Waterlogging prediction method and equipment based on two-dimensional hydraulic model, and medium

Abstract

The invention discloses a waterlogging prediction method and device based on a two-dimensional hydraulic model and a medium, and the method comprises the steps: determining a plurality of data sources, integrating the plurality of data sources, and carrying out the preprocessing of the integrated data sources, so as to obtain a geographic information library; determining a preset two-dimensional hydraulic model, inputting the geographic information base into the two-dimensional hydraulic model, and deducing through the two-dimensional hydraulic model to obtain a waterlogging simulation result; and performing simulation according to the waterlogging simulation result to obtain a waterlogging simulation result, and determining a waterlogging risk map according to the waterlogging simulation result. The waterlogging decision-making capability is improved, and the potential risk area can be recognized in advance. Dynamic and refined simulation of the waterlogging process is realized, and real-time and visual information support is provided for flood control decision. Based on a risk assessment result, a waterlogging prevention and disaster reduction strategy with high pertinence and high operability is provided, and improvement of disaster response efficiency and resource optimization configuration is facilitated.

Description

Technical Field

[0001] This application relates to the technical field of urban waterlogging prevention and control, and particularly to a method, device and medium for urban waterlogging prediction based on a two-dimensional hydrodynamic model. Background Art

[0002] The frequent occurrence of urban waterlogging disasters has become a major challenge in the global urbanization process, and the resulting chain reactions have a profound impact on the economic society and ecological system. The traditional urban waterlogging prediction system overly relies on manual experience judgment, and it is difficult to accurately analyze the characteristics of the underlying surface in high-density construction areas, such as complex parameters like the sharp increase in the proportion of impervious surfaces and the weakening of the sponge effect of green spaces. Moreover, it is unable to dynamically couple the operating states of new drainage systems such as underground integrated pipe galleries and rainwater storage facilities. The real-time coupling mechanism of surface runoff, pipe network confluence, and river and lake storage and discharge during rainstorms is simplified, resulting in significant spatio-temporal deviations in risk warnings. This technical lag is becoming increasingly prominent against the background of frequent extreme climates. Existing urban waterlogging prediction and risk assessment methods often rely on manual experience and are insufficient in accurately predicting the waterlogging risks under complex urban underlying surface conditions and complex urban drainage systems. Summary of the Invention

[0003] To solve the above problems, this application proposes a method for urban waterlogging prediction based on a two-dimensional hydrodynamic model, including: determining a plurality of data sources, integrating the plurality of data sources, and preprocessing the integrated data sources to obtain a geographic information database; determining a pre-set two-dimensional hydrodynamic model, inputting the geographic information database into the two-dimensional hydrodynamic model for deduction through the two-dimensional hydrodynamic model, so as to obtain a waterlogging simulation result; simulating based on the waterlogging simulation result to obtain a waterlogging simulation result, and determining a waterlogging risk map according to the waterlogging simulation result.

[0004] In one example, the method further includes: determining two-dimensional hydrodynamic factors, where the two-dimensional hydrodynamic factors include flow velocity, water depth, and pressure distribution; processing the two-dimensional hydrodynamic factors by the finite volume method to obtain the two-dimensional hydrodynamic model, and the expression of the two-dimensional hydrodynamic model is:

[0005]

[0006] where h is the water depth, u is the flow velocity in the x direction, v is the flow velocity in the y direction, s x 、s y is the source term, and t represents time.

[0007] In one example, the expression of the source term is:

[0008]

[0009] where p ais the atmospheric pressure on the water surface, z b is the height of the riverbed bottom, τ ax 、τ ay is the wind load acting force, c x 、c y is the geostrophic Coriolis force, τ bx 、τ by is the river bottom resistance, ρ is the density of the water body.

[0010] In one example, the expression of the wind load acting force is:

[0011]

[0012] where ρ a is the air density, is the wind speed at 10 meters above the water surface, C Ds is the drag coefficient.

[0013] In one example, the expression of the river bottom resistance is:

[0014]

[0015] where n is the roughness coefficient.

[0016] In one example, simulating according to the waterlogging simulation result specifically includes: determining a pre-set simulation software, obtaining the waterlogging simulation result of the two-dimensional hydraulic model through the simulation software, and simulating according to the waterlogging simulation result to determine simulation data, where the simulation data includes the number of water accumulation points, the distribution situation, and the water accumulation depth situation; determining a pre-determined rainstorm intensity level, and determining the waterlogging simulation result according to the rainstorm intensity level and the simulation data.

[0017] In one example, before determining the waterlogging risk map according to the waterlogging simulation result, the method further includes: determining a pre-set thematic layer, performing overlay analysis on the waterlogging simulation result according to the thematic layer to determine risk indicators, where the risk indicators include the inundated area, population loss, economic loss, and ecological impact; determining the weights corresponding to the risk indicators, and determining the flood risk level according to the weights.

[0018] In one example, determining the waterlogging risk map according to the waterlogging simulation result specifically includes: determining the waterlogging risk map according to the flood risk level and the thematic layer.

[0019] On the other hand, the present application also proposes a waterlogging prediction device based on a two-dimensional hydraulic model, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the waterlogging prediction device based on the two-dimensional hydraulic model to execute: determining a plurality of data sources, integrating the plurality of data sources, and preprocessing the integrated data sources to obtain a geographic information database; determining a pre-set two-dimensional hydraulic model, inputting the geographic information database into the two-dimensional hydraulic model, and performing deduction through the two-dimensional hydraulic model to obtain a waterlogging simulation result; performing simulation based on the waterlogging simulation result to obtain a waterlogging simulation result, and determining a waterlogging risk map based on the waterlogging simulation result.

[0020] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set to: determining a plurality of data sources, integrating the plurality of data sources, and preprocessing the integrated data sources to obtain a geographic information database; determining a pre-set two-dimensional hydraulic model, inputting the geographic information database into the two-dimensional hydraulic model, and performing deduction through the two-dimensional hydraulic model to obtain a waterlogging simulation result; performing simulation based on the waterlogging simulation result to obtain a waterlogging simulation result, and determining a waterlogging risk map based on the waterlogging simulation result.

[0021] The present application integrates a variety of data sources and performs preprocessing to construct a high-quality geographic information database, providing a reliable basis for model deduction, improving data utilization rate and model input quality. The finite volume method is used to process key hydraulic factors such as flow velocity, water depth, and pressure distribution to improve the model calculation accuracy and accurately simulate complex water flow movements. The model incorporates the influence of external factors such as wind load and river bottom resistance to enhance the comprehensiveness and accuracy of prediction, making the simulation results closer to the actual scenario. Combined with simulation software, it supports waterlogging prediction for different rainstorm intensity levels, and realizes multi-dimensional risk assessment by overlaying thematic layers, providing a scientific basis for disaster prevention and mitigation. The waterlogging risk map is output to visually display the risk levels of different regions, assisting decision-makers in formulating targeted measures and enhancing the urban flood prevention ability. The present application improves the waterlogging decision-making ability, is especially applicable to plain cities and cities with frequent rainstorms, and is conducive to identifying potential risk areas in advance. It realizes dynamic and refined simulation of the waterlogging process, providing real-time and intuitive information support for flood control decision-making. Based on the risk assessment results, it provides highly targeted and operable flood prevention and mitigation strategies, which helps to improve the disaster response efficiency and optimize the allocation of resources. Description of the Drawings

[0022] The accompanying drawings described herein are used to provide a further understanding of the present application and form 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 to the present application. In the drawings:

[0023] Figure 1 is a schematic flow chart of a waterlogging prediction method based on a two-dimensional hydraulic model in an embodiment of the present application;

[0024] Figure 2 is a schematic diagram of a waterlogging prediction device based on a two-dimensional hydraulic model in an embodiment of the present application. Detailed Description of the Embodiments

[0025] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0026] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0027] As Figure 1 shown, to solve the above problems, a waterlogging prediction method based on a two-dimensional hydraulic model provided by an embodiment of the present application includes:

[0028] S101. Determine a plurality of data sources, integrate the plurality of data sources, and preprocess the integrated data sources to obtain a geographic information database.

[0029] Integrate high-precision Digital Elevation Model (DEM) and land use type data, and successively perform preprocessing steps such as coordinate system unification, data format conversion, missing value interpolation, and spatial interpolation. Finally, generate a geographic information data set that meets the input standard of the two-dimensional hydraulic model. This data set completely covers terrain file data, land use type data, and roughness data. For example, obtain 12.5-meter resolution DEM data of the city and convert it into a.dfs2 format terrain file; use Landsat 8 satellite remote sensing images to interpret land use types, covering categories such as urban land, roads, cultivated land, forest land, aquaculture ponds, and water bodies, and assign corresponding roughnesses to different land use types; based on the local rainfall intensity formula, design four rainfall scenarios with a rainfall duration of 2 hours, specifically including rainfall conditions with return periods of 2 years, 10 years, 50 years, and 100 years.

[0030] S102. Set up a pre - configured two - dimensional hydraulic model, input the geographical information database into the two - dimensional hydraulic model, and conduct deduction through the two - dimensional hydraulic model to obtain the waterlogging simulation results.

[0031] A two - dimensional unsteady flow hydraulic model is constructed based on the finite volume method. This model comprehensively considers key hydrological parameters such as flow velocity, water depth, and pressure distribution, aiming to accurately simulate the dynamic process of waterlogging. The model inputs include topographic data, roughness data, and rainstorm data, and the output can provide the time - series change process of water depth and flow velocity at any position within the modeled area, and then derive key waterlogging characteristic information such as inundation range, inundation depth, and inundation time. The expression of the model is:

[0032]

[0033]

[0034] Among them, t is time; h is water depth; x and y are the x - axis and y - axis of the plane rectangular coordinate system respectively, u is the flow velocity in the x direction; v is the flow velocity in the y direction; s x 、s y are source terms, and the expression of the source terms is:

[0035]

[0036] Among them, p a is the atmospheric pressure on the water surface, z b is the height of the river bed bottom, ρ is the density of water body, τ ax 、τ ay are wind load forces, c x 、c y are the geostrophic Coriolis forces, τ bx 、τ by are the river bottom resistances.

[0037] The expression of the wind load force is:

[0038]

[0039] Among them, ρ a is the air density, is the wind speed at 10 meters above the water surface, set to 0, C Ds is the drag coefficient, set to 12.

[0040] The expression of the geostrophic Coriolis force is:

[0041] C x =fv

[0042] C y =-fu

[0043] Among them, f is the Coriolis coefficient, is the angular velocity of the Earth's rotation, is the dimension, and the latitude of the project area is estimated at 23 degrees, is set to 7.2921×10 -5 .

[0044] The expression for the river bottom resistance is:

[0045]

[0046] Among them, n is the roughness coefficient, and the commonly set values are shown in Table 1.

[0047] Table 1, Values of the roughness coefficient

[0048]

[0049]

[0050] S103. Perform a simulation based on the described waterlogging simulation results to obtain the waterlogging simulation results, and determine the waterlogging risk map according to the described waterlogging simulation results.

[0051] The waterlogging simulation and simulation module is deeply integrated with the two-dimensional hydraulic model to ensure the efficiency and accuracy of the model equation solution. Using high-performance computing technologies such as parallel computing and distributed computing, the model equation is efficiently solved. By optimizing the algorithm and computing resource allocation, the computing efficiency is significantly improved, making large-scale waterlogging simulation possible. According to the local rainstorm intensity formula, rainstorm scenarios with different return periods are designed, such as once in 2 years, once in 5 years, once in 50 years, and once in 100 years, etc. The rainstorm scenario data is input into the module as boundary conditions to provide a basis for waterlogging accumulation simulation. The module combines the input terrain data, roughness coefficient data, and rainstorm data to dynamically calculate the temporal and spatial evolution process of waterlogging accumulation. Through high-resolution numerical calculations, the distribution and changes of water accumulation at different times and spaces are accurately simulated. During the simulation process, the number, spatial distribution characteristics, and depth changes of water accumulation points are statistically synchronized. Using visualization technology, the evolution process and inundation situation of waterlogging accumulation are intuitively displayed, providing a quantitative basis for waterlogging risk assessment. For example, after calculation in a certain southern city, under the rainstorm intensity of once in 2 years, the minimum relative error between the water depth research results of 4 water accumulation points and the simulation is 1%, and the maximum relative error is 6%. The relative errors are all less than 10%, indicating that the model has good applicability for simulating the surface inundation situation in the research area.

[0052] In one embodiment, thematic layers such as population density, building type, and distribution of important facilities are loaded on a GIS platform. Combining with the inundation area data simulated by a two-dimensional hydraulic model, the inundation area is first statistically calculated. By overlaying the inundation area with the population density data, the population quantity in the overlapping area is calculated. The number of inundated people = inundation area × population density to evaluate the scale of affected population. At the same time, the inundation area is overlaid with the building layer to estimate the economic loss. The economic loss = value per unit area of the building × inundation area. Subsequently, the weighted comprehensive evaluation method is adopted to quantitatively analyze the risks of inundated population and economic loss: The risk of inundated population is divided into four levels. For example, less than 10 people is 25 points, 10 - 100 people is 50 points, 100 - 500 people is 75 points, and more than 500 people is 100 points. The risk of property loss is also divided into four levels. For example, less than 10,000 yuan is 25 points, 10,000 - 50,000 yuan is 50 points, 50,000 - 1,000,000 yuan is 75 points, and more than 1,000,000 yuan is 100 points. And a population risk weight of 0.6 and an economic loss weight of 0.4 are assigned. The final flood risk level calculation formula is: Flood risk level = inundated population score × population weight + economic loss score × economic weight. According to the calculation results, the urban area is divided into four levels: low risk, medium risk, high risk, and extremely high risk, and is represented by different colors to form an urban waterlogging risk map for different rainstorm return periods such as once in 2 years, once in 5 years, once in 50 years, and once in 100 years. For example, for a certain city in the south, first obtain the urban population distribution data and rasterize it according to a 12.5m grid; then, according to the obtained land use type data, assign different property economic values and rasterize it according to a 12.5m grid; then, overlay the flood inundation area data of different return periods, calculate the number of inundated people and economic loss of different return periods; finally, assign different weights to population and economy, accumulate them into risk data, and divide them into four levels: low, medium, high, and extremely high from small to large, and mark them with different colors to form an urban waterlogging risk map of different return periods.

[0053] As Figure 2 shown, an embodiment of the present application also provides a waterlogging prediction device based on a two-dimensional hydraulic model, including:

[0054] At least one processor; and,

[0055] A memory communicatively connected to the at least one processor; wherein,

[0056] The memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor so that a waterlogging prediction device based on a two-dimensional hydraulic model can execute:

[0057] Determine multiple data sources, integrate the multiple data sources, and preprocess the integrated data sources to obtain a geographic information database;

[0058] Determine a pre-set two-dimensional hydraulic model, input the geographic information database into the two-dimensional hydraulic model, and perform deduction through the two-dimensional hydraulic model to obtain an urban waterlogging simulation result; [[ID=__1]] [[ID=__2]]

[0059] Perform simulation based on the urban waterlogging simulation result to obtain an urban waterlogging simulation result, and determine an urban waterlogging risk map according to the urban waterlogging simulation result. [[ID=__4]] [[ID=__5]]

[0060] The embodiment of the present application also provides a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set as: [[ID=__7]] [[ID=__8]]

[0061] Determine a plurality of data sources, integrate the plurality of data sources, and preprocess the integrated data sources to obtain a geographic information database; [[ID=__10]] [[ID=__11]]

[0062] Determine a pre-set two-dimensional hydraulic model, input the geographic information database into the two-dimensional hydraulic model, and perform deduction through the two-dimensional hydraulic model to obtain an urban waterlogging simulation result; [[ID=__13]] [[ID=__14]]

[0063] Perform simulation based on the urban waterlogging simulation result to obtain an urban waterlogging simulation result, and determine an urban waterlogging risk map according to the urban waterlogging simulation result. [[ID=__16]] [[ID=__17]]

[0064] Note: The tags ,

[0059] , etc. seem to be some kind of custom or specific tags without clear semantic meaning in this context. They are preserved as they are in the translation as per the requirements. Also, the "__" added before the ID numbers in the translation for the repeated parts is just for better visual distinction of the repeated content.In the 1990s, it was obvious to distinguish whether an improvement in a technology was an improvement in hardware (e.g., improvement in circuit structures such as diodes, transistors, switches, etc.) or an improvement in software (improvement in method flows). However, with the development of technology, many improvements in method flows today can be regarded as direct improvements in hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structures by programming the improved method flows into the hardware circuits. Therefore, it cannot be said that an improvement in a method flow cannot be implemented with a hardware entity module. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit, whose logical function is determined by the user's programming of the device. The designer can program by himself to "integrate" a digital system on a piece of PLD, without having to ask the chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called Hardware Description Language (HDL), and there is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be clear that only by slightly logically programming the method flow with the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0065] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, and embedded microcontrollers to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0066] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0067] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0068] Each embodiment in this application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0069] The devices, media, and methods provided by the embodiments of the present application correspond one-to-one. Therefore, the devices and media also have beneficial technical effects similar to those of their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be elaborated here.

[0070] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0072] These computer program instructions can 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, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0074] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0075] The memory may include non-permanent memory in the form of computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0076] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. 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 technologies, 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-transitory 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, such as modulated data signals and carrier waves.

[0077] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0078] The above are only examples of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for predicting waterlogging based on a two-dimensional hydraulic model, characterized in that, Including: Determine multiple data sources, integrate the multiple data sources, and preprocess the integrated data sources to obtain a geographic information database; Determine a pre-set two-dimensional hydraulic model, input the geographic information database into the two-dimensional hydraulic model, and perform deduction through the two-dimensional hydraulic model to obtain an urban waterlogging simulation result; Perform simulation based on the urban waterlogging simulation result to obtain an urban waterlogging simulation result, and determine an urban waterlogging risk map based on the urban waterlogging simulation result.

2. The method according to claim 1, wherein The method further includes: Determine two-dimensional hydraulic factors, where the two-dimensional hydraulic factors include flow velocity, water depth, and pressure distribution; Perform finite volume method processing on the two-dimensional hydraulic factors to obtain the two-dimensional hydraulic model, and the expression of the two-dimensional hydraulic model is: where h is the water depth, u is the flow velocity in the x direction, v is the flow velocity in the y direction, s x , s y and s are source terms, and t represents time.

3. The method according to claim 2, wherein The expression of the source term is: where p a is the atmospheric pressure on the water surface, z b is the height of the riverbed bottom, τ ax , τ ay is the wind load acting force, c x , c y is the geostrophic Coriolis force, τ bx , τ by is the riverbed resistance, and ρ is the density of the water body.

4. The method according to claim 3, characterized in that, The expression of the wind load force is: where ρ a is the air density, is the wind speed at 10 meters above the water surface, and C Ds is the drag coefficient.

5. The method according to claim 3, characterized in that, The expression of the river bottom resistance is: Where n is the roughness coefficient.

6. The method according to claim 1, characterized in that, Performing simulation according to the urban waterlogging simulation result specifically includes: Determine a pre-set simulation software, obtain the urban waterlogging simulation result of the two-dimensional hydraulic model through the simulation software, perform simulation based on the urban waterlogging simulation result to determine simulation data, and the simulation data includes the number of waterlogging points, distribution, and waterlogging depth; Determine a pre-determined rainstorm intensity level, and determine the urban waterlogging simulation result according to the rainstorm intensity level and the simulation data.

7. The method according to claim 1, characterized in that, Before determining the urban waterlogging risk map according to the urban waterlogging simulation result, the method further includes: Determine a pre-set thematic layer, perform overlay analysis on the urban waterlogging simulation result according to the thematic layer to determine risk indicators, and the risk indicators include inundated area, population loss, economic loss, and ecological impact; Determine the weights corresponding to the risk indicators, and determine the flood risk level according to the weights.

8. The method according to claim 7, wherein Determining the urban waterlogging risk map according to the urban waterlogging simulation result specifically includes: Determine the urban waterlogging risk map according to the flood risk level and the thematic layer.

9. An urban waterlogging prediction device based on a two-dimensional hydrodynamic model, characterized in that, Including: At least one processor; And, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that an urban waterlogging prediction device based on a two-dimensional hydraulic model can execute: Determine multiple data sources, integrate the multiple data sources, and preprocess the integrated data sources to obtain a geographic information database; Determine a pre-set two-dimensional hydraulic model, input the geographic information database into the two-dimensional hydraulic model, and perform deduction through the two-dimensional hydraulic model to obtain an urban waterlogging simulation result; Perform simulation based on the urban waterlogging simulation result to obtain an urban waterlogging simulation result, and determine an urban waterlogging risk map based on the urban waterlogging simulation result.

10. A non-volatile computer storage medium stores computer-executable instructions, characterized in that, The computer-executable instructions are set to: Determine multiple data sources, integrate the multiple data sources, and preprocess the integrated data sources to obtain a geographic information database; Determine a pre-set two-dimensional hydraulic model, input the geographical information database into the two-dimensional hydraulic model, and perform deduction through the two-dimensional hydraulic model to obtain an urban waterlogging simulation result; Perform simulation based on the urban waterlogging simulation result to obtain an urban waterlogging simulation result, and determine an urban waterlogging risk map according to the urban waterlogging simulation result.

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