Flood high-precision simulation method, system and device and storage medium

Abstract

The invention discloses a flood high-precision simulation method, system and device and a storage medium, and the method comprises the steps: obtaining actual measurement rainfall data and high-resolution topographic data of a target region, and determining a first hydrodynamic parameter of the target region according to the high-resolution topographic data; performing coarse-scale grid flood simulation on the target area by using the hydrodynamic model according to the first hydrodynamic parameter by taking the actually measured rainfall data as a boundary condition to obtain low-resolution flood simulation data of the target area; and inputting the low-resolution flood simulation data and the high-resolution topographic data into a pre-trained super-resolution generative adversarial network to obtain high-resolution flood simulation data of the target area. The flood simulation efficiency is improved while the simulation precision is guaranteed, and the method can be widely applied to the technical field of artificial intelligence.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a high-precision flood simulation method, system, device and storage medium. Background Art

[0002] Cities are densely populated with people and social and economic wealth. Once floods occur, they will cause more serious casualties and economic and property losses. In order to ensure social and economic sustainability, urban floods must be properly managed. Accurate and rapid simulation of floods can help accurately identify urban flood risks, scientifically guide flood disaster prevention and emergency management measures, and is crucial to reducing casualties and economic losses caused by floods. There is an urgent need to carry out research on urban flood simulation technology to achieve high-precision and rapid simulation of flood inundation risks.

[0003] Accurate and rapid simulation of flood disasters helps to accurately identify urban flood risks and scientifically implement flood disaster prevention and emergency management measures. However, among the existing flood simulation methods, the computational efficiency of the hydrodynamic model is low, which makes it difficult to meet the needs of rapid flood warning; the machine learning model has poor reliability in simulating complex flood processes due to the lack of physical mechanism constraints. Therefore, how to achieve the organic integration of mechanism models and machine learning algorithms, make up for the inherent defects of the two types of algorithms, and greatly improve the computational efficiency while ensuring accuracy is an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0005] To this end, an object of an embodiment of the present invention is to provide a high-precision flood simulation method, which improves the efficiency of flood simulation while ensuring simulation accuracy.

[0006] Another object of an embodiment of the present invention is to provide a high-precision flood simulation system.

[0007] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0008] In a first aspect, an embodiment of the present invention provides a high-precision flood simulation method, comprising the following steps:

[0009] Acquire measured rainfall data and high-resolution terrain data of a target area, and determine a first hydrodynamic parameter of the target area according to the high-resolution terrain data;

[0010] Using the measured rainfall data as boundary conditions, using a hydrodynamic model to perform a coarse-scale grid flood simulation on the target area according to the first hydrodynamic parameter, and obtaining low-resolution flood simulation data for the target area;

[0011] The low-resolution flood simulation data and the high-resolution terrain data are input into a pre-trained super-resolution generative adversarial network to obtain high-resolution flood simulation data of the target area.

[0012] Furthermore, in one embodiment of the present invention, the super-resolution generative adversarial network is trained by the following steps:

[0013] Acquire high-resolution terrain samples of a plurality of test areas, and determine second hydrodynamic parameters of the corresponding test areas according to the high-resolution terrain samples;

[0014] Randomly generate sample rainfall data, use the sample rainfall data as boundary conditions, use a hydrodynamic model to perform coarse-scale grid flood simulation and fine-scale grid flood simulation on the test area according to the second hydrodynamic parameter, and obtain low-resolution flood simulation results and high-resolution flood simulation results for each of the test areas;

[0015] A pre-constructed generative adversarial network is trained according to the low-resolution flood simulation results, the high-resolution terrain samples, and the high-resolution flood simulation results to obtain the trained super-resolution generative adversarial network.

[0016] Further, in one embodiment of the present invention, the training of a pre-constructed generative adversarial network according to the low-resolution flood simulation result, the high-resolution terrain sample, and the high-resolution flood simulation result specifically includes:

[0017] Inputting the low-resolution flood simulation results and the high-resolution terrain samples into the generator of the generative adversarial network to obtain a high-resolution flood prediction result;

[0018] Inputting the high-resolution flood prediction result and the high-resolution flood simulation result into the discriminator of the generative adversarial network to obtain a discrimination result;

[0019] determining a first loss value of the generator according to the discrimination result, and determining a second loss value of the discriminator according to the high-resolution flood prediction result and the high-resolution flood simulation result;

[0020] The parameters of the generator are updated according to the first loss value, and the parameters of the discriminator are updated according to the second loss value.

[0021] Furthermore, in one embodiment of the present invention, the generator includes a shallow feature extraction module, a deep feature extraction module and a feature fusion module, the shallow feature extraction module is used to extract shallow features of input data, the deep feature extraction module is used to extract deep features based on shallow features, and the feature fusion module is used to perform feature fusion on multiple deep features.

[0022] Further, in one embodiment of the present invention, the step of inputting the low-resolution flood simulation result and the high-resolution terrain sample into the generator of the generative adversarial network to obtain a high-resolution flood prediction result specifically includes:

[0023] Performing interpolation processing on the low-resolution flood simulation results to obtain interpolated flood simulation results having the same resolution as the high-resolution terrain sample;

[0024] The shallow feature extraction module extracts features from the interpolated flood simulation result and the high-resolution terrain sample to obtain shallow features of flood simulation and shallow features of terrain samples;

[0025] The shallow features of the flood simulation and the shallow features of the terrain samples are respectively extracted by the deep feature extraction module to obtain deep features of the flood simulation and deep features of the terrain samples;

[0026] The feature fusion module performs feature fusion on the deep features of the flood simulation and the deep features of the terrain sample to obtain spatial detail residual data;

[0027] The spatial detail residual data is injected into the interpolated flood simulation result to obtain the high-resolution flood prediction result.

[0028] Furthermore, in one embodiment of the present invention, the hydrodynamic model is:

[0029]

[0030] Where t represents time, U is a vector of conserved variables, E and G are fluxes in the x and y directions, S is the source term, h represents water level, u and v represent the average flow velocity along the water level in the x and y directions, q e is the mass source term per unit area, g represents the gravitational acceleration, S 0x and S 0y are the bottom slope terms in the x and y directions, S fx and S fy are the friction slopes in the x and y directions respectively, and n represents the Manning roughness coefficient.

[0031] Further, in one embodiment of the present invention, the measured rainfall data is used as a boundary condition, and a hydrodynamic model is used to perform a coarse-scale grid flood simulation on the target area according to the first hydrodynamic parameter to obtain low-resolution flood simulation data of the target area, which specifically includes:

[0032] Dividing the target area into a plurality of coarse-scale grids;

[0033] Using the measured rainfall data as boundary conditions, and using a hydrodynamic model to calculate water level data and flow velocity data of each of the coarse-scale grids according to the first hydrodynamic parameters;

[0034] The low-resolution flood simulation data is generated according to the water level data and the flow velocity data of each of the coarse-scale grids.

[0035] In a second aspect, an embodiment of the present invention provides a high-precision flood simulation system, including:

[0036] A data acquisition module, used to acquire measured rainfall data and high-resolution terrain data of a target area, and determine a first hydrodynamic parameter of the target area according to the high-resolution terrain data;

[0037] A low-resolution flood simulation module is used to use the measured rainfall data as boundary conditions, use a hydrodynamic model to perform a coarse-scale grid flood simulation on the target area according to the first hydrodynamic parameter, and obtain low-resolution flood simulation data of the target area;

[0038] The high-resolution flood reconstruction module is used to input the low-resolution flood simulation data and the high-resolution terrain data into a pre-trained super-resolution generative adversarial network to obtain high-resolution flood simulation data of the target area.

[0039] In a third aspect, an embodiment of the present invention provides a high-precision flood simulation device, including:

[0040] at least one processor;

[0041] at least one memory for storing at least one program;

[0042] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned high-precision flood simulation method.

[0043] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the above-mentioned high-precision flood simulation method.

[0044] The advantages and beneficial effects of the present invention will be partly given in the following description, partly become apparent from the following description, or be understood through the practice of the present invention:

[0045] The embodiment of the present invention obtains measured rainfall data and high-resolution terrain data of the target area, determines the first hydrodynamic parameter of the target area according to the high-resolution terrain data, takes the measured rainfall data as the boundary condition, uses the hydrodynamic model to perform a coarse-scale grid flood simulation on the target area according to the first hydrodynamic parameter, obtains low-resolution flood simulation data of the target area, and inputs the low-resolution flood simulation data and the high-resolution terrain data into a pre-trained super-resolution generative adversarial network to obtain high-resolution flood simulation data of the target area. The embodiment of the present invention uses the hydrodynamic model to perform a coarse-scale grid flood simulation on the target area to obtain low-resolution flood simulation data, and then inputs the low-resolution flood simulation data and the high-resolution terrain data into the super-resolution generative adversarial network for super-resolution reconstruction to obtain high-resolution flood simulation data. Compared with directly using the hydrodynamic model to perform fine-scale network flood simulation, the computational efficiency is improved, thereby improving the efficiency of flood simulation while ensuring the simulation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 A flowchart of a method for high-precision flood simulation provided by an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of the training process of a super-resolution generative adversarial network provided in an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of a generator provided by an embodiment of the present invention generating high-resolution flood prediction results;

[0050] Figure 4 A structural block diagram of a high-precision flood simulation system provided by an embodiment of the present invention;

[0051] Figure 5 A structural block diagram of a high-precision flood simulation device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0053] In the description of the present invention, the meaning of "a plurality" is two or more than two. If there is a description of "a first" or "a second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by those skilled in the art.

[0054] At present, the common flood disaster simulation methods in China are mainly divided into mechanism models represented by hydrological and hydrodynamic models and machine learning models based on various AI algorithms. The mechanism model uses numerical algorithms to solve the control equations, and the reliability of the calculation results is the best, but there are problems such as complex solution and low calculation efficiency. Machine learning models are mostly data-driven, do not need to solve complex control equations, and have higher calculation efficiency; however, machine learning models have defects such as high training difficulty, inability to ensure the conservation of physical quantities in the results, and poor simulation and fitting ability for complex processes. In view of the respective limitations of mechanism models and machine learning models, it is urgent to explore a fusion path, that is, to retain the accuracy of mechanism models and the high efficiency and flexibility of machine learning models, and develop a new generation of simulation technology that can simulate complex flood processes and ensure fast and accurate simulation results. The relevant research results can provide core technical support for the construction of urban flood rapid warning and forecasting systems.

[0055] The embodiment of the present invention trains a super-resolution neural network based on the flood simulation results of the hydrodynamic model under generalized rainfall conditions, and performs rapid flood simulation by fusing the hydrodynamic model with the super-resolution generative adversarial network, thereby achieving accurate and rapid simulation of the flood process at meter-level and sub-meter-level fine spatial resolutions.

[0056] Reference Figure 1 The embodiment of the present invention provides a high-precision flood simulation method, which specifically includes the following steps:

[0057] S101, obtaining measured rainfall data and high-resolution terrain data of a target area, and determining a first hydrodynamic parameter of the target area according to the high-resolution terrain data;

[0058] S102, using the measured rainfall data as boundary conditions, using a hydrodynamic model to perform a coarse-scale grid flood simulation on the target area according to a first hydrodynamic parameter, and obtaining low-resolution flood simulation data for the target area;

[0059] S103, inputting the low-resolution flood simulation data and the high-resolution terrain data into a pre-trained super-resolution generative adversarial network to obtain high-resolution flood simulation data of the target area.

[0060] Specifically, the embodiment of the present invention uses a hydrodynamic model to perform a coarse-scale grid flood simulation on the target area to obtain low-resolution flood simulation data. The low-resolution flood simulation data and the high-resolution terrain data are input into a super-resolution generative adversarial network for super-resolution reconstruction to obtain high-resolution flood simulation data. Compared with directly using the hydrodynamic model for fine-scale network flood simulation, the computational efficiency is improved, thereby improving the efficiency of flood simulation while ensuring the simulation accuracy.

[0061] The super-resolution generative adversarial network of the embodiment of the present invention takes high-resolution terrain and low-resolution flood simulation results such as water level and flow velocity as input, and takes high-resolution flood simulation results as output. The training process of the super-resolution generative adversarial network of the embodiment of the present invention is described below.

[0062] As an optional implementation, the super-resolution generative adversarial network is trained by the following steps:

[0063] S201, obtaining high-resolution terrain samples of multiple test areas, and determining second hydrodynamic parameters of corresponding test areas according to the high-resolution terrain samples;

[0064] S202, randomly generate sample rainfall data, use the sample rainfall data as boundary conditions, use the hydrodynamic model to perform coarse-scale grid flood simulation and fine-scale grid flood simulation on the test area according to the second hydrodynamic parameter, and obtain low-resolution flood simulation results and high-resolution flood simulation results for each test area;

[0065] S203: training a pre-constructed generative adversarial network according to the low-resolution flood simulation results, the high-resolution terrain samples, and the high-resolution flood simulation results to obtain a trained super-resolution generative adversarial network.

[0066] Specifically, sample rainfall data of different durations, rainfall types, and return periods are generated as boundary conditions to drive the hydrodynamic model to simulate floods at a grid spatial resolution of 0.5 to 8m, and obtain low-resolution flood simulation results and high-resolution flood simulation results; based on the low-resolution flood simulation results, high-resolution flood simulation results, and high-resolution terrain samples obtained by simulation, a super-resolution generative adversarial network is trained. After the training is completed, the measured rainfall of typical events is selected, and the hydrodynamic model is used to simulate at a coarse grid resolution. Based on the trained super-resolution generative adversarial network, the simulation results are super-resolved to meter-level and sub-meter-level spatial scales.

[0067] As an optional implementation, a pre-constructed generative adversarial network is trained based on the low-resolution flood simulation results, the high-resolution terrain samples, and the high-resolution flood simulation results, which specifically includes:

[0068] S2031, inputting the low-resolution flood simulation results and the high-resolution terrain samples into the generator of the generative adversarial network to obtain the high-resolution flood prediction results;

[0069] S2032, inputting the high-resolution flood prediction result and the high-resolution flood simulation result into the discriminator of the generative adversarial network to obtain a discrimination result;

[0070] S2033, determining a first loss value of the generator according to the discrimination result, and determining a second loss value of the discriminator according to the high-resolution flood prediction result and the high-resolution flood simulation result;

[0071] S2034. Update the parameters of the generator according to the first loss value, and update the parameters of the discriminator according to the second loss value.

[0072] Specifically, Figure 2 The figure shows a schematic diagram of the training process of the super-resolution generative adversarial network provided by an embodiment of the present invention, which mainly includes a generator and a discriminator. The generator extracts the depth features of the terrain and the low-resolution simulation results, generates a high-resolution flood prediction result, and the discriminator evaluates the generated high-resolution flood prediction result. In the cycle process, the generator and the discriminator compete with each other. When the generated high-resolution flood prediction result and the high-resolution flood simulation result output by the hydrodynamic model cannot be effectively distinguished by the discriminator, it means that a reliable high-resolution flood prediction result is generated, and a trained super-resolution generative adversarial network is obtained.

[0073] Further as an optional implementation, the generator includes a shallow feature extraction module, a deep feature extraction module and a feature fusion module. The shallow feature extraction module is used to extract shallow features of input data, the deep feature extraction module is used to extract deep features based on shallow features, and the feature fusion module is used to fuse multiple deep features.

[0074] Specifically, the generator includes three modules: shallow feature extraction, deep feature extraction, and feature fusion, which are used to extract deep features from terrain data and low-resolution simulation results and fuse them to generate high-resolution simulation results. The discriminator is based on a convolutional neural network architecture and is used to evaluate the difference between the generated simulation results and the real data, thereby achieving high-resolution reconstruction of the hydrodynamic simulation results.

[0075] As an optional implementation, the low-resolution flood simulation results and the high-resolution terrain samples are input into the generator of the generative adversarial network to obtain the high-resolution flood prediction results, which specifically include:

[0076] S20311, interpolating the low-resolution flood simulation results to obtain interpolated flood simulation results with the same resolution as the high-resolution terrain samples;

[0077] S20312, extracting features from the interpolated flood simulation results and the high-resolution terrain samples respectively through a shallow feature extraction module to obtain shallow features of flood simulation and shallow features of terrain samples;

[0078] S20313, extracting shallow features of flood simulation and shallow features of terrain samples respectively through a deep feature extraction module to obtain deep features of flood simulation and deep features of terrain samples;

[0079] S20314, performing feature fusion on the deep features of flood simulation and the deep features of terrain samples through a feature fusion module to obtain spatial detail residual data;

[0080] S20315. Inject the spatial detail residual data into the interpolated flood simulation results to obtain high-resolution flood prediction results.

[0081] Specifically, Figure 3 The figure shows a schematic diagram of a generator provided by an embodiment of the present invention generating a high-resolution flood prediction result. Before performing shallow feature extraction, the low-resolution simulation result is first interpolated to ensure that it has the same resolution as the terrain data; the shallow feature extraction module obtains shallow features of the terrain and the interpolated hydrodynamic simulation results based on 3x3 convolution; the deep feature extraction module is composed of a residual Swin Transformmer module (RSTB), a convolutional layer (Conv), and a jump connection layer (Patch Embedding), which are used to extract the global and local deep features of the terrain and simulation results respectively; the feature fusion module is used to fuse the deep features of the terrain and simulation results, and generate spatial detail residuals. The spatial detail residuals are fused with the interpolated low-resolution simulation results to generate high-resolution flood prediction results.

[0082] As a further optional implementation, the hydrodynamic model is:

[0083]

[0084] Where t represents time, U is a vector of conserved variables, E and G are fluxes in the x and y directions, S is the source term, h represents water level, u and v represent the average flow velocity along the water level in the x and y directions, q e is the mass source term per unit area, g represents the gravitational acceleration, S 0x and S 0y are the bottom slope terms in the x and y directions, S fx and S fy are the friction slopes in the x and y directions respectively, and n represents the Manning roughness coefficient.

[0085] As an optional implementation, the measured rainfall data is used as a boundary condition, and a hydrodynamic model is used to perform a coarse-scale grid flood simulation on the target area according to the first hydrodynamic parameter to obtain low-resolution flood simulation data of the target area, which specifically includes:

[0086] S1021, dividing the target area into a plurality of coarse-scale grids;

[0087] S1022, using the measured rainfall data as boundary conditions, and using the hydrodynamic model to calculate the water level data and flow velocity data of each coarse-scale grid according to the first hydrodynamic parameter;

[0088] S1023. Generate low-resolution flood simulation data based on the water level data and flow velocity data of each coarse-scale grid.

[0089] The above is a description of the method steps of the embodiment of the present invention. It can be appreciated that the embodiment of the present invention uses a hydrodynamic model to perform a coarse-scale grid flood simulation on the target area to obtain low-resolution flood simulation data, and then inputs the low-resolution flood simulation data and high-resolution terrain data into a super-resolution generative adversarial network for super-resolution reconstruction to obtain high-resolution flood simulation data. Compared with directly using a hydrodynamic model to perform fine-scale network flood simulation, the computational efficiency is improved, thereby improving the efficiency of flood simulation while ensuring simulation accuracy.

[0090] Reference Figure 4 The embodiment of the present invention provides a high-precision flood simulation system, including:

[0091] A data acquisition module, used to acquire measured rainfall data and high-resolution terrain data of a target area, and determine a first hydrodynamic parameter of the target area according to the high-resolution terrain data;

[0092] A low-resolution flood simulation module is used to use the measured rainfall data as boundary conditions, use the hydrodynamic model to perform coarse-scale grid flood simulation on the target area according to the first hydrodynamic parameter, and obtain low-resolution flood simulation data of the target area;

[0093] The high-resolution flood reconstruction module is used to input low-resolution flood simulation data and high-resolution terrain data into a pre-trained super-resolution generative adversarial network to obtain high-resolution flood simulation data of the target area.

[0094] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0095] Reference Figure 5 The embodiment of the present invention provides a high-precision flood simulation device, including:

[0096] at least one processor;

[0097] at least one memory for storing at least one program;

[0098] When the at least one program is executed by the at least one processor, the at least one processor implements the high-precision flood simulation method.

[0099] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0100] An embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the above-mentioned high-precision flood simulation method.

[0101] A computer-readable storage medium according to an embodiment of the present invention can execute a high-precision flood simulation method provided by an embodiment of the method of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0102] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0103] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0104] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0105] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0106] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0107] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0108] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0109] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0110] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0111] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A high-precision flood simulation method, characterized in that: The following steps are involved: Acquire measured rainfall data and high-resolution terrain data of a target area, and determine a first hydrodynamic parameter of the target area according to the high-resolution terrain data; Using the measured rainfall data as boundary conditions, using a hydrodynamic model to perform a coarse-scale grid flood simulation on the target area according to the first hydrodynamic parameter, and obtaining low-resolution flood simulation data for the target area; The low-resolution flood simulation data and the high-resolution terrain data are input into a pre-trained super-resolution generative adversarial network to obtain the high-resolution flood simulation data of the target area.

2. A high-precision flood simulation method according to claim 1, characterized in that: The super-resolution generative adversarial network is trained by the following steps: Acquire high-resolution terrain samples of a plurality of test areas, and determine second hydrodynamic parameters of the corresponding test areas according to the high-resolution terrain samples; Randomly generate sample rainfall data, use the sample rainfall data as boundary conditions, use a hydrodynamic model to perform coarse-scale grid flood simulation and fine-scale grid flood simulation on the test area according to the second hydrodynamic parameter, and obtain low-resolution flood simulation results and high-resolution flood simulation results for each of the test areas; A pre-constructed generative adversarial network is trained according to the low-resolution flood simulation results, the high-resolution terrain samples, and the high-resolution flood simulation results to obtain the trained super-resolution generative adversarial network.

3. A high-precision flood simulation method according to claim 2, characterized in that: The step of training a pre-constructed generative adversarial network according to the low-resolution flood simulation result, the high-resolution terrain sample, and the high-resolution flood simulation result specifically includes: Inputting the low-resolution flood simulation results and the high-resolution terrain samples into the generator of the generative adversarial network to obtain a high-resolution flood prediction result; Inputting the high-resolution flood prediction result and the high-resolution flood simulation result into the discriminator of the generative adversarial network to obtain a discrimination result; determining a first loss value of the generator according to the discrimination result, and determining a second loss value of the discriminator according to the high-resolution flood prediction result and the high-resolution flood simulation result; The parameters of the generator are updated according to the first loss value, and the parameters of the discriminator are updated according to the second loss value.

4. A high-precision flood simulation method according to claim 3, characterized in that: The generator includes a shallow feature extraction module, a deep feature extraction module and a feature fusion module. The shallow feature extraction module is used to extract shallow features of input data, the deep feature extraction module is used to extract deep features based on shallow features, and the feature fusion module is used to fuse multiple deep features.

5. A high-precision flood simulation method according to claim 4, characterized in that: The step of inputting the low-resolution flood simulation result and the high-resolution terrain sample into the generator of the generative adversarial network to obtain a high-resolution flood prediction result specifically includes: Performing interpolation processing on the low-resolution flood simulation results to obtain interpolated flood simulation results having the same resolution as the high-resolution terrain sample; The shallow feature extraction module extracts features from the interpolated flood simulation result and the high-resolution terrain sample to obtain shallow features of flood simulation and shallow features of terrain samples; The shallow features of the flood simulation and the shallow features of the terrain samples are respectively extracted by the deep feature extraction module to obtain deep features of the flood simulation and deep features of the terrain samples; The feature fusion module performs feature fusion on the deep features of the flood simulation and the deep features of the terrain sample to obtain spatial detail residual data; The spatial detail residual data is injected into the interpolated flood simulation result to obtain the high-resolution flood prediction result.

6. A high-precision flood simulation method according to claim 1, characterized in that: The hydrodynamic model is: Where t represents time, U is a vector of conserved variables, E and G are fluxes in the x and y directions, S is the source term, h represents water level, u and v represent the average flow velocity along the water level in the x and y directions, q e is the mass source term per unit area, g represents the gravitational acceleration, S 0x and S 0y are the bottom slope terms in the x and y directions, S fx and S fy are the friction slopes in the x and y directions respectively, and n represents the Manning roughness coefficient.

7. A high-precision flood simulation method according to any one of claims 1 to 6, characterized in that: The method of using the measured rainfall data as a boundary condition and using a hydrodynamic model to perform a coarse-scale grid flood simulation on the target area according to the first hydrodynamic parameter to obtain low-resolution flood simulation data of the target area specifically includes: Dividing the target area into a plurality of coarse-scale grids; Using the measured rainfall data as boundary conditions, and using a hydrodynamic model to calculate water level data and flow velocity data of each of the coarse-scale grids according to the first hydrodynamic parameters; The low-resolution flood simulation data is generated according to the water level data and the flow velocity data of each of the coarse-scale grids.

8. A high-precision flood simulation system, characterized in that: include: A data acquisition module, used to acquire measured rainfall data and high-resolution terrain data of a target area, and determine a first hydrodynamic parameter of the target area according to the high-resolution terrain data; A low-resolution flood simulation module is used to use the measured rainfall data as boundary conditions, use a hydrodynamic model to perform a coarse-scale grid flood simulation on the target area according to the first hydrodynamic parameter, and obtain low-resolution flood simulation data of the target area; The high-resolution flood reconstruction module is used to input the low-resolution flood simulation data and the high-resolution terrain data into a pre-trained super-resolution generative adversarial network to obtain high-resolution flood simulation data of the target area.

9. A high-precision flood simulation device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a high-precision flood simulation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to execute a high-precision flood simulation method as claimed in any one of claims 1 to 7 when executed by the processor.

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