A rapid modeling method for flood and waterlogging

Abstract

The present application relates to the technical field of flood prediction, and specifically to a method for rapid modeling of flood and waterlogging; the method includes: collecting flood and waterlogging data source information of the target analysis area, completing the flood and waterlogging data source information, establishing a flood and waterlogging model based on the completion processing results of the flood and waterlogging data source information, and evaluating the flood risk of the target analysis area through the flood and waterlogging model; and, the present application also combines the water volume observation data with the flood and waterlogging model to correct the model parameters of the flood and waterlogging model in real time. The above scheme completes the flood and waterlogging data source information, automatically establishes a flood and waterlogging model based on the gravity flow principle and potential flow principle, and performs rapid calculation and prediction, which helps to accurately predict the occurrence and development of flood events, take emergency measures in advance, and reduce losses.

Description

Technical Field

[0001] The present application relates to the technical field of flood prediction, and in particular to a method for rapid modeling of flood and waterlogging. Background Art

[0002] Floods are one of the most common natural disasters in the world, often causing significant loss of life and property. These disasters are usually caused by factors such as abnormally increased rainfall, river overflow, and failure of drainage systems.

[0003] The field of flood simulation and prediction faces many challenges, including complex modeling, incomplete data, low model accuracy and efficiency. Specifically: In terms of complex modeling and incomplete data, existing flood models and methods usually rely on complex numerical calculations and high-resolution geographic information system (GIS) data, which requires a lot of time and resources, and the accuracy of these methods is highly dependent on the quality and completeness of data, which may not be easy to obtain in many areas. In terms of model accuracy and low efficiency, for large areas, the traditional modeling process is time-consuming and labor-intensive, lacks high-quality geographic information and hydrological data, resulting in low modeling accuracy and computational efficiency.

[0004] Therefore, traditional flood waterlogging models and calculation methods often cannot meet the needs of fast and accurate flood prediction and management. Summary of the invention

[0005] In view of this, an embodiment of the present application provides a method for rapid modeling of floods and waterlogging, which can meet the needs of rapid and accurate flood prediction and management.

[0006] In a first aspect, the present application provides a method for rapid modeling of flood waterlogging, the method comprising:

[0007] Collect flood and waterlogging data source information in the target analysis area;

[0008] Complete the flood and waterlogging data source information to establish a flood and waterlogging model according to the complete processing result of the flood and waterlogging data source information, and evaluate the flood risk of the target analysis area through the flood and waterlogging model;

[0009] The water volume observation data of the target analysis area is obtained, and the water volume observation data is combined with the flood waterlogging model for processing to correct the model parameters of the flood waterlogging model in real time.

[0010] According to the above-mentioned technical means, this application completes the flood and waterlogging data source information, automatically establishes a flood and waterlogging model based on the completed flood and waterlogging data source information, and performs rapid calculation and prediction, which helps to accurately predict the occurrence and development of flood events, take emergency measures in advance, and reduce losses.

[0011] In combination with the first aspect, in one implementation, the flood and waterlogging data source information includes geographic information data, pipeline network information, and hydraulic control facility information.

[0012] According to the above technical means, based on the existing geographic information data, pipeline network information and water volume observation data, full use is made of the existing resource data, which reduces the dependence on additional data and improves the operability of the method.

[0013] In combination with the first aspect, in one implementation, the completion processing includes adaptive grid division processing; the completion processing of the flood waterlogging data source information includes:

[0014] Based on the flood and waterlogging data source information of the target analysis area, obtaining the terrain complexity distribution of the target analysis area;

[0015] According to the distribution of the complexity of the terrain, the target analysis area is adaptively meshed to obtain meshes of various areas.

[0016] According to the above technical means, the present application creates denser grids in areas with higher terrain complexity and sparser grids in areas with simpler terrain according to the distribution of terrain complexity, thereby improving calculation efficiency and model accuracy.

[0017] In combination with the first aspect, in one implementation, the completion processing includes terrain data processing; the completion processing of the flood waterlogging data source information includes:

[0018] The terrain data in the geographic information data is processed by digital elevation model to generate elevation information and ground elevation information of each watershed area in the target analysis area, and to extract the water system network.

[0019] In combination with the first aspect, in one implementation, the completion processing includes pipeline information network processing; the completion processing of the flood waterlogging data source information includes:

[0020] Performing graph theory and network analysis on the pipeline network information to generate a pipeline topology structure of the target analysis area;

[0021] The pipeline topology structure is topologically sorted to obtain the upstream and downstream relationship of the pipelines in the target analysis area.

[0022] According to the above technical means, traditional models often ignore the pipeline network information in the urban drainage system, and this application processes the pipeline network information and incorporates it into the flood waterlogging model to better simulate the urban waterlogging situation and improve the accuracy and practicality of the flood waterlogging model.

[0023] In combination with the first aspect, in one implementation, the completion processing includes gutter information processing; the completion processing of the flood waterlogging data source information includes:

[0024] A gutter topology structure is established according to the road surface information in the geographic information data, and a gutter segmentation process is performed according to the ground elevation information.

[0025] In combination with the first aspect, in one implementation, establishing a flood waterlogging model according to the completion processing result of the flood waterlogging data source information includes:

[0026] The completed processing result of the flood and waterlogging data source information is subjected to hydrological coupling processing to establish a flood and waterlogging model of a pipe network-ditch-river-lake.

[0027] According to the above technical means, the present application automatically couples a flood and waterlogging model based on the complete processing results of the flood and waterlogging data source information, without the need to manually construct a complex model structure, greatly reducing the time and workload of modeling.

[0028] In combination with the first aspect, in one implementation, the assessing the flood risk of the target analysis area by using the flood waterlogging model includes:

[0029] According to the water volume observation data and the water levels upstream and downstream of the pipeline, the water levels of each node in the target analysis area are inferred based on the gravity flow principle and the potential flow principle to perform hydraulic analysis on the target analysis area;

[0030] According to the upstream and downstream water levels of the pipeline and the Manning coefficient of the pipeline, the pipeline flow rate is estimated based on the potential flow principle;

[0031] According to the pipeline topology and the pipeline flow, the water volume of each water catchment area is obtained to perform water volume analysis on the target analysis area;

[0032] Based on the water volume of each catchment area and the water volume observation data, the runoff volume and runoff coefficient of each catchment area are estimated to determine the flood formation process of the target analysis area and evaluate the flood risk of the target analysis area.

[0033] Based on the above-mentioned technical means, this application adopts the principle of gravity flow and potential flow to simulate water flow, which is simpler and more efficient than traditional complex numerical models, and can reduce computational complexity while maintaining accuracy. In addition, this application also comprehensively considers the water flow conditions in the entire watershed, including rivers, pipes, ditches and surface runoff, and provides a comprehensive flood and waterlogging analysis, rather than just analysis of specific points.

[0034] In combination with the first aspect, in one implementation, the water volume observation data includes water level data, flow data, and rainfall data; and the combining the water volume observation data with the flood waterlogging model to correct the model parameters of the flood waterlogging model in real time includes:

[0035] Data assimilation processing is performed on the flood waterlogging model based on the water volume observation data to correct model parameters of the flood waterlogging model in real time.

[0036] According to the above technical means, this application combines the measured data with the numerical model through data assimilation technology, updates the model parameters in real time, and improves the applicability and accuracy of the model.

[0037] In combination with the first aspect, in one implementation, the method further includes:

[0038] According to the regional grids divided into the target analysis area, the flood waterlogging model is spatially divided into a plurality of model grids, and parallel calculation processing is performed on the plurality of model grids.

[0039] Based on the above technical means, this application adopts parallel computing technology to improve the calculation speed, so that the flood and waterlogging model can handle larger and more complex geographical areas, and enhance the practicality of the flood and waterlogging model.

[0040] In a second aspect, the present application provides a rapid flood modeling device, the device comprising:

[0041] A flood and waterlogging data source information acquisition module is used to collect flood and waterlogging data source information in the target analysis area;

[0042] A flood waterlogging model building module is used to complete the flood waterlogging data source information, to establish a flood waterlogging model according to the completion processing result of the flood waterlogging data source information, and to evaluate the flood risk of the target analysis area through the flood waterlogging model;

[0043] The model parameter correction module is used to obtain water volume observation data of the target analysis area, and combine the water volume observation data with the flood waterlogging model to correct the model parameters of the flood waterlogging model in real time.

[0044] In a third aspect, the present application provides a computer device, comprising a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the above-mentioned method for rapid modeling of flood and waterlogging.

[0045] In a fourth aspect, the present application provides a computer-readable storage medium, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned method for rapid modeling of flood and waterlogging.

[0046] The technical solution provided by this application may have the following beneficial effects:

[0047] 1. The flood and waterlogging model uses the potential flow principle to simulate water flow. Compared with traditional complex numerical models, it is simpler and more efficient. It can simplify the complexity of flood and waterlogging modeling, and achieve rapid model construction through reasonable simplification and estimation of model parameters. It can reduce computational complexity while maintaining model accuracy, which helps to accurately predict the occurrence and development of flood events, take emergency measures in advance, and reduce losses.

[0048] 2. Hydrological coupling processing is performed based on the processing results of adaptive grid division, gutter information, terrain data and pipeline information network, which can automatically establish a flood waterlogging model without manually building a complex model structure, greatly reducing the time and workload of modeling;

[0049] 3. The flood waterlogging model is based on the principles of gravity flow and potential flow, and comprehensively considers the water flow conditions in the entire basin, including rivers, pipes, ditches and surface runoff, providing a comprehensive flood waterlogging analysis rather than just an analysis of specific points. It also effectively simplifies complex water flow calculation problems, reduces calculation costs, and improves the calculation efficiency of the model;

[0050] 4. Through data assimilation technology, the measured data is combined with the numerical model to update the model parameters of the flood waterlogging model in real time, thus improving the applicability and accuracy of the model;

[0051] 5. The flood waterlogging model is divided into multiple sub-models, and parallel computing technology is used to improve the computing speed, so that the flood waterlogging model can handle larger and more complex geographical areas, and enhance the practicality of the model;

[0052] 6. Rapid risk assessment can be carried out based on the output results of the flood waterlogging model, and intelligent risk assessment and emergency response plans can be introduced to carry out real-time flood warnings and provide targeted emergency response suggestions, thus improving the efficiency of disaster management;

[0053] 7. The output results of the flood and waterlogging model can be presented in an intuitive way, providing decision makers and the public with an intuitive display of the flood situation and data visualization so that they can take necessary measures and make decisions. In addition, a cross-border cooperation platform can be established based on the flood and waterlogging model to promote cooperation and information sharing between different regions and institutions, which will contribute to more extensive flood monitoring and early warning, and improve the ability to respond to disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 The present invention is a method flow chart of a method for rapid modeling of flood waterlogging according to an exemplary embodiment.

[0056] Figure 2 The present invention is a method flow chart of a method for rapid modeling of flood waterlogging according to an exemplary embodiment.

[0057] Figure 3 The present invention is a method flow chart of a method for rapid modeling of flood waterlogging according to an exemplary embodiment.

[0058] Figure 4 It is a schematic diagram showing the implementation of the gravity flow principle and the potential flow principle according to an exemplary embodiment.

[0059] Figure 5 The present invention is a structural block diagram of a device for rapid modeling of flood and waterlogging according to an exemplary embodiment.

[0060] Figure 6 A structural block diagram of a computer device shown in an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0061] The technical solution of the present application will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0062] It should be noted that traditional flood simulation and prediction have many defects, such as the complexity of flood modeling: traditional flood modeling methods usually rely on complex numerical calculations and high-resolution geographic information system data, which makes the modeling process time-consuming and labor-intensive; this application proposes a method based on potential flow theory, which aims to simplify the complexity of flood modeling, and achieve rapid model construction through reasonable simplification and estimation of model parameters. For example, the problem of incomplete data: many areas lack high-quality geographic information and hydrological data, which limits the application of traditional flood models in these areas; this application uses data assimilation technology to combine water volume observation data with flood models to improve the applicability of the model, and even in the case of incomplete data, reliable results can be obtained. For example, the modeling efficiency problem: for large areas, the calculation efficiency of traditional modeling methods is low; this application proposes a parallel computing strategy to divide the model into multiple sub-models for simultaneous calculation to speed up the model operation, thereby supporting flood modeling and analysis in a wider range of areas. For example, the simplification of the real world: traditional flood waterlogging models usually require accurate modeling of the real world in terms of digital twins, which is often challenging under complex terrain and hydrological conditions; this application adopts a simplified method based on potential flow theory to describe the real world through rapid analysis of key information (such as mountains, rivers, pumping stations, etc.) to obtain the results required for practical applications. Although it is not very accurate, it is still practical. For example, the application of pipeline information: traditional flood waterlogging models often ignore the pipeline information in the urban drainage system, and this application incorporates pipeline information into the flood waterlogging model to better simulate urban waterlogging conditions and improve the accuracy and practicality of the flood waterlogging model. In summary, this application proposes a rapid flood waterlogging modeling method based on potential flow theory, aiming to solve key technical problems such as the complexity of flood waterlogging modeling, incomplete data, modeling efficiency, simplification of the real world, and application of pipeline information.

[0063] Figure 1 FIG. 1 is a method flow chart of a method for rapid modeling of flood waterlogging according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps:

[0064] Step S101: Collect flood data source information in the target analysis area.

[0065] In a possible implementation, this embodiment first collects flood and waterlogging data source information of the target analysis area. The flood and waterlogging data source information may be geographic information data of the target analysis area, such as obtaining the geographic information data through a network map. However, since the directly obtained geographic information data is incomplete and cannot meet the modeling needs of the flood and waterlogging model, the geographic information data needs to be completed.

[0066] Step S102: completing the flood and waterlogging data source information, establishing a flood and waterlogging model according to the completion processing result of the flood and waterlogging data source information, and evaluating the flood risk of the target analysis area through the flood and waterlogging model.

[0067] In one possible implementation, continuing with the above example, for a certain road in the geographic information data, the network map can provide the geographical location and layout of the road, but establishing a flood and waterlogging model also requires the elevation information and ground elevation information of the road; therefore, through completion processing, the elevation information and ground elevation information of the road can be obtained, and then a flood and waterlogging model can be established based on the flood and waterlogging data source information after completion processing to evaluate the flood risk in the target analysis area.

[0068] Step S103: obtaining water volume observation data of the target analysis area, and combining the water volume observation data with the flood waterlogging model for processing, so as to correct the model parameters of the flood waterlogging model in real time.

[0069] In a possible implementation, this embodiment also collects water volume observation data of the target analysis area in real time, which can be collected through online monitoring and sensing devices such as flow meters, liquid level meters, rain gauges, etc. in various places; the water volume observation data is combined with the predicted values ​​of the flood and waterlogging model, and the output results of the flood and waterlogging model are optimized with real observation data, so as to achieve real-time correction of the model parameters of the flood and waterlogging model and improve the applicability and accuracy of the model.

[0070] In summary, the technical solution provided by this application may include the following beneficial effects:

[0071] 1. The flood and waterlogging model uses the potential flow principle to simulate water flow. Compared with traditional complex numerical models, it is simpler and more efficient. It can simplify the complexity of flood and waterlogging modeling, and achieve rapid model construction through reasonable simplification and estimation of model parameters. It can reduce computational complexity while maintaining model accuracy, which helps to accurately predict the occurrence and development of flood events, take emergency measures in advance, and reduce losses.

[0072] 2. Hydrological coupling processing is performed based on the processing results of adaptive grid division, gutter information, terrain data and pipeline information network, which can automatically establish a flood waterlogging model without manually building a complex model structure, greatly reducing the time and workload of modeling;

[0073] 3. The flood waterlogging model is based on the principles of gravity flow and potential flow, and comprehensively considers the water flow conditions in the entire basin, including rivers, pipes, ditches and surface runoff, providing a comprehensive flood waterlogging analysis rather than just an analysis of specific points. It also effectively simplifies complex water flow calculation problems, reduces calculation costs, and improves the calculation efficiency of the model;

[0074] 4. Through data assimilation technology, the measured data is combined with the numerical model to update the model parameters of the flood waterlogging model in real time, thus improving the applicability and accuracy of the model;

[0075] 5. The flood waterlogging model is divided into multiple sub-models, and parallel computing technology is used to improve the computing speed, so that the flood waterlogging model can handle larger and more complex geographical areas, and enhance the practicality of the model;

[0076] 6. Rapid risk assessment can be carried out based on the output results of the flood waterlogging model, and intelligent risk assessment and emergency response plans can be introduced to carry out real-time flood warnings and provide targeted emergency response suggestions, thus improving the efficiency of disaster management;

[0077] 7. The output results of the flood and waterlogging model can be presented in an intuitive way, providing decision makers and the public with an intuitive display of the flood situation and data visualization so that they can take necessary measures and make decisions. In addition, a cross-border cooperation platform can be established based on the flood and waterlogging model to promote cooperation and information sharing between different regions and institutions, which will contribute to more extensive flood monitoring and early warning, and improve the ability to respond to disasters.

[0078] Figure 2 FIG. 1 is a method flow chart of a method for rapid modeling of flood waterlogging according to an exemplary embodiment. Figure 2 As shown, the method may include the following steps:

[0079] Step S201, collecting flood and waterlogging data source information of the target analysis area; the flood and waterlogging data source information includes geographic information data, pipeline network information and hydraulic control facility information.

[0080] For further information, please refer to Figure 3 The method flow chart of a method for rapid modeling of flood and waterlogging is shown. In this embodiment, the data collection and preparation phase is first performed (i.e., collecting flood and waterlogging data source information of the target analysis area), including:

[0081] This embodiment collects geographic information data of the target analysis area through network maps and other data sources. The geographic information data may include the topography, water network, road network, mountains, rivers, ponds, buildings, etc. of the target analysis area. The geographic information data collected at this time is incomplete data and needs to be completed before modeling.

[0082] This embodiment obtains relevant information of the pipeline network (i.e., the above-mentioned pipeline network information), which includes the pipe bottom elevation, ground elevation, pipe diameter, pipe material, etc. of the upstream and downstream of the pipeline; in some smaller towns, there may be no pipeline network for the time being, and the pipeline network information may not be collected at this time.

[0083] This embodiment collects information on hydraulic control facilities, such as pump stations, river sluice stations, valves, etc.

[0084] Step S202: based on the flood waterlogging data source information of the target analysis area, obtain the terrain complexity distribution of the target analysis area; according to the terrain complexity distribution, perform adaptive grid division processing on the target analysis area to obtain grids of each area.

[0085] Furthermore, after the data collection and preparation phase is completed, the present embodiment performs completion processing on the flood waterlogging data source information to improve the flood waterlogging data source information and realize the modeling data preparation of the flood waterlogging model; Figure 3 As shown, the completion process includes adaptive meshing process, terrain data process, pipeline information network process and ditch information process;

[0086] When performing adaptive grid division processing, this embodiment analyzes the collected flood data source information, considers the terrain, water flow path and other factors to determine the density of grid division, creates denser grids in areas with higher terrain complexity, and creates sparser grids in areas with simpler terrain, so as to improve computing efficiency and model accuracy.

[0087] Step S203: Perform digital elevation model processing on the terrain data in the geographic information data to generate elevation information and ground elevation information of each watershed area in the target analysis area, and extract the water system network.

[0088] Furthermore, when processing terrain data, the present embodiment utilizes a digital elevation model (DEM) method to process the terrain data in the geographic information data to generate elevation and ground elevation information of each watershed area; and, when processing terrain data, the present embodiment also implements automatic water network extraction, which includes the distribution of water bodies such as rivers, ditches, and lakes, and determines the path and connection relationship of the water flow based on the elevation information, ground elevation information, and flow direction information of each watershed area in the target analysis area to establish a water network.

[0089] Step S204: Perform graph theory and network analysis on the pipeline network information to generate a pipeline topology structure of the target analysis area; perform topological sorting on the pipeline topology structure to obtain upstream and downstream relationships of the pipelines in the target analysis area.

[0090] Furthermore, when performing pipeline information network processing, this embodiment uses graph theory and network analysis algorithms to establish a topological structure, and uses a topological sorting algorithm to establish pipeline connection relationships:

[0091] 1) Graph theory and network analysis algorithms:

[0092] (1) Branch and confluence detection: By analyzing the connection relationship between pipes, the branches (forks) and confluences (convergence points) in the pipe network are detected to determine the path of the water flow.

[0093] (2) Loop detection: Detect loops in the pipeline network, that is, the pipeline paths form a loop. These loops can cause computational problems, so it is very important to detect and handle them.

[0094] (3) Shortest path analysis: Calculate the shortest path between two points in the pipeline network. It can be seen that when the pipeline network information is subjected to graph theory and network analysis, the present embodiment performs branch and confluence detection processing, loop detection processing, and shortest path analysis processing on the pipeline information network, and generates the pipeline topology structure of the target analysis area based on the branch and confluence detection processing results, the loop detection processing results, and the shortest path analysis processing results.

[0095] 2) Topological sorting algorithm:

[0096] Sort nodes in a directed acyclic graph (DAG). In pipeline information processing, topological sorting is used to determine the upstream and downstream order of pipelines to ensure that water flows in the correct direction in the model;

[0097] (1) Acyclic property: In a directed acyclic graph (DAG), the topological sorting algorithm can ensure that there are no cycles, which means that the water will always flow in the correct direction and no infinite loop will occur.

[0098] (2) Upstream and downstream order: Topological sorting determines the upstream and downstream relationship of the pipeline, enabling the model to accurately simulate water flow transmission; it can be seen that this implementation performs topological sorting on the pipeline topology structure based on the directed acyclic graph (DAG) to obtain the upstream and downstream relationship of the pipeline in the target analysis area.

[0099] Step S205: Establish a ditch topology structure based on the road surface information in the geographic information data, and perform ditch segmentation processing based on the ground elevation information.

[0100] Furthermore, when processing the gutter information, this embodiment uses the Dijkstra algorithm to establish the gutter topology structure, including:

[0101] (1) The road in the pavement information is used as the ditch of the model, and the topological structure of the ditch is established according to the pavement information;

[0102] (2) The side ditch is divided into sections according to the ground elevation. The cross-section of the side ditch is trapezoidal, with the bottom width being the road width and the top width being the grid width.

[0103] The Dijkstra algorithm is used to calculate the single-source shortest path analysis in a weighted directed graph. In gutter information processing, the Dijkstra algorithm can be used to determine which roads should be considered as gutter and establish the connection relationship between them.

[0104] Step S206, performing hydrological coupling processing on the completion processing result of the flood waterlogging data source information to establish a flood waterlogging model of the pipe network-ditch-river-lake, and evaluating the flood risk of the target analysis area through the flood waterlogging model.

[0105] Furthermore, the construction of the flood waterlogging model includes:

[0106] (1) Hydrological coupling: Hydrological coupling is a technology that connects water flows between different water bodies. In the flood waterlogging model, this includes connecting water bodies such as pipe networks, ditches, rivers and lakes so that water flows can be transmitted freely between them. In other words, this embodiment combines the adaptive grid division processing results, terrain data processing results, pipeline information network processing results and ditch information processing results to perform hydrological coupling processing to construct a flood waterlogging model.

[0107] (2) Information transmission: Different parts need to transmit information in real time, including water level, flow, rainfall data, etc. (i.e., the water volume observation data mentioned above). This can be achieved through data interfaces or shared databases to ensure that the various components can work together.

[0108] (3) Unified model parameters: Models of different components use consistent parameters and units to ensure correct simulation and calculation, including a unified coordinate system, time step, terrain information, etc.

[0109] In a possible implementation manner, the “assessing the flood risk of the target analysis area by using the flood waterlogging model” in step S206 includes:

[0110] According to the water volume observation data and the water levels upstream and downstream of the pipeline, the water levels of each node in the target analysis area are inferred based on the gravity flow principle and the potential flow principle to perform hydraulic analysis on the target analysis area;

[0111] According to the upstream and downstream water levels of the pipeline and the Manning coefficient of the pipeline, the pipeline flow rate is estimated based on the principle of potential flow;

[0112] According to the pipeline topology and the pipeline flow, the water volume of each water catchment area is obtained to perform water volume analysis on the target analysis area;

[0113] Based on the water volume of each catchment area and the water volume observation data, the runoff volume and runoff coefficient of each catchment area are estimated to determine the flood formation process of the target analysis area and evaluate the flood risk of the target analysis area.

[0114] Furthermore, this embodiment uses the flood waterlogging model to perform hydraulic analysis processing, water catchment analysis processing, and runoff water volume analysis processing on the target analysis area, and evaluates the flood risk of the target analysis area based on the hydraulic analysis processing results, water catchment analysis processing results, and runoff water volume analysis processing results;

[0115] When performing hydraulic analysis, this embodiment uses the gravity flow principle and potential flow principle to infer the water level of each node based on partial water level monitoring data and the upstream and downstream water levels of the pipeline, ditch, or river.

[0116] Please refer to Figure 4 The schematic diagram of the realization of the gravity flow principle and the potential flow principle is shown in FIG. The gravity flow principle and the potential flow principle are introduced respectively as follows:

[0117] (1) Gravity flow principle: The gravity flow principle is based on Bernoulli's equation, which describes that the total energy of a fluid remains constant at different positions. In this context, Bernoulli's equation can be used to estimate the water level of the fluid.

[0118]

[0119] Among them, P represents the pressure of the fluid, ρ represents the density of the fluid, v represents the velocity of the fluid, g represents the acceleration of gravity, h represents the height of the fluid (relative to a reference point), and constant is a constant that is related to the properties and environment of the fluid, indicating that the total energy remains unchanged during the flow of the fluid.

[0120] Based on the above formula, when estimating the water level of each node, this embodiment uses Bernoulli's equation to compare the total energy at different positions to calculate the water level, and the implementation steps include:

[0121] a. Select a reference point: Select a reference point and set its height as the benchmark for measuring relative height.

[0122] b. Calculate fluid velocity: Use the upstream and downstream water level difference of the pipe, ditch or river to calculate the fluid velocity. The fluid velocity calculation formula is as follows:

[0123]

[0124] Among them, v represents the flow velocity, n is the Manning roughness coefficient, which describes the surface friction resistance of the pipe or river, and the appropriate value is usually selected according to the specific situation; R is the hydraulic radius, which is usually expressed as the ratio of the cross-sectional area of ​​the river or channel to the wetted perimeter; S is the slope of the water flow or the slope of the water surface, expressed as the ratio of the height difference to the horizontal distance.

[0125] c. Apply Bernoulli's equation: For each node, apply Bernoulli's equation to the location of that node and the reference point to produce an equation that represents the total energy.

[0126] d. Solve the equations: Solve Bernoulli's equation to calculate the water level at each node, which will involve values ​​for pressure, density, flow rate, and altitude, as well as the known altitude of the reference point.

[0127] (2) Potential flow principle: Potential flow principle is a mathematical method used to describe fluid flow. It is based on the concepts of stream function and potential function. In the potential flow method, the velocity of the fluid can be expressed by the gradient (derivative). For two-dimensional flow, the potential function φ(x, y) of the fluid velocity and the stream function The following relations are satisfied:

[0128]

[0129] Where V represents the fluid velocity vector, represents the gradient operator, represents the Laplace operator, x and y represent the horizontal and vertical coordinates respectively. In the potential flow method, the potential function φ(x,y) or stream function is determined by solving the Laplace equation The fluid velocity is then calculated from the gradient.

[0130] Based on the above (1) gravity flow principle and (2) potential flow principle, this embodiment integrates the gravity flow principle and the potential flow principle to infer the water level of each node; the specific steps are as follows (i.e., the above-mentioned inferring the water level of each node in the target analysis area based on the gravity flow principle and the potential flow principle according to the water volume observation data and the upstream and downstream water levels of the pipeline includes the following steps):

[0131] a. First, estimate the fluid velocity and pressure distribution in a pipe, ditch or river based on Bernoulli's equation;

[0132] b. Calculate the potential function φ(x,y) or stream function based on the fluid velocity and pressure distribution information The specific calculation process is achieved by solving the Laplace equation;

[0133] c. Use potential function φ(x,y) or stream function Calculate fluid velocity distribution;

[0134] d. Combining the fluid velocity and gravity flow principle, Bernoulli's equation is applied to estimate the water level of each node. That is, this embodiment calculates the fluid velocity distribution according to the upstream and downstream water levels of the pipeline based on the gravity flow principle and the potential flow principle, and obtains the water level of each node according to the fluid velocity distribution.

[0135] This integrated approach can estimate the water level at each node more accurately, taking into account the advantages of both the potential flow principle and the gravity flow principle.

[0136] After calculating the water level of each node, this embodiment also infers the Manning coefficient of the pipeline based on part of the flow monitoring data, the node water level and the pipeline diameter; in addition, this embodiment also considers the processing of control facilities such as pump stations, and uses water pump characteristic curves to analyze water pumps and head loss curves to analyze valves.

[0137] When analyzing the water volume, this embodiment uses the upstream and downstream water levels of the pipeline and the Manning coefficient of the pipeline, adopts the potential flow principle to estimate the flow of the pipeline, ditch, and river, and estimates the water volume of each catchment area based on the upstream and downstream flow.

[0138] When performing runoff water volume analysis and processing, this embodiment uses the water volume of each catchment area and the ground rainfall monitoring data to estimate the runoff water volume and runoff coefficient of each catchment area, and estimates the runoff coefficient of each catchment area by comparing with the actual rainfall.

[0139] Step S207: Acquire water volume observation data of the target analysis area, and perform data assimilation processing on the flood waterlogging model based on the water volume observation data to correct the model parameters of the flood waterlogging model in real time. The water volume observation data includes water level data, flow data and rainfall data.

[0140] Furthermore, this embodiment combines observation data such as water level, flow, rainfall, etc. with the numerical model for data assimilation processing, and modifies the model parameters to improve the applicability and accuracy of the model; wherein, ensemble data assimilation processing is an advanced data assimilation technology that aims to improve the performance of numerical models, especially in the presence of parameter uncertainty and model structure uncertainty. Ensemble data assimilation processing estimates state variables by using a set of model sets rather than a single model, which can more comprehensively consider the uncertainty of the model.

[0141] In a possible implementation, Figure 3 As shown, the method also includes:

[0142] According to the regional grids divided by the target analysis area, the flood waterlogging model is spatially divided into multiple model grids, and the multiple model grids are parallelly calculated. It can be seen that this embodiment divides the flood waterlogging model into multiple grids in space and performs parallel calculations to improve calculation efficiency.

[0143] In one possible implementation, this embodiment performs parameter optimization based on machine learning. Specifically, this embodiment updates the inferred Manning coefficient, runoff coefficient and other parameters in real time based on the measured water volume observation data, and uses historical calculation results to perform big data analysis to improve the calculation accuracy of the model.

[0144] In a possible implementation, Figure 3 As shown, this embodiment can establish a cross-border cooperation platform, allowing different regions and institutions to share flood models and real-time data to achieve more extensive flood monitoring and early warning, and promote cooperation and information sharing.

[0145] In a possible implementation, Figure 3 As shown, this embodiment introduces an intelligent risk assessment and emergency response system to quickly assess the flood risks in different areas based on the output of the flood and waterlogging model, and provide targeted emergency response suggestions to reduce losses and improve the efficiency of flood control and rescue.

[0146] In a possible implementation, Figure 3 As shown, this embodiment presents the model results in an intuitive manner through a visual decision-making tool, providing decision makers and the public with an intuitive display of flood conditions and data visualization so that they can take necessary measures and make decisions. It can be seen that this embodiment achieves rapid modeling and calculation of flood and waterlogging models through reasonable simplification, data assimilation, and parallel computing, providing a new solution for improving the efficiency and feasibility of flood and waterlogging prediction and management.

[0147] The following two simple examples are used to explain the contents disclosed in the above embodiment:

[0148] Example 1: Establishment of flood waterlogging model and risk assessment in a certain area of ​​City A:

[0149] In July 2023, a certain area in City A suffered from continuous heavy rainfall that was rare in history. The water levels of rivers and lakes in the entire area soared, and many places encountered dangerous situations, which induced mountain torrents, causing huge losses to the lives and property of the people in the entire area. In order to reduce the recurrence of such incidents, when the analysis area is large and there is no basic data on the pipeline network, you can use the network map to obtain information such as topography, water systems, road networks, structures, etc., combine the digital elevation model (DEM) to process the data, and introduce water observation data such as water level and rainfall. Then, you can automatically establish a flood waterlogging model, quickly assess the flood risk in the area, and provide basic support for the prevention and control of extreme heavy rainfall disasters. The establishment and risk assessment of the flood waterlogging model in a certain area of ​​City A includes the following steps:

[0150] 1. Data collection and preparation stage:

[0151] a. Use online maps and satellite remote sensing data to obtain geographic information data such as topography, water network, road network, mountains, rivers, ponds and structures in the area of ​​City A.

[0152] b. Based on the digital elevation model (DEM) processing, the elevation information and ground elevation information of each area are obtained.

[0153] c. Collect real-time or historical water level, rainfall, and other water quantity observation data, including monitoring station and meteorological data.

[0154] 2. Adaptive meshing processing:

[0155] a. Automatically divide the area into square grids and determine the grid size to suit the required calculation accuracy.

[0156] 3. Terrain data processing:

[0157] a. Use digital elevation model (DEM) to process terrain information and generate elevation and ground elevation data for each catchment area.

[0158] b. Automatically extract water networks, including rivers, ditches and lakes.

[0159] 4. Pipeline information network processing:

[0160] a. In this case, it is assumed that there is no pipeline network in the area, so pipeline information processing is not required.

[0161] 5. Gutter information processing:

[0162] a. Take the road as the ditch of the model and establish the topological structure of the ditch based on the road surface information;

[0163] b. Divide the ditch into sections according to the ground elevation and use a trapezoidal model to represent the cross section, with the bottom width being the road width and the top width being the grid width.

[0164] 6. Data assimilation processing:

[0165] a. Combine real-time water level, rainfall and other water volume observation data with the flood waterlogging model to update the model parameters in real time to ensure that the model reflects the actual situation.

[0166] 7. Hydraulic analysis and processing:

[0167] a. Using the principles of potential flow and gravity flow, the water level of each node can be estimated based on water level monitoring data and the upstream and downstream water levels of ditches and rivers.

[0168] b. When considering mountain torrents, whether overflow will occur is determined based on the depth of water on the ground.

[0169] c. Consider the river as an open channel and calculate the flow based on the water level and trapezoidal section model.

[0170] 8. Analysis and processing of water volume:

[0171] a. Using the upstream and downstream water levels of the pipeline and the trapezoidal cross-section model, the potential flow method is used to estimate the flow of pipelines, ditches, and rivers.

[0172] b. Estimate the water volume of each catchment area based on the flow rates upstream and downstream.

[0173] 9. Prediction and processing of rainfall runoff coefficient:

[0174] a. Using the water volume of each catchment area and the actual rainfall monitoring data, the runoff volume of each catchment area is estimated to determine the formation process of the flood.

[0175] 10. Parallel computing processing:

[0176] a. Divide the flood waterlogging model into multiple grids in space and improve computational efficiency through parallel computing.

[0177] 11. Machine Learning Parameter Optimization:

[0178] a. Use historical data and machine learning techniques to optimize the inferred model parameters to improve the accuracy of the model.

[0179] 12. Cross-border cooperation platform:

[0180] a. Establish a platform to enable different regions and institutions to share flood models and real-time data to achieve more extensive flood monitoring and early warning and strengthen cross-border cooperation.

[0181] 13. Visual decision-making tools:

[0182] a. Present the model results in a visual way for leadership and public decision support.

[0183] In the above embodiment, the area of ​​City A can automatically establish a flood model, assess flood risks, and provide basic support to reduce the risks brought by extreme rainfall disasters. This embodiment makes full use of existing data and technology to provide a flexible and efficient solution.

[0184] Example 2: Establishment of flood waterlogging model and risk assessment in a certain area of ​​City B:

[0185] In a certain area of ​​City B, a variety of online monitoring and sensing equipment such as flow meters, liquid level meters, and rain gauges have been installed on drainage facilities such as drainage pipes, outlets, and pump stations, and the online river water level data installed by the hydrological and water conservancy departments has been connected. This allows real-time perception of the drainage status of the entire area, and a flood waterlogging model is established based on the results of a detailed underground drainage basic information census. This assesses the scope of waterlogging and the depth of water accumulation in the area, providing key data support for decision-making analysis for urban flood control emergencies.

[0186] 1. Data collection and preparation stage:

[0187] a. Use the installed flow meters, liquid level meters, rain gauges and other online monitoring and sensing equipment to collect real-time data on drainage facilities such as drainage pipe networks, outlets, pumping stations, including flow, water level and rainfall.

[0188] b. Access the online river water level data of the hydrological and water conservancy departments to obtain river water level information.

[0189] 2. Adaptive meshing processing:

[0190] a. Based on the existing drainage basic information census data, the area is automatically divided into square grids, and the grid size is determined according to the existing sensing equipment data and calculation accuracy requirements.

[0191] 3. Terrain data processing:

[0192] a. Utilize the results of the underground drainage basic information survey to obtain geographic information data of the underground drainage pipeline network, including pipeline location, diameter, material, etc.

[0193] b. Use existing geographic information data, such as topography, road networks, buildings, etc., to provide more comprehensive geographic information data for the model.

[0194] 4. Data assimilation processing:

[0195] a. Combine real-time sensing data with existing flood waterlogging models, and use data assimilation technology to update model parameters in real time to ensure that the model reflects the actual drainage status.

[0196] 5. Hydraulic analysis and processing:

[0197] a. Using the data from the installed online sensing equipment, the potential flow principle and potential flow method are used to infer the water level of each node based on the water level monitoring data and the upstream and downstream relationship of the pipeline network.

[0198] b. Considering the real-time perceived rainfall data, predict the rainfall in different areas.

[0199] c. Simulate water accumulation depth and drainage conditions in different areas based on water level, rainfall and the performance of drainage facilities.

[0200] 6. Analysis and processing of water volume:

[0201] a. Using the upstream and downstream water levels and pipeline parameters, the potential flow method is used to estimate the flow rate of the pipeline.

[0202] b. Based on the existing underground drainage pipe information, estimate the drainage flow rate of different outlets.

[0203] 7. Parallel computing processing:

[0204] a. Use existing data and sensing devices to perform parallel computing in the area to improve computing efficiency and real-time performance.

[0205] 8. Real-time updates and warnings:

[0206] a. Establish a real-time update mechanism to reflect changes in perception data and support flood control emergency decision-making.

[0207] b. Provide real-time flood warning information, including the scope of waterlogging and the depth of waterlogging, and provide key data support for urban flood control and emergency response.

[0208] Through the above example, the drainage situation in this area of ​​City B is monitored in real time, a flood model is established, and decision support is provided to reduce potential flood risks. This method makes the city more capable of responding to extreme rainfall events.

[0209] In summary, the technical solution provided by this application may include the following beneficial effects:

[0210] 1. The flood and waterlogging model uses the potential flow principle to simulate water flow. Compared with traditional complex numerical models, it is simpler and more efficient. It can simplify the complexity of flood and waterlogging modeling, and achieve rapid model construction through reasonable simplification and estimation of model parameters. It can reduce computational complexity while maintaining model accuracy, which helps to accurately predict the occurrence and development of flood events, take emergency measures in advance, and reduce losses.

[0211] 2. Hydrological coupling processing is performed based on the processing results of adaptive grid division, gutter information, terrain data and pipeline information network, which can automatically establish a flood waterlogging model without manually building a complex model structure, greatly reducing the time and workload of modeling;

[0212] 3. The flood waterlogging model is based on the principles of gravity flow and potential flow, and comprehensively considers the water flow conditions in the entire basin, including rivers, pipes, ditches and surface runoff, providing a comprehensive flood waterlogging analysis rather than just an analysis of specific points. It also effectively simplifies complex water flow calculation problems, reduces calculation costs, and improves the calculation efficiency of the model;

[0213] 4. Through data assimilation technology, the measured data is combined with the numerical model to update the model parameters of the flood waterlogging model in real time, thus improving the applicability and accuracy of the model;

[0214] 5. The flood waterlogging model is divided into multiple sub-models, and parallel computing technology is used to improve the computing speed, so that the flood waterlogging model can handle larger and more complex geographical areas, and enhance the practicality of the model;

[0215] 6. Rapid risk assessment can be carried out based on the output results of the flood waterlogging model, and intelligent risk assessment and emergency response plans can be introduced to carry out real-time flood warnings and provide targeted emergency response suggestions, thus improving the efficiency of disaster management;

[0216] 7. The output results of the flood and waterlogging model can be presented in an intuitive way, providing decision makers and the public with an intuitive display of the flood situation and data visualization so that they can take necessary measures and make decisions. In addition, a cross-border cooperation platform can be established based on the flood and waterlogging model to promote cooperation and information sharing between different regions and institutions, which will contribute to more extensive flood monitoring and early warning, and improve the ability to respond to disasters.

[0217] Figure 5 This is a structural block diagram of a flood waterlogging rapid modeling device according to an exemplary embodiment. The device includes:

[0218] Flood and waterlogging data source information acquisition module 501, used to collect flood and waterlogging data source information of the target analysis area;

[0219] The flood waterlogging model building module 502 is used to complete the flood waterlogging data source information, to establish a flood waterlogging model according to the completion processing result of the flood waterlogging data source information, and to evaluate the flood risk of the target analysis area through the flood waterlogging model;

[0220] The model parameter correction module 503 is used to obtain the water volume observation data of the target analysis area, and combine the water volume observation data with the flood waterlogging model to correct the model parameters of the flood waterlogging model in real time.

[0221] In a possible implementation manner, the flood waterlogging data source information includes geographic information data, pipeline network information, and hydraulic control facility information.

[0222] In a possible implementation, the completion process includes an adaptive grid division process; the flood waterlogging model establishment module 502 is further used to:

[0223] Based on the flood and waterlogging data source information of the target analysis area, the terrain complexity distribution of the target analysis area is obtained;

[0224] According to the distribution of the complexity of the terrain, the target analysis area is subjected to adaptive meshing processing to obtain meshes in each area.

[0225] In a possible implementation, the completion processing includes terrain data processing; the flood waterlogging model building module 502 is also used to:

[0226] The terrain data in the geographic information data is processed by digital elevation model to generate elevation information and ground elevation information of each watershed area in the target analysis area, and to extract the water system network.

[0227] In a possible implementation, the completion process includes pipeline information network processing; the flood waterlogging model establishment module 502 is also used for:

[0228] Performing graph theory and network analysis on the pipeline network information to generate a pipeline topology structure of the target analysis area;

[0229] The pipeline topology structure is topologically sorted to obtain the upstream and downstream relationships of the pipelines in the target analysis area.

[0230] In a possible implementation, the completion process includes gutter information processing; the flood waterlogging model building module 502 is also used for:

[0231] The gutter topology structure is established according to the road surface information in the geographic information data, and the gutter segmentation processing is performed according to the ground elevation information.

[0232] In a possible implementation manner, the flood waterlogging model building module 502 is further used to:

[0233] The completed processing results of the flood and waterlogging data source information are subjected to hydrological coupling processing to establish a flood and waterlogging model of pipe network-ditch-river-lake.

[0234] In a possible implementation manner, the flood waterlogging model building module 502 is further used to:

[0235] According to the water volume observation data and the water levels upstream and downstream of the pipeline, the water levels of each node in the target analysis area are inferred based on the gravity flow principle and the potential flow principle to perform hydraulic analysis on the target analysis area;

[0236] According to the upstream and downstream water levels of the pipeline and the Manning coefficient of the pipeline, the pipeline flow rate is estimated based on the principle of potential flow;

[0237] According to the pipeline topology and the pipeline flow, the water volume of each water catchment area is obtained to perform water volume analysis on the target analysis area;

[0238] Based on the water volume of each catchment area and the water volume observation data, the runoff volume and runoff coefficient of each catchment area are estimated to determine the flood formation process of the target analysis area and evaluate the flood risk of the target analysis area.

[0239] In a possible implementation, the water volume observation data includes water level data, flow data and rainfall data; the model parameter correction module 503 includes:

[0240] Data assimilation processing is performed on the flood waterlogging model based on the water volume observation data to correct model parameters of the flood waterlogging model in real time.

[0241] In a possible implementation, the device is also used for:

[0242] According to the regional grids divided by the target analysis area, the flood waterlogging model is spatially divided into a plurality of model grids, and the plurality of model grids are subjected to parallel calculation processing.

[0243] In summary, the technical solution provided by this application may include the following beneficial effects:

[0244] 1. The flood and waterlogging model uses the potential flow principle to simulate water flow. Compared with traditional complex numerical models, it is simpler and more efficient. It can simplify the complexity of flood and waterlogging modeling, and achieve rapid model construction through reasonable simplification and estimation of model parameters. It can reduce computational complexity while maintaining model accuracy, which helps to accurately predict the occurrence and development of flood events, take emergency measures in advance, and reduce losses.

[0245] 2. Hydrological coupling processing is performed based on the processing results of adaptive grid division, gutter information, terrain data and pipeline information network, which can automatically establish a flood waterlogging model without manually building a complex model structure, greatly reducing the time and workload of modeling;

[0246] 3. The flood waterlogging model is based on the principles of gravity flow and potential flow, and comprehensively considers the water flow conditions in the entire basin, including rivers, pipes, ditches and surface runoff, providing a comprehensive flood waterlogging analysis rather than just an analysis of specific points. It also effectively simplifies complex water flow calculation problems, reduces calculation costs, and improves the calculation efficiency of the model;

[0247] 4. Through data assimilation technology, the measured data is combined with the numerical model to update the model parameters of the flood waterlogging model in real time, thus improving the applicability and accuracy of the model;

[0248] 5. The flood waterlogging model is divided into multiple sub-models, and parallel computing technology is used to improve the computing speed, so that the flood waterlogging model can handle larger and more complex geographical areas, and enhance the practicality of the model;

[0249] 6. Rapid risk assessment can be carried out based on the output results of the flood waterlogging model, and intelligent risk assessment and emergency response plans can be introduced to carry out real-time flood warnings and provide targeted emergency response suggestions, thus improving the efficiency of disaster management;

[0250] 7. The output results of the flood and waterlogging model can be presented in an intuitive way, providing decision makers and the public with an intuitive display of the flood situation and data visualization so that they can take necessary measures and make decisions. In addition, a cross-border cooperation platform can be established based on the flood and waterlogging model to promote cooperation and information sharing between different regions and institutions, which will contribute to more extensive flood monitoring and early warning, and improve the ability to respond to disasters.

[0251] See also Figure 6 , which is a schematic diagram of a computer device provided according to an exemplary embodiment of the present application, the computer device includes a memory and a processor, the memory is used to store a computer program, and when the computer program is executed by the processor, the above-mentioned method for rapid modeling of flood waterlogging is implemented.

[0252] The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0253] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the method in the implementation of the present application. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory, that is, implementing the method in the above method implementation.

[0254] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0255] In an exemplary embodiment, a computer-readable storage medium is also provided, which is used to store at least one computer program, and the at least one computer program is loaded and executed by a processor to implement all or part of the steps in the above method. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0256] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0257] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for rapid modeling of flood waterlogging, characterized in that: The method comprises: Collecting flood and waterlogging data source information in the target analysis area; the flood and waterlogging data source information includes geographic information data, pipeline network information, and hydraulic control facility information; Complete the flood and waterlogging data source information to establish a flood and waterlogging model according to the complete processing result of the flood and waterlogging data source information, and evaluate the flood risk of the target analysis area through the flood and waterlogging model; Acquire water volume observation data of the target analysis area, and combine the water volume observation data with the flood waterlogging model to correct the model parameters of the flood waterlogging model in real time; The completion processing includes adaptive grid division processing; the completion processing of the flood waterlogging data source information includes: Based on the flood and waterlogging data source information of the target analysis area, obtaining the terrain complexity distribution of the target analysis area; According to the distribution of the complexity of the terrain, the target analysis area is subjected to adaptive grid division processing to obtain grids of each area; The completion processing includes terrain data processing; the completion processing of the flood and waterlogging data source information includes: Performing digital elevation model processing on the terrain data in the geographic information data to generate elevation information and ground elevation information of each watershed area in the target analysis area, and extracting the water system network; The completion processing includes pipeline information network processing; the completion processing of the flood waterlogging data source information includes: Performing graph theory and network analysis on the pipeline network information to generate a pipeline topology structure of the target analysis area; Performing topological sorting processing on the pipeline topology structure to obtain the upstream and downstream relationship of the pipelines in the target analysis area; The completion processing includes pipeline information network processing; the completion processing of the flood waterlogging data source information includes: Performing graph theory and network analysis on the pipeline network information to generate a pipeline topology structure of the target analysis area; Performing topological sorting processing on the pipeline topology structure to obtain the upstream and downstream relationship of the pipelines in the target analysis area; The completion processing includes gutter information processing; the completion processing of the flood waterlogging data source information includes: Establishing a gutter topology structure according to the road surface information in the geographic information data, and performing gutter segmentation processing according to the ground elevation information; The step of evaluating the flood risk of the target analysis area by using the flood waterlogging model includes: According to the water volume observation data and the water levels upstream and downstream of the pipeline, the water levels of each node in the target analysis area are inferred based on the gravity flow principle and the potential flow principle to perform hydraulic analysis on the target analysis area; According to the upstream and downstream water levels of the pipeline and the Manning coefficient of the pipeline, the pipeline flow rate is estimated based on the potential flow principle; According to the pipeline topology and the pipeline flow, the water volume of each water catchment area is obtained to perform water volume analysis on the target analysis area; According to the water volume of each catchment area and the water volume observation data, the runoff volume and runoff coefficient of each catchment area are estimated to determine the flood formation process of the target analysis area and evaluate the flood risk of the target analysis area; The method of inferring the water level of each node in the target analysis area based on the water volume observation data and the upstream and downstream water levels of the pipeline based on the gravity flow principle and the potential flow principle includes: Estimate fluid velocity and pressure distribution in pipes, ditches, or rivers based on Bernoulli's equation; Calculate the potential function or stream function based on the Laplace equation using the fluid velocity and pressure distribution information; Calculate fluid velocity distribution using the potential function or stream function; The fluid velocity profile and gravity flow principles are combined and the Bernoulli equation is applied to estimate the water level at each node.

2. The method according to claim 1, characterized in that The method of establishing a flood model according to the completed processing result of the flood data source information comprises: The completed processing result of the flood and waterlogging data source information is subjected to hydrological coupling processing to establish a flood and waterlogging model of a pipe network-ditch-river-lake.

3. The method according to claim 1, characterized in that The water volume observation data includes water level data, flow data and rainfall data; the processing of combining the water volume observation data with the flood waterlogging model to correct the model parameters of the flood waterlogging model in real time includes: Data assimilation processing is performed on the flood waterlogging model based on the water volume observation data to correct model parameters of the flood waterlogging model in real time.

4. The method according to claim 1, characterized in that The method further comprises: According to the regional grids divided into the target analysis area, the flood waterlogging model is spatially divided into a plurality of model grids, and parallel calculation processing is performed on the plurality of model grids.

Images