Hydraulic runoff modeling method capable of reducing dimensions and simplifying, electronic equipment and storage medium

Abstract

The invention discloses a hydraulic runoff modeling method capable of reducing dimensions and simplifying, electronic equipment and a storage medium, and belongs to the technical field of water environment engineering. In order to improve the efficiency of urban hydraulic runoff modeling, the method comprises the following steps: acquiring rainfall time sequence data, raster data of an underlying surface digital elevation model (DEM) and underlying surface land utilization data; dividing and constructing a square grid, four vertex positions of each square grid, and a plane coordinate value and an elevation value at the geometric center of each square grid; model simplification topology construction is carried out, simulation inspection well nodes, simulation pipe duct connection and simulation sub catchment area polygons are constructed based on an SWMM model, and all simulation sub catchment area polygons are associated with the simulation inspection well nodes located in the centers of the simulation sub catchment area polygons; the method comprises the following steps: calculating runoff of each grid, calculating runoff of each grid, taking rainfall time sequence data as input of an SWMM model, constructing an SWMM simulation rain gauge, constructing an SWMM rainfall flood model, and calculating to obtain real-time dynamic simulation water quantity and water depth data in each grid. The simulation calculation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of water environment engineering, and in particular relates to a hydraulic runoff modeling method capable of reducing dimension and simplifying, an electronic device and a storage medium. Background Art

[0002] Urban hydraulic runoff refers to the flow state of rainwater in urban areas under the action of surface runoff, infiltration, evaporation, etc. It is one of the important causes of urban waterlogging and an indispensable factor in the design and planning of urban drainage systems and waterlogging prevention. With the acceleration of urbanization, the problem of urban waterlogging caused by rainstorm hydraulic runoff has become increasingly prominent, which not only affects the lives and property safety of citizens, but also poses a serious threat to urban infrastructure.

[0003] The rapid modeling of urban hydraulic runoff is to meet the needs of urban drainage system design and planning and waterlogging risk assessment and early warning, so as to improve the city's flood control and drainage capabilities and ensure the safety of urban infrastructure and residents' lives. In the existing technology, the establishment of urban hydraulic runoff models often requires a lot of time and manpower costs, and there are problems such as complex model construction, cumbersome data processing, and low calculation efficiency. Summary of the invention

[0004] The problem to be solved by the present invention is to improve the efficiency of urban hydraulic runoff modeling, and propose a hydraulic runoff modeling method, electronic equipment and storage medium that can reduce dimensionality and simplify.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions:

[0006] A hydraulic runoff modeling method capable of reducing dimension and simplifying includes the following steps:

[0007] S1. Collect data of the modeling area, including rainfall time series data, raster data of the underlying surface digital elevation model (DEM), and underlying surface land use data;

[0008] S2. Divide and construct a grid. Based on the spatial resolution of the DEM grid data obtained in step S1, divide the modeling area into grids of the same size as the DEM grid, and obtain the four vertex positions of each grid, the plane coordinate value and the elevation value at the geometric center of each grid;

[0009] S3. Simplify the topology of the model. Based on the SWMM model, a simulated manhole node is constructed at the geometric center of each square obtained in step S2. Each adjacent simulated manhole node is connected by a simulated pipe. A simulated subcatchment polygon is constructed with the four vertices of each square as inflection points. Each simulated subcatchment polygon is associated with the simulated manhole node at its center.

[0010] S4. Calculate the runoff of each grid, use the precipitation time series data as the input of the SWMM model, build a SWMM simulated rain gauge, based on the underlying land use and DEM slope data of each grid, integrate the underlying land infiltration and evaporation characteristics data, optimize the SWMM model sub-catchment area object, combine the simulated inspection well nodes, simulated pipe connections and simulated sub-catchment area polygons created in step S3, build a SWMM stormwater model, and calculate the real-time dynamic simulated water volume and water depth data in each grid.

[0011] Furthermore, the specific implementation method of step S1 includes the following steps:

[0012] S1.1. Collect raster data of the digital elevation model (DEM) and digital orthophoto (DOM) of the modeling area, and set the spatial resolution to 5 meters;

[0013] S1.2. Using ArcGIS or ENVI remote sensing image interpretation software, interpret the raster data of the digital orthophoto map DOM obtained in step S1.1 according to the image features to generate raster data of land use classification plots;

[0014] S1.3. Collect rainfall time series data through rain gauge monitoring equipment or calculate the simulated return period heavy rainfall time series data through the local rainstorm intensity formula in the modeling area. The calculation expression is:

[0015]

[0016] Among them, q is the design rainfall intensity, a is the rain force parameter, t is the rainfall duration, C is the rainfall force variation parameter, P is the recurrence period, b is the precipitation duration correction parameter, and n is the rainstorm attenuation index.

[0017] Furthermore, the land use classification in step S1.2 includes bare land, grassland, cultivated land, forest land, artificial surface and water body.

[0018] Furthermore, the specific implementation method of step S2 includes the following steps:

[0019] S2.1. Traverse the grid cells in the grid data of the digital elevation model DEM obtained in step S1, select the grid cells falling into the modeling area, and divide the modeling area into square grids with a size equal to the grid size of the DEM;

[0020] S2.2. Based on the plane coordinates of the four vertices of each grid cell obtained in step S2.1 as the vertex positions of the grid, construct grid vector data, where the side length of each grid is d, and calculate the plane coordinate value and elevation value at the geometric center of each grid.

[0021] Furthermore, the specific implementation method of step S3 includes the following steps:

[0022] S3.1. Use the plane coordinate values ​​and elevation values ​​at the geometric center of each grid obtained in step S2 as the X coordinate value, Y coordinate value, and inner bottom elevation value of the SWMM simulated inspection well node object, where X is the horizontal abscissa and Y is the horizontal ordinate. Then construct a simulated inspection well node, set the initial maximum well depth to the difference h between the maximum elevation and the minimum elevation of the modeling area, and obtain the simulated inspection well node of the modeling area;

[0023] S3.2. Create a simulated pipe connection object to connect the adjacent simulated manhole nodes obtained in step S3.1. The upstream node of the simulated pipe connection object is selected from the adjacent simulated manhole nodes with a larger elevation, and the downstream node is selected from the adjacent simulated manhole nodes with a smaller elevation. Set the cross-sectional shape of the simulated pipe connection object to a rectangle, and the width of the simulated pipe connection object is d, the maximum height is h, and the length is the length of the line between adjacent simulated manhole nodes L;

[0024] S3.3. Use the plane coordinate values ​​of the four vertices of each grid obtained in step S2 as the inflection point coordinates of the SWMM simulated sub-catchment polygon to construct the simulated sub-catchment polygon, and set the outlet of each simulated sub-catchment to the simulated inspection well node located at its center to associate each simulated sub-catchment polygon with the simulated inspection well node located at its center.

[0025] Furthermore, the specific implementation method of step S4 includes the following steps:

[0026] S4.1. Construct a SWMM simulated rain gauge and input the rainfall time series data collected in step S1 into the SWMM simulated rain gauge;

[0027] S4.2. Import the DEM raster data obtained in step S1, the land use classification plot raster data, the grid vector data obtained in step S2, and the simulated sub-catchment polygon obtained in step S3 into ArcGIS software for superposition, convert the DEM raster data into slope raster data using the slope calculation tool of ArcGIS, and add the sub-catchment model parameter field to the simulated sub-catchment polygon using ArcGIS;

[0028] S4.3. Using the table display zoning statistics tool of ArcGIS, analyze the slope grid data and grid vector data superimposed in step S4.2, generate a zoning statistics table of average percentage slopes corresponding to each grid of the grid, and obtain the slope value in each grid;

[0029] S4.4. Using ArcGIS's table display zoning statistics tool, analyze the land use classification plot raster data and grid vector data superimposed in step S4.2 to obtain a statistical table of land use classification areas corresponding to each grid grid; divide the land use type into permeable areas and impermeable areas, the permeable areas include bare land, grassland, cultivated land, and forest land, and the impermeable areas include artificial surfaces and water bodies, calculate the weighted coefficient value of each land use classification area in the land use classification area statistical table, and obtain a statistical table of land use classification area weighted coefficients, the calculation expression is:

[0030]

[0031] Among them, w perv is the weighted coefficient value of the i-th land use classification area in the infiltration area type, w imp is the weighted coefficient value of the j-th land use classification area in the infiltration area type, S i is the land use classification area of ​​the i-th type in the infiltration area type, S perv is the total area of ​​the infiltration area, S j is the land use classification area of ​​the jth type in the impervious area type, S imp is the total area of ​​impervious areas;

[0032] S4.5. According to the statistical table of land use classification area weighted coefficients of the underlying surface permeable area and impervious area calculated in step S4.4, combined with the reference values ​​of soil permeable depression water storage, Manning's N value, and Horton infiltration related to different land uses given in the public "Stormwater Management Model User Manual SWMM" or reference to relevant literature manuals, calculate the underlying surface sub-catchment parameters corresponding to each grid of the grid, and batch assign them to the sub-catchment objects obtained in step S4.2;

[0033] S4.6. Export the sub-catchment data with parameter assignment completed in step S4.5, combine it with the rain gauge obtained in step S4.1, and the simulated manhole nodes and simulated pipe connections obtained in step S3, build a SWMM stormwater model, and then perform simulation calculations to obtain the real-time water volume and water depth data in the simulated manholes, which is the simulated real-time changing runoff in the corresponding grid in the grid obtained in step S2. The statistical runoff of all unit grids in the grid in the entire simulation time domain is the total runoff of the modeling area.

[0034] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a hydraulic runoff modeling method capable of dimensionality reduction and simplification are implemented.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a hydraulic runoff modeling method capable of dimensionality reduction and simplification.

[0036] Beneficial effects of the present invention:

[0037] The hydraulic runoff modeling method capable of dimensionality reduction and simplification described in the present invention is to simplify the surface two-dimensional hydrodynamic simulation grid by converting it into nodes and sub-catchment areas of a one-dimensional pipe network, connect the grids through simplified pipes, construct a SWMM one-dimensional pipe network hydraulic model, and quickly simulate and calculate the dynamic real-time flow production and runoff data of the surface two-dimensional grid to obtain real-time simulated runoff and total runoff. The present invention effectively reduces the modeling complexity of surface two-dimensional grid runoff modeling, improves the efficiency of modeling and simulation calculation, reduces the workload, and saves costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of a hydraulic runoff modeling method capable of dimensionality reduction and simplification according to the present invention;

[0039] Figure 2 A schematic diagram of the topological structure of the simplified two-dimensional grid model of the surface constructed by the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0041] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected specific embodiments of the present invention. Based on the specific embodiments of the present invention, all other specific embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0042] In order to further understand the content, features and effects of the present invention, the following specific implementation methods are given as examples, and the attached Figure 1 -Attached Figure 2 The detailed instructions are as follows:

[0043] Embodiment 1:

[0044] A hydraulic runoff modeling method capable of reducing dimension and simplifying includes the following steps:

[0045] S1. Collect data of the modeling area, including rainfall time series data, raster data of the underlying surface digital elevation model (DEM), and underlying surface land use data;

[0046] Furthermore, the specific implementation method of step S1 includes the following steps:

[0047] S1.1. Collect raster data of the digital elevation model (DEM) and digital orthophoto (DOM) of the modeling area, and set the spatial resolution to 5 meters;

[0048] S1.2. Using ArcGIS or ENVI remote sensing image interpretation software, interpret the raster data of the digital orthophoto map DOM obtained in step S1.1 according to the image features to generate raster data of land use classification plots;

[0049] Furthermore, the land use classification in step S1.2 includes bare land, grassland, cultivated land, forest land, artificial surface and water body;

[0050] S1.3. Collect rainfall time series data through rain gauge monitoring equipment or calculate the simulated return period heavy rainfall time series data through the local rainstorm intensity formula in the modeling area. The calculation expression is:

[0051]

[0052] Among them, q is the design rainfall intensity, a is the rain force parameter, t is the rainfall duration, C is the rainfall change parameter, P is the return period, b is the precipitation duration correction parameter, and n is the rainstorm attenuation index;

[0053] S2. Divide and construct a grid. Based on the spatial resolution of the DEM grid data obtained in step S1, divide the modeling area into grids of the same size as the DEM grid, and obtain the four vertex positions of each grid, the plane coordinate value and the elevation value at the geometric center of each grid;

[0054] Furthermore, the specific implementation method of step S2 includes the following steps:

[0055] S2.1. Traverse the grid cells in the grid data of the digital elevation model DEM obtained in step S1, select the grid cells falling into the modeling area, and divide the modeling area into square grids with a size equal to the grid size of the DEM;

[0056] S2.2. According to the plane coordinates of the four vertices of each grid cell obtained in step S2.1 as the vertex positions of the grid, construct grid vector data, where the side length of each grid is d, and calculate the plane coordinate value and elevation value at the geometric center of each grid;

[0057] S3. Simplify the topology of the model. Based on the SWMM model, a simulated manhole node is constructed at the geometric center of each square obtained in step S2. Each adjacent simulated manhole node is connected by a simulated pipe. A simulated subcatchment polygon is constructed with the four vertices of each square as inflection points. Each simulated subcatchment polygon is associated with the simulated manhole node at its center.

[0058] Furthermore, the specific implementation method of step S3 includes the following steps:

[0059] S3.1. Use the plane coordinate values ​​and elevation values ​​at the geometric center of each grid obtained in step S2 as the X coordinate value, Y coordinate value, and inner bottom elevation value of the SWMM simulated inspection well node object, where X is the horizontal abscissa and Y is the horizontal ordinate. Then construct a simulated inspection well node, set the initial maximum well depth to the difference h between the maximum elevation and the minimum elevation of the modeling area, and obtain the simulated inspection well node of the modeling area;

[0060] S3.2. Create a simulated pipe connection object to connect the adjacent simulated manhole nodes obtained in step S3.1. The upstream node of the simulated pipe connection object is selected from the adjacent simulated manhole nodes with a larger elevation, and the downstream node is selected from the adjacent simulated manhole nodes with a smaller elevation. Set the cross-sectional shape of the simulated pipe connection object to a rectangle, and the width of the simulated pipe connection object is d, the maximum height is h, and the length is the length of the line between adjacent simulated manhole nodes L;

[0061] S3.3. Use the plane coordinate values ​​of the four vertices of each grid obtained in step S2 as the inflection point coordinates of the SWMM simulated subcatchment polygon to construct the simulated subcatchment polygon, and set the outlet of each simulated subcatchment to the simulated inspection well node located at the center thereof to associate each simulated subcatchment polygon with the simulated inspection well node located at the center thereof;

[0062] S4. Calculate the runoff of each grid, use the precipitation time series data as the input of the SWMM model, build a SWMM simulated rain gauge, based on the underlying land use and DEM slope data of each grid, integrate the underlying land infiltration and evaporation characteristics data, optimize the SWMM model sub-catchment area object, combine the simulated inspection well nodes, simulated pipe connections and simulated sub-catchment area polygons created in step S3, build a SWMM stormwater model, and calculate the real-time dynamic simulated water volume and water depth data in each grid.

[0063] Furthermore, the specific implementation method of step S4 includes the following steps:

[0064] S4.1. Construct a SWMM simulated rain gauge and input the rainfall time series data collected in step S1 into the SWMM simulated rain gauge;

[0065] S4.2. Import the DEM raster data obtained in step S1, the land use classification plot raster data, the grid vector data obtained in step S2, and the simulated sub-catchment polygon obtained in step S3 into ArcGIS software for superposition, convert the DEM raster data into slope raster data using the slope calculation tool of ArcGIS, and add the sub-catchment model parameter field to the simulated sub-catchment polygon using ArcGIS;

[0066] Furthermore, the sub-catchment model parameter fields include slope Slope, rainfall Rainage, area Area, width characteristic width, impervable percentage Imperv, permeability N value Nperv, impervable N value Nimp, permeable depression storage Sperv, impervious depression storage Simp, maximum infiltration rate MaxRate, minimum infiltration rate MinRate, attenuation coefficient Decay, and soil drainage days DryTime;

[0067] S4.3. Using the table display zoning statistics tool of ArcGIS, analyze the slope grid data and grid vector data superimposed in step S4.2, generate a zoning statistics table of average percentage slopes corresponding to each grid of the grid, and obtain the slope value in each grid;

[0068] Furthermore, the average percentage slope partition statistics table corresponding to each unit grid is generated as slope_statistic, the associated field is the grid element ID, and the slope value slope in each unit grid is obtained;

[0069] S4.4. Using ArcGIS's table display zoning statistics tool, analyze the land use classification plot raster data and grid vector data superimposed in step S4.2 to obtain a statistical table of land use classification areas corresponding to each grid grid; divide the land use type into permeable areas and impermeable areas, the permeable areas include bare land, grassland, cultivated land, and forest land, and the impermeable areas include artificial surfaces and water bodies, calculate the weighted coefficient value of each land use classification area in the land use classification area statistical table, and obtain a statistical table of land use classification area weighted coefficients, the calculation expression is:

[0070]

[0071] Among them, w pervis the weighted coefficient value of the i-th land use classification area in the infiltration area type, w imp is the weighted coefficient value of the j-th land use classification area in the infiltration area type, S i is the land use classification area of ​​the i-th type in the infiltration area type, S perv is the total area of ​​the infiltration area, S j is the land use classification area of ​​the jth type in the impervious area type, S imp is the total area of ​​impervious areas;

[0072] Furthermore, the obtained grid cells are associated with the corresponding land use classification area statistics table area_statistic, and the associated field is the grid element ID; the weighted coefficient value of each land use classification area is calculated in the land use classification area statistics table area_statistic, and the land use classification area weighted coefficient statistics area_statistic_perv and area_statistic_imp are obtained;

[0073] S4.5. According to the statistical table of land use classification area weighted coefficients of the underlying surface permeable area and impervious area calculated in step S4.4, combined with the reference values ​​of soil permeable depression water storage, Manning's N value, and Horton infiltration related to different land uses given in the public "Stormwater Management Model User Manual SWMM (Version 5.1)" or reference to relevant literature manuals, calculate the underlying surface sub-catchment parameters corresponding to each grid of the grid, and batch assign them to the sub-catchment objects obtained in step S4.2;

[0074] S4.6. Export the sub-catchment data with parameter assignment completed in step S4.5, combine it with the rain gauge obtained in step S4.1, and the simulated manhole nodes and simulated pipe connections obtained in step S3, build a SWMM stormwater model, and then perform simulation calculations to obtain the real-time water volume and water depth data in the simulated manholes, which is the simulated real-time changing runoff in the corresponding grid in the grid obtained in step S2. The statistical runoff of all unit grids in the grid in the entire simulation time domain is the total runoff of the modeling area.

[0075] Embodiment 2:

[0076] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a hydraulic runoff modeling method capable of dimensionality reduction and simplification described in Example 1 are implemented.

[0077] The computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit, etc. Moreover, the processor is used to implement the steps of the above-mentioned hydraulic runoff modeling method that can reduce dimensionality and simplify when executing the computer program stored in the memory.

[0078] The processor may be a central processing unit (CPU), 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, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0079] The memory may mainly 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 (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0080] Embodiment 3:

[0081] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the hydraulic runoff modeling method capable of reducing dimensionality and simplifying described in Example 1 is implemented.

[0082] The computer-readable storage medium of the present invention can be any form of storage medium that can be read by a processor of a computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. A computer program is stored on the computer-readable storage medium. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned hydraulic runoff modeling method that can be simplified by reducing dimensionality can be implemented.

[0083] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer readable media do not include electric carrier signals and telecommunication signals.

[0084] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0085] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A hydraulic runoff modeling method capable of dimensionality reduction and simplification, characterized in that: The steps include: S1. Collect data of the modeling area, including rainfall time series data, raster data of the underlying surface digital elevation model (DEM), and underlying surface land use data; S2. Divide and construct a grid. Based on the spatial resolution of the DEM grid data obtained in step S1, divide the modeling area into grids of the same size as the DEM grid, and obtain the four vertex positions of each grid, the plane coordinate value and the elevation value at the geometric center of each grid; S3. Simplify the topology of the model. Based on the SWMM model, a simulated manhole node is constructed at the geometric center of each square obtained in step S2. Each adjacent simulated manhole node is connected by a simulated pipe. A simulated subcatchment polygon is constructed with the four vertices of each square as inflection points. Each simulated subcatchment polygon is associated with the simulated manhole node at its center. S4. Calculate the runoff of each grid, use the precipitation time series data as the input of the SWMM model, build a SWMM simulated rain gauge, based on the underlying land use and DEM slope data of each grid, integrate the underlying land infiltration and evaporation characteristics data, optimize the SWMM model sub-catchment area object, combine the simulated inspection well nodes, simulated pipe connections and simulated sub-catchment area polygons created in step S3, build a SWMM stormwater model, and calculate the real-time dynamic simulated water volume and water depth data in each grid.

2. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 1, characterized in that: The specific implementation method of step S1 includes the following steps: S1.

1. Collect raster data of the digital elevation model (DEM) and digital orthophoto (DOM) of the modeling area, and set the spatial resolution to 5 meters; S1.

2. Using ArcGIS or ENVI remote sensing image interpretation software, interpret the raster data of the digital orthophoto map DOM obtained in step S1.1 according to the image features to generate raster data of land use classification plots; S1.

3. Collect rainfall time series data through rain gauge monitoring equipment or calculate the simulated return period heavy rainfall time series data through the local rainstorm intensity formula in the modeling area. The calculation expression is: Among them, q is the design rainfall intensity, a is the rain force parameter, t is the rainfall duration, C is the rainfall force variation parameter, P is the recurrence period, b is the precipitation duration correction parameter, and n is the rainstorm attenuation index.

3. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 2, characterized in that: The land use classification in step S1.2 includes bare land, grassland, cultivated land, forest land, artificial surface and water body.

4. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 3, characterized in that: The specific implementation method of step S2 includes the following steps: S2.

1. Traverse the grid cells in the grid data of the digital elevation model DEM obtained in step S1, select the grid cells falling into the modeling area, and divide the modeling area into square grids with a size equal to the grid size of the DEM; S2.

2. Based on the plane coordinates of the four vertices of each grid cell obtained in step S2.1 as the vertex positions of the grid, construct grid vector data, where the side length of each grid is d, and calculate the plane coordinate value and elevation value at the geometric center of each grid.

5. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 4, characterized in that: The specific implementation method of step S3 includes the following steps: S3.

1. Use the plane coordinate values ​​and elevation values ​​at the geometric center of each grid obtained in step S2 as the X coordinate value, Y coordinate value, and inner bottom elevation value of the SWMM simulated inspection well node object, where X is the horizontal abscissa and Y is the horizontal ordinate. Then construct a simulated inspection well node, set the initial maximum well depth to the difference h between the maximum elevation and the minimum elevation of the modeling area, and obtain the simulated inspection well node of the modeling area; S3.

2. Create a simulated pipe connection object to connect the adjacent simulated manhole nodes obtained in step S3.

1. The upstream node of the simulated pipe connection object is selected from the adjacent simulated manhole nodes with a larger elevation, and the downstream node is selected from the adjacent simulated manhole nodes with a smaller elevation. Set the cross-sectional shape of the simulated pipe connection object to a rectangle, and the width of the simulated pipe connection object is d, the maximum height is h, and the length is the length of the line between adjacent simulated manhole nodes L; S3.

3. Use the plane coordinate values ​​of the four vertices of each grid obtained in step S2 as the inflection point coordinates of the SWMM simulated sub-catchment polygon to construct the simulated sub-catchment polygon, and set the outlet of each simulated sub-catchment to the simulated inspection well node located at its center to associate each simulated sub-catchment polygon with the simulated inspection well node located at its center.

6. A hydraulic runoff modeling method capable of dimensionality reduction and simplification according to claim 5, characterized in that: The specific implementation method of step S4 includes the following steps: S4.

1. Construct a SWMM simulated rain gauge and input the rainfall time series data collected in step S1 into the SWMM simulated rain gauge; S4.

2. Import the DEM raster data obtained in step S1, the land use classification plot raster data, the grid vector data obtained in step S2, and the simulated sub-catchment polygon obtained in step S3 into ArcGIS software for superposition, convert the DEM raster data into slope raster data using the slope calculation tool of ArcGIS, and add the sub-catchment model parameter field to the simulated sub-catchment polygon using ArcGIS; S4.

3. Using the table display zoning statistics tool of ArcGIS, analyze the slope grid data and grid vector data superimposed in step S4.2, generate a zoning statistics table of average percentage slopes corresponding to each grid of the grid, and obtain the slope value in each grid; S4.

4. Using ArcGIS's table display zoning statistics tool, analyze the land use classification plot raster data and grid vector data superimposed in step S4.2 to obtain a statistical table of land use classification areas corresponding to each grid grid; divide the land use type into permeable areas and impermeable areas, the permeable areas include bare land, grassland, cultivated land, and forest land, and the impermeable areas include artificial surfaces and water bodies, calculate the weighted coefficient value of each land use classification area in the land use classification area statistical table, and obtain a statistical table of land use classification area weighted coefficients, the calculation expression is: Among them, w perv is the weighted coefficient value of the i-th land use classification area in the infiltration area type, w imp is the weighted coefficient value of the j-th land use classification area in the infiltration area type, S i is the land use classification area of ​​the i-th type in the infiltration area type, S perv is the total area of ​​the infiltration area, S j is the land use classification area of ​​the jth type in the impervious area type, S imp is the total area of ​​impervious areas; S4.

5. According to the statistical table of land use classification area weighted coefficients of the underlying surface permeable area and impervious area calculated in step S4.4, combined with the reference values ​​of soil permeable depression water storage, Manning's N value, and Horton infiltration related to different land uses given in the public "Stormwater Management Model User Manual SWMM" or reference to relevant literature manuals, calculate the underlying surface sub-catchment parameters corresponding to each grid of the grid, and batch assign them to the sub-catchment objects obtained in step S4.2; S4.

6. Export the sub-catchment data with parameter assignment completed in step S4.5, combine it with the rain gauge obtained in step S4.1, and the simulated manhole nodes and simulated pipe connections obtained in step S3, build a SWMM stormwater model, and then perform simulation calculations to obtain the real-time water volume and water depth data in the simulated manholes, which is the simulated real-time changing runoff in the corresponding grid in the grid obtained in step S2. The statistical runoff of all unit grids in the grid in the entire simulation time domain is the total runoff of the modeling area.

7. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a hydraulic runoff modeling method capable of dimensionality reduction and simplification as described in any one of claims 1 to 6 when executing the computer program.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for hydraulic runoff modeling capable of dimensionality reduction and simplification as described in any one of claims 1 to 6 is implemented.

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