hydrological modeling software (such as HEC-HMS) to complete the construction of the runoff generation and confluence model. This model, when fed with rainfall data, outputs the flow profile at each sub-basin outlet or channel section.

[0065] Step 2.2: Based on the river system map and river topography data, construct the river network topology and cross-sectional topography, and set boundary conditions and roughness coefficients to construct a one-dimensional hydrodynamic model of the open channel.

[0066] One-dimensional open channel hydrodynamic model: A mathematical model used to simulate the dynamic changes (water level, flow) of water flow in open channels such as rivers and canals along the river channel direction (one dimension), based on the solution of the Saint-Venant equations.

[0067] River network topology: describes the structural relationship of how each river section (Link) in the river network is connected to each other through nodes (Node), including the starting point, end point, length, flow direction and cross-section location information of the river section.

[0068] Cross-sectional topography refers to the geometric shape data of the river cross section, that is, the changes in riverbed elevation and width perpendicular to the centerline of the river at different locations (mileages).

[0069] Boundary conditions refer to the water flow conditions that need to be set at the boundaries of the model calculation area. Common ones include upstream flow boundaries and downstream water level boundaries.

[0070] The roughness coefficient refers to the empirical coefficient that reflects the resistance of the riverbed and riverbank to water flow (such as the Manning coefficient n), which directly affects the water flow velocity and energy loss.

[0071] Step 2.2.1: Construct a river network topology based on the water system map; the river network topology includes section locations and river segment attributes.

[0072] Import a river network vector diagram into professional hydrodynamic modeling software (such as MIKE11 or HEC-RAS). Based on the diagram, draw or import the river centerlines. Construct the river network topology by defining nodes (such as river intersections and endpoints) and connecting the river segments between them. Also, set the locations of cross-sections along the river segments (e.g., at regular intervals or at terrain changes).

[0073] Step 2.2.2: Perform data interpolation on the river channel topography data and cross-section locations to generate cross-section topography.

[0074] Match the collected river channel topography data (measured cross-section point data) with the cross-section locations set in step 2.2.1. For cross-section locations without measured data, use spatial interpolation methods (such as linear interpolation and spline interpolation) to generate cross-section topography data for that location based on the adjacent measured cross-section data.

[0075] Step 2.2.3: Set the cross-sectional topography and flow state in the river network topology at the initial moment as the initial conditions.

[0076] Set the initial state for the model calculation. Usually, a relatively stable (no drastic changes) period of water flow is selected, and the water level and flow rate of each section at this time (which can be obtained from historical data or estimates) are used as the initial conditions for the model calculation.

[0077] Step 2.2.4: Initialize the river section roughness coefficient in the river network topology to construct a one-dimensional hydrodynamic model of the open channel.

[0078] Based on the river's geomorphological characteristics (such as riverbed material and vegetation cover) and bank type, refer to relevant manuals or empirical values ​​to set the initial roughness coefficient (Manning's n) for each river section in the river network. After completing these settings, create a one-dimensional open channel hydrodynamic model in the software.

[0079] Step 2.3: Solve the pipe network flow data by simultaneously solving the preset mass balance equation and momentum equation to construct a one-dimensional hydrodynamic model of the pipe network.

[0080] The one-dimensional hydrodynamic model of the pipe network is a mathematical model used to simulate the internal water flow movement (water level, flow rate, flow velocity) of the urban drainage pipe network (pipes, inspection wells, etc.). It is usually solved based on the one-dimensional Saint-Venant equations or simplified equations (such as dynamic waves and diffusion waves).

[0081] The mass balance equation describes the physical law that states that the amount of water flowing into and out of a node (such as a manhole) in a pipe network must be conserved.

[0082] The momentum equation is a physical law that describes the balance between driving forces (pressure gradient, gravity) and resistance (friction) when water moves in a pipe.

[0083] In urban drainage modeling software (such as SWMM and Mikeruban), collected GIS data for the drainage network (such as pipe diameter, length, bottom elevation, node elevation, and connection relationships) is imported to construct the physical topology of the network. The software's core solver simultaneously solves the mass balance equation and momentum equation (or simplified versions thereof) for all nodes and segments in the network to calculate the temporal evolution of flow parameters such as flow rate, water level (depth), and velocity at each location in the network under varying rainfall inflows or boundary conditions. Once the network geometry data is input and basic settings are completed, a one-dimensional hydrodynamic model of the network is constructed.

[0084] Step 2.4: Calculate the inundation evolution of the study area using the preset shallow water equation to construct a two-dimensional surface hydrodynamic model.

[0085] The two-dimensional surface hydrodynamic model is a mathematical model used to simulate the flow, diffusion and inundation of water on the surface (two-dimensional plane) under conditions such as rainfall or dam breach. It is usually solved based on a two-dimensional shallow water equation set.

[0086] The shallow water equations are physical equations that describe the flow of relatively shallow water bodies (such as floodplains). They include the conservation of mass and momentum equations, and take into account the movement of water in two directions (x, y) on the plane.

[0087] Inundation evolution calculation refers to the process of simulating the diffusion of floods on the surface over time, changes in inundation range and water depth distribution.

[0088] Import high-precision DEM data of the study area into 2D hydrodynamic modeling software (such as MIKE21, TUFLOW, or HEC-RAS2D) as the terrain foundation. Based on the required simulation accuracy and computing resources, divide the study area surface into a regular grid (such as a square grid) or an irregular triangular grid (TIN). Based on shallow water equations, the software calculates the temporal evolution of water depth and flow velocity for each grid cell, given the specified rainfall input, boundary conditions (such as river breach flow and pipe network overflow), and initial conditions. This simulates the movement, diffusion, and inundation evolution of surface water flows.

[0089] Step 3: Couple the runoff generation and confluence model, the open channel one-dimensional hydrodynamic model, the pipe network one-dimensional hydrodynamic model, and the surface two-dimensional hydrodynamic model to construct an overall model for waterlogging forecast simulation.

[0090] Coupling refers to connecting multiple independent models so that they can exchange data in real time or on demand and work together to simulate more complex and realistic system behaviors.

[0091] The overall model for urban flood forecasting simulation refers to an integrated model system formed by coupling multiple sub-models, which can comprehensively simulate the entire process from rainfall runoff, river / pipeline water delivery to surface inundation.

[0092] Step 3.1. Configure the data channel between the output of the runoff generation and confluence model and the boundary input of the open channel one-dimensional hydrodynamic model, and use the uncontrolled section flow calculated by the runoff generation and confluence model as the flow boundary input of the open channel one-dimensional hydrodynamic model. Configure the data transfer interface between the runoff generation and confluence model and the open channel 1D hydrodynamic model. Specifically, the flow hydrograph (Qt) of an uncontrolled section (i.e., a natural section without hydraulic engineering control) located at the upstream headwaters or where a tributary flows into the mainstream, calculated by the runoff generation and confluence model, is used as the upstream flow boundary condition (BoundaryCondition) at the corresponding location in the open channel 1D hydrodynamic model. This ensures that the primary source of river flow (from overland confluence) drives the open channel model calculations.

[0093] Step 3.2: Construct a levee coupling mechanism between the open channel one-dimensional hydrodynamic model and the surface two-dimensional hydrodynamic model, and simulate the evolution of the levee breach flood through water level exchange. A levee coupling mechanism is established between the 1D open channel hydrodynamic model and the 2D surface hydrodynamic model. Coupling links are set along the river levees (or sections of the river where overflow or breach is likely). During the simulation, the open channel model calculates the water level at that location in real time. When the river water level exceeds the preset levee elevation (levee height), an overflow or breach is considered to have occurred. At this point, the open channel model transmits the water level information (or the overflow volume calculated based on the water level difference) to the 2D surface model. After receiving this information, the 2D surface model sets inflow boundary conditions (such as water level boundaries or flow boundaries) at the corresponding grid locations to simulate the spreading and inundation of floodwaters on the surface after overflowing from the river channel. This water level exchange mechanism dynamically simulates the evolution of the breach flood.

[0094] Step 3.3: Construct a drainage outlet connection channel between the one-dimensional hydrodynamic model of the open channel and the one-dimensional hydrodynamic model of the pipe network, and dynamically calculate the pipe network support or backflow effect based on the river water level.

[0095] Establish outlet connections between the one-dimensional open channel hydrodynamic model and the one-dimensional pipe network model. In urban drainage pipe networks, rainwater typically drains into the river through outlets. These outlets are clearly located in the model. During the simulation, the open channel model calculates the river water level at the outlet locations in real time. The pipe network model considers the influence of the river water level at the outlet nodes when calculating the water level at the outlet nodes: If the river water level is lower than the drainage outlet elevation, drainage will be smooth.

[0096] If the water level in the river is higher than the elevation of the drainage outlet, it will have a backwater effect on the drainage of the pipe network, reducing the drainage efficiency or even completely preventing drainage.

[0097] In extreme cases (such as when the river water level is much higher than the drain outlet and the water level in the pipe network is low), river water may flow back into the pipe network through the drain outlet.

[0098] The pipe network model dynamically adjusts the boundary conditions at the outlet (such as setting it to the water level boundary) based on the real-time river water level data received, thereby accurately simulating the supporting or backflow effect of the river water level on the pipe network drainage.

[0099] Step 3.4: Set up the gully data exchange interface between the two-dimensional surface hydrodynamic model and the one-dimensional pipe network model, and realize the coupling of the overflow and inundation process through the interaction between the surface unit and the pipe network discharge.

[0100] Set up a gully data exchange interface between the 2D surface hydrodynamic model and the 1D pipe network model. On the urban surface, rainwater is collected through inlets (gullies) and enters the drainage network. In the model, establish a correspondence between surface grid cells and pipe network gully nodes (usually based on spatial location). During the simulation: The 2D surface model calculates the depth of surface water in each grid cell.

[0101] The network model calculates the drainage capacity of each gully node (subject to the hydraulic conditions of the network).

[0102] Data is exchanged between the two models: the surface model transmits the surface water depth of the grid cell to the corresponding gully node, which serves as the inflow boundary condition of the pipe network model (part of the rainfall runoff). At the same time, the pipe network model feeds back the actual discharge volume (or remaining water storage capacity) of the gully node to the surface model.

[0103] The surface model adjusts the surface water accumulation calculation for that grid cell based on the actual drainage volume (or inflow limit) it receives. If drainage capacity is insufficient, the surface model simulates increased water accumulation or even overflow. This interaction dynamically couples surface overflow with the network's drainage process.

[0104] Step 4: Import the input values ​​of the historical measured data of the study area into the overall model of waterlogging forecast simulation to output calculated data. When the deviation between the calculated data and the output values ​​of the historical measured data meets the preset requirements, determine the parameters of the overall model of waterlogging forecast simulation.

[0105] Calibration refers to the process of adjusting the parameters of the model so that when the model inputs known historical event data, its output results (such as water level, flow) can be as close as possible to the actual observed values ​​of the event.

[0106] The preset threshold refers to the pre-set error range used to judge whether the degree of consistency between the model simulation results and the measured data is acceptable (such as water level error ±0.1m, flow error ±15%).

[0107] Step 4.1: Input historical measured data into the boundary of the overall waterlogging forecast simulation model to obtain the calculated water level and calculated flow at the monitoring station.

[0108] Select one or more representative historical rainfall flooding events within the study area. Use actual meteorological data (particularly the spatial and temporal distribution of rainfall) and external river water level data during the event as inputs to the integrated flooding forecast simulation model constructed in Step 3. After the model runs, it outputs the calculated water level and flow rate at each monitoring station (water level station and flow rate station) within the study area.

[0109] Step 4.2: Compare the calculated water level with the water level deviation value measured at the monitoring station.

[0110] For each water level monitoring station, the model-calculated water level process line is compared with the measured water level process line at that station. The deviation between the two at key time points (such as flood peaks) or throughout the entire process (such as root mean square error (RMSE) and mean absolute error (MAE)) is calculated, which is the water level deviation value.

[0111] Step 4.3: Compare the flow deviation value between the calculated flow rate and the measured flow rate.

[0112] For each flow monitoring site (e.g., a river control section or a key node in the pipeline network), compare the flow process line calculated by the model with the measured flow process line at that site. Calculate the deviation between the two at key time points or throughout the entire process (e.g., RMSE, MAE, NSE Nash efficiency coefficient), which is the flow deviation value.

[0113] Step 4.4: When the water level deviation and flow rate deviation exceed the preset threshold, adjust the riverbed roughness coefficient and grid infiltration rate parameters and recalculate until the water level deviation and flow rate deviation are lower than the preset threshold, so as to output the parameters of the overall model for waterlogging forecast simulation.

[0114] Evaluate the water level deviation and flow deviation values ​​at all monitoring stations. If these deviations exceed pre-set thresholds (indicating poor model simulation), key model parameters need to be adjusted. The most commonly adjusted parameters include: Riverbed roughness coefficient: affects the water flow velocity in rivers and open channels, and thus affects the shape, peak value and time of water level and flow process lines.

[0115] Grid infiltration rate parameter: affects the rate at which rainwater infiltrates into the soil in the two-dimensional surface model, thereby affecting After adjusting the parameters, rerun the model (steps 4.1-4.3) and recalculate the deviation values. Repeat this iterative process until the water level deviation and flow deviation values ​​at all monitoring stations are below the preset thresholds. At this point, the model parameters are considered reasonably calibrated. These finalized parameter values ​​constitute the parameter set for the overall waterlogging forecast simulation model.

[0116] Step 5. Based on the preset rainfall data, regional waterlogging forecast simulation is performed through the overall waterlogging forecast simulation model, and the water flow conditions of the drainage network, the water flow conditions of the open channel, and the waterlogging and inundation conditions are output; among which, the water flow conditions of the drainage network include the pipe liquid level, flow rate, and full pipe rate, the water flow conditions of the open channel include the comparison of the open channel water level, flow rate, and embankment height, and the waterlogging and inundation conditions include the surface inundation range, inundation depth, and inundation duration.

[0117] The preset rainfall data refers to the rainfall input used to drive the waterlogging forecast simulation, which can be measured rainfall, forecast rainfall or designed rainstorm scenario.

[0118] The dynamic rolling calculation process means that during the simulation process, the model gradually advances the calculation according to the set time step, and processes the input, performs calculations, exchanges coupling data and outputs the results within each time step.

[0119] The time window refers to the length of the time period covered by each calculation in the dynamic rolling calculation.

[0120] Step 5.1: Input the spatiotemporal distribution data of the set rainfall scenario into the overall model of waterlogging forecast simulation and start the dynamic rolling calculation process.

[0121] Input the rainfall scenario data to be simulated (for example, the rainfall distribution data for the next 6 hours from the meteorological numerical forecast) into the calibrated (step 4) overall model for waterlogging forecast simulation, and start the dynamic simulation calculation of the model.

[0122] Step 5.1.1: Split the rainfall input data into preset time steps to construct continuous calculation time windows.

[0123] The continuous rainfall input data is divided into preset calculation time steps (such as Δt = 1 minute, 5 minutes). The model calculation proceeds step by step according to this time step, and each time step constitutes a calculation time window.

[0124] Step 5.1.2: Synchronously execute the calculation threads of the runoff generation and confluence model, the open channel one-dimensional hydrodynamic model, the pipe network one-dimensional hydrodynamic model, and the surface two-dimensional hydrodynamic model within each time window. Within each time window (i.e., each time step), the model system synchronously executes the computational threads of four core sub-models: The runoff generation and confluence model calculates the slope runoff generation and the flow confluenced into the river channel for the current step.

[0125] The one-dimensional hydrodynamic model of the open channel calculates the water level and flow rate at each section of the river channel at the current step length.

[0126] The one-dimensional hydrodynamic model of the pipe network calculates the water level, flow rate and full pipe status of each node in the pipe network at the current step.

[0127] The surface two-dimensional hydrodynamic model calculates the water depth, flow velocity and inundation state of each grid cell on the surface at the current step.

[0128] Step 5.1.3: Exchange coupling interface data and update boundary conditions. After each submodel completes its calculations for the current time step, immediately exchange data (e.g., flow boundaries for the open channel from the source and confluence, 2D dam-break levels / flows for the surface from the open channel, river levels for the pipe network from the open channel, 2D stormwater discharge from the pipe network to the surface / 2D ponding depth for the pipe network from the surface) through the coupling interfaces established in Step 3 (3.1-3.4). This exchanged data is used to update the boundary conditions required for each model at the next time step.

[0129] Step 5.1.4: Cumulatively output the pipe network status, open channel status, and surface inundation data for each time step.

[0130] At the end of each time step, the key status data at that moment is recorded and accumulated and output, including: Pipeline network status: liquid level (water depth), flow rate, and full pipe rate (water depth / pipe diameter) of each pipeline.

[0131] Open channel status: water level and flow of each section, as well as the comparison between the real-time water level and the embankment elevation (embankment height comparison).

[0132] Surface inundation data: whether each grid cell is inundated and the depth of the inundation.

[0133] Step 5.2: Based on the one-dimensional hydrodynamic model of the pipe network, the pipe level change process, flow fluctuation process and full pipe rate status data of the drainage pipe network are output in real time. During simulation, the one-dimensional hydrodynamic model of the network provides real-time data on the time-varying flow levels (water depths) at key locations (or across all pipelines) within the entire drainage network, flow fluctuations, and pipe fill rates (indicating the degree of pipe fullness). This data can be used to identify bottlenecks and potential overflow points within the network.

[0134] Step 5.3: Synchronously run the open channel one-dimensional hydrodynamic model to generate the open channel water level fluctuation process, flow change process, and real-time embankment height comparison data set. During the simulation, a one-dimensional open channel hydrodynamic model was run simultaneously, generating water level and flow profiles for each control section of the main river channel within the study area. Simultaneously, the model calculated and output, in real time, the difference between the water level at each section and the corresponding levee elevation (levee height comparison), visually indicating which river sections are at risk of overtopping or breaching.

[0135] Step 5.4: Generate a spatial distribution map of the surface inundation range, a contour map of the inundation depth, and a thermal map of the inundation duration using a two-dimensional surface hydrodynamic model.

[0136] During the simulation, the two-dimensional surface hydrodynamic model is continuously calculated, and finally a spatial distribution result is generated that reflects the surface inundation situation of the entire study area: The spatial distribution map of surface flooding range uses different colors or fills to indicate the area of ​​the surface flooded at the end of the simulation (or at a specified time).

[0137] Contour maps of flooded water depths draw lines of equal depth on a map to clearly show the distribution of water depths at different locations within the flooded area.

[0138] The inundation duration heat map uses color depth (heat) to indicate how long each surface location was flooded throughout the rainfall event, helping to identify areas of long-term waterlogging.

[0139] In a specific case, the flooding risk of urban area B when it encounters a preset short-duration heavy rain (for example, rainfall reaches 80 mm in 1 hour) is simulated.

[0140] Urban district B is traversed by a major river, River C. In the downstream section of River C (Section D) that passes through the urban center, the terrain along both banks is low, and the levees are relatively low in flood control standards. To assess the impact of overflowing banks in this section of the river during extreme rainstorms on the urban area, the overall model for urban waterlogging forecasting and simulation was specifically strengthened with levee coupling.

[0141] In the one-dimensional hydrodynamic model of the open channel, the cross-sectional topography of river section D was accurately constructed. Based on geological survey and embankment design data, the crest elevations of the embankments on the left and right banks of this river section were accurately set in the cross-sectional attributes of the model.

[0142] In the two-dimensional surface hydrodynamic model, high-precision DEM data were used to generate a fine computational grid (e.g., a 10 m x 10 m grid) covering the urban areas on both sides of river section D (areas that may be affected by floods).

[0143] In the model coupling setup, a bank coupling connection line was precisely drawn on the GIS platform along the direction of the levees on both sides of river section D. This line defines the spatial location where water level exchange occurs.

[0144] During the model run (step 5.1): At the end of each calculation time step (e.g. 1 minute), the open channel 1D model calculates the water level at the preset coupling points (spaced along the embankment line) on the river section D.

[0145] When the river water level calculated at a coupling point exceeds the embankment elevation (embankment height) corresponding to that point, the open channel model determines that overflow has occurred at this point.

[0146] The open channel model immediately transmits the real-time water level value of this coupling point (or the estimated overflow volume calculated based on the water level exceeding the dike height) to the surface two-dimensional model.

[0147] After receiving the water level information at the coupling point, the 2D surface model sets a water level boundary condition (or flow boundary condition) on the corresponding grid boundary. This boundary condition represents that the flood is overflowing the embankment and pouring into the urban area.

[0148] Based on this boundary condition, the two-dimensional surface model combines the terrain and water flow movement calculations within the grid to simulate the dynamic process of flood diffusion path, deepening and expansion of the urban surface after the flood flows in from this coupling point.

[0149] This process performs judgment and data exchange at all coupling points at each time step, dynamically simulating the entire process of floods breaking through multiple points along the embankment line and gradually inundating low-lying areas in the urban area.

[0150] Output: After the simulation, the spatial distribution map of the surface inundation generated in Step 5.4 shows that due to multiple overtopping of banks in River Section D, floodwaters influx caused widespread waterlogging in low-lying urban areas downstream (such as Old Town District E and Riverside Park F). A contour map of inundation depth further shows that the maximum inundation depth in some areas of District E exceeded 1.5 meters. A heat map of inundation duration indicates that Park F, due to its low-lying terrain and poor drainage, experienced a slow receding of water, resulting in inundation lasting over 12 hours.

[0151] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a regional waterlogging warning device based on multiple water professional models, the structure of which is as follows: Figure 2 shown.

[0152] Figure 2 This is a schematic diagram of the internal structure of a regional waterlogging warning device based on a multi-water professional model provided in an embodiment of the present application. Figure 2 As shown, the equipment includes: at least one processor 201; and, a memory 202 communicatively coupled to the at least one processor; The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to: Select a study area and collect meteorological data, geographic data, and hydrological data for the study area; meteorological data includes numerical meteorological forecast data, geographic data includes high-precision DEM elevation data, and hydrological data includes regional rainfall and runoff data, measured water level data of external rivers, river system maps, river topography data, pipe network flow data, and surface data; Based on meteorological data, geographic data, and hydrological data, a runoff generation and confluence model, a one-dimensional open channel hydrodynamic model, a one-dimensional pipe network hydrodynamic model, and a two-dimensional surface hydrodynamic model were constructed for the study area. The runoff generation and confluence model simulates the process of converting rainfall into surface or underground runoff, including runoff generation and confluence. The one-dimensional open channel hydrodynamic model simulates the dynamic changes of open channel water flow. The one-dimensional pipe network hydrodynamic model predicts water flow parameters within the pipe network. The two-dimensional surface hydrodynamic model simulates surface water flow movement. Couple the runoff generation and confluence model, the open channel one-dimensional hydrodynamic model, the pipe network one-dimensional hydrodynamic model, and the surface two-dimensional hydrodynamic model to construct an overall model for waterlogging forecast simulation; Importing the input values ​​of historical measured data of the study area into the overall model of waterlogging forecast simulation to output calculated data. When the deviation between the calculated data and the output values ​​of the historical measured data meets the preset requirements, the parameters of the overall model of waterlogging forecast simulation are determined. Based on the preset rainfall data, regional waterlogging forecast simulation is carried out through the overall waterlogging forecast simulation model, and the water flow conditions of the drainage network, the water flow conditions of the open channel and the waterlogging and inundation conditions are output; among them, the water flow conditions of the drainage network include the pipeline liquid level, flow and full pipe rate, the water flow conditions of the open channel include the comparison of the open channel water level, flow and embankment height, and the waterlogging and inundation conditions include the surface inundation range, inundation depth and inundation duration.

[0153] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for regional waterlogging early warning based on multiple water professional models stores computer executable instructions, wherein the computer executable instructions are set as follows: Select a study area and collect meteorological data, geographic data, and hydrological data for the study area; meteorological data includes numerical meteorological forecast data, geographic data includes high-precision DEM elevation data, and hydrological data includes regional rainfall and runoff data, measured water level data of external rivers, river system maps, river topography data, pipe network flow data, and surface data; Based on meteorological data, geographic data, and hydrological data, a runoff generation and confluence model, a one-dimensional open channel hydrodynamic model, a one-dimensional pipe network hydrodynamic model, and a two-dimensional surface hydrodynamic model were constructed for the study area. The runoff generation and confluence model simulates the process of converting rainfall into surface or underground runoff, including runoff generation and confluence. The one-dimensional open channel hydrodynamic model simulates the dynamic changes of open channel water flow. The one-dimensional pipe network hydrodynamic model predicts water flow parameters within the pipe network. The two-dimensional surface hydrodynamic model simulates surface water flow movement. Couple the runoff generation and confluence model, the open channel one-dimensional hydrodynamic model, the pipe network one-dimensional hydrodynamic model, and the surface two-dimensional hydrodynamic model to construct an overall model for waterlogging forecast simulation; Importing the input values ​​of historical measured data of the study area into the overall model of waterlogging forecast simulation to output calculated data. When the deviation between the calculated data and the output values ​​of the historical measured data meets the preset requirements, the parameters of the overall model of waterlogging forecast simulation are determined. Based on the preset rainfall data, regional waterlogging forecast simulation is carried out through the overall waterlogging forecast simulation model, and the water flow conditions of the drainage network, the water flow conditions of the open channel and the waterlogging and inundation conditions are output; among them, the water flow conditions of the drainage network include the pipeline liquid level, flow and full pipe rate, the water flow conditions of the open channel include the comparison of the open channel water level, flow and embankment height, and the waterlogging and inundation conditions include the surface inundation range, inundation depth and inundation duration.

[0154] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0155] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0156] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0160] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0161] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0162] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0163] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0164] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A regional waterlogging early warning method based on multiple water professional models, characterized by: The method comprises: Select a study area and collect meteorological data, geographic data, and hydrological data for the study area; wherein the meteorological data includes numerical meteorological forecast data, the geographic data includes high-precision DEM elevation data, and the hydrological data includes regional rainfall runoff data, measured water level data of external rivers, water system maps, river topography data, pipe network flow data, and surface data; Based on the meteorological data, geographic data, and hydrological data, a runoff generation and confluence model, a one-dimensional open channel hydrodynamic model, a one-dimensional pipe network hydrodynamic model, and a two-dimensional surface hydrodynamic model of the study area are constructed; wherein the runoff generation and confluence model is used to simulate the process of converting rainfall into surface or underground runoff, including runoff generation and confluence; the one-dimensional open channel hydrodynamic model is used to simulate the dynamic changes of open channel water flow; the one-dimensional pipe network hydrodynamic model is used to predict water flow parameters in the pipe network; and the two-dimensional surface hydrodynamic model is used to simulate the movement of surface water flow; The runoff generation and confluence model, the one-dimensional open channel hydrodynamic model, the one-dimensional pipe network hydrodynamic model and the two-dimensional surface hydrodynamic model are coupled to construct an overall model for waterlogging forecast simulation; Importing input values ​​of historical measured data of the study area into the overall waterlogging forecast simulation model to output calculated data, and determining parameters of the overall waterlogging forecast simulation model when a deviation between the calculated data and the output value of the historical measured data meets a preset requirement; Based on the preset rainfall data, regional waterlogging forecast simulation is performed through the overall waterlogging forecast simulation model, and the water flow conditions of the drainage network, the water flow conditions of the open channel and the waterlogging and inundation conditions are output; wherein, the water flow conditions of the drainage network include the pipeline liquid level, flow rate and full pipe rate, the water flow conditions of the open channel include the comparison of the open channel water level, flow rate and embankment height, and the waterlogging and inundation conditions include the surface inundation range, inundation depth and inundation duration.

2. A regional waterlogging early warning method based on multiple water professional models according to claim 1, characterized in that: Based on the meteorological data, geographic data and hydrological data, a runoff model, a one-dimensional open channel hydrodynamic model, a one-dimensional pipe network hydrodynamic model and a two-dimensional surface hydrodynamic model of the study area are constructed, specifically including: Generate a regional rainwater dataset based on the regional rainfall runoff data and high-precision DEM elevation data, divide the study area into sub-basins using the high-precision DEM elevation data, and process the sub-basins using a preset rainstorm calculation chart to construct a runoff generation and confluence model; Constructing a river network topology and cross-sectional topography based on the water system map and the river topography data, and setting boundary conditions and roughness coefficients to construct a one-dimensional hydrodynamic model of an open channel; Solving the pipe network flow data by simultaneously solving the preset mass balance equation and momentum equation to construct a one-dimensional hydrodynamic model of the pipe network; The inundation evolution of the study area is calculated using a preset shallow water equation to construct a two-dimensional surface hydrodynamic model.

3. A regional waterlogging early warning method based on multiple water professional models according to claim 2, characterized in that: Generate a regional rainwater dataset based on the regional rainfall runoff data and high-precision DEM elevation data, divide the study area into sub-basins using the high-precision DEM elevation data, and process the sub-basins using a preset rainstorm calculation chart to construct a runoff generation and confluence model, specifically including: Associating the regional rainfall runoff data with high-precision DEM elevation data to generate a regional rainwater dataset; Performing depression filling processing on the high-precision DEM elevation data to determine the surface runoff flow path; determining a water flow direction based on the surface runoff overland flow path; Extracting the boundaries of regional water systems and sub-watersheds in the study area based on the water flow direction; Identifying the watershed characteristic parameters of the sub-watershed; wherein the fluid characteristic parameters include average elevation, area, average slope, river channel length and river channel gradient; The total water volume and unit line of the watershed in the study area are determined based on the rainstorm calculation chart, and the characteristic parameters of the sub-watershed are processed to determine the unit line hysteresis, thereby constructing the runoff generation and confluence model.

4. A regional waterlogging early warning method based on multiple water professional models according to claim 2, characterized in that: Based on the water system map and the river topography data, a river network topology and cross-sectional topography are constructed, and boundary conditions and roughness coefficients are set to construct a one-dimensional hydrodynamic model of an open channel, specifically including: Constructing a river network topology structure based on the water system map; wherein the river network topology structure includes section locations and river section attributes; Performing data interpolation on the river channel topography data and the cross-section position to generate cross-section topography; The cross-sectional topography and flow state in the river network topology at the initial moment are set as initial conditions; The roughness coefficient of the river section in the river network topology is initialized to construct a one-dimensional hydrodynamic model of the open channel.

5. A regional waterlogging early warning method based on multiple water professional models according to claim 1, characterized in that: The runoff generation and confluence model, the one-dimensional open channel hydrodynamic model, the one-dimensional pipe network hydrodynamic model, and the two-dimensional surface hydrodynamic model are coupled to construct an overall model for waterlogging forecast simulation, specifically including: Configuring a data channel between the output end of the runoff generation and confluence model and the boundary input end of the open channel one-dimensional hydrodynamic model, and using the uncontrolled section flow calculated by the runoff generation and confluence model as the flow boundary input of the open channel one-dimensional hydrodynamic model; Construct a levee coupling mechanism between a one-dimensional open channel hydrodynamic model and a two-dimensional surface hydrodynamic model, and simulate the evolution of a levee breach flood through water level exchange; Construct a drainage outlet connection channel between the one-dimensional hydrodynamic model of the open channel and the one-dimensional hydrodynamic model of the pipe network, and dynamically calculate the pipe network's jacking or backflow effects based on the river water level; A rainwater inlet data exchange interface is set up between the two-dimensional surface hydrodynamic model and the one-dimensional hydrodynamic model of the pipe network, and the overflow and inundation process is coupled through the interaction between the surface unit and the pipe network discharge.

6. A regional waterlogging early warning method based on multiple water professional models according to claim 1, characterized in that: Importing historical measured data of the study area into the overall waterlogging forecast simulation model to output calculated data, and when the deviation between the calculated data and the output value of the historical measured data meets a preset requirement, determining the parameters of the overall waterlogging forecast simulation model, specifically including: Inputting historical measured data into the boundary of the overall waterlogging forecast simulation model to obtain the calculated water level and calculated flow at the monitoring station; Comparing the calculated water level with the water level deviation value of the measured water level at the monitoring station; Compare the flow deviation value between the calculated flow and the measured flow; When the water level deviation value and the flow rate deviation value exceed the preset threshold value, the riverbed roughness coefficient and the grid infiltration rate parameters are adjusted and recalculated until the water level deviation value and the flow rate deviation value are lower than the preset threshold value, so as to output the parameters of the overall model of the waterlogging forecast simulation.

7. A regional waterlogging early warning method based on multiple water professional models according to claim 1, characterized in that: Based on the preset rainfall data, the regional waterlogging forecast simulation is performed through the overall waterlogging forecast simulation model, and the water flow conditions of the drainage network, the water flow conditions of the open channel, and the waterlogging and inundation conditions are output, specifically including: Input the temporal and spatial distribution data of the set rainfall scenario into the overall model of waterlogging forecast simulation to start the dynamic rolling calculation process; Based on the one-dimensional hydrodynamic model of the pipe network, the pipeline liquid level change process, flow fluctuation process and full pipe rate status data of the drainage pipe network are output in real time; A one-dimensional open channel hydrodynamic model was run simultaneously to generate data sets of open channel water level fluctuations, flow rate changes, and real-time embankment height comparisons. The spatial distribution map of the surface inundation range, the contour map of the inundation depth and the thermal map of the inundation duration are calculated by using the two-dimensional surface hydrodynamic model.

8. A regional waterlogging early warning method based on multiple water professional models according to claim 1, characterized in that: Input the temporal and spatial distribution data of the set rainfall scenario into the overall model of waterlogging forecast simulation to start the dynamic rolling calculation process, which includes: Split rainfall input data into preset time steps to construct continuous calculation time windows; In each time window, the computational threads of the runoff model, the open channel one-dimensional hydrodynamic model, the pipe network one-dimensional hydrodynamic model, and the surface two-dimensional hydrodynamic model are executed synchronously. Exchange coupling interface data and update boundary conditions; The pipe network status, open channel status and surface inundation data of each time step are cumulatively output.

9. 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 regional waterlogging early warning method based on multiple water professional models according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the regional waterlogging early warning method based on multiple water professional models according to any one of claims 1 to 8 is implemented.

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