A method for simulating runoff concentration in a plain net river area and related equipment

Abstract

The application discloses a plain net river area runoff simulation method and related equipment, and the method comprises the following steps: correcting the original DEM data of the plain net river area; generating a flow direction matrix of the whole area according to the corrected DEM data, and then calculating a catchment area matrix of the whole area; generating a binary raster water system layer according to the catchment area matrix, and performing river channel grading based on the binary raster water system layer to generate a river channel grade matrix; determining a sub-basin of a hydrological station based on the flow direction matrix and the river channel grade matrix; dividing a plurality of runoff boundaries, optimizing each sub-basin according to the runoff boundaries and a preset boundary priority, and then constructing a catchment unit partition; inputting the catchment unit partition, the corrected DEM data and meteorological data into a Topmodel model to perform simulation and calculation, obtaining a total outlet cross-section flow process line of the basin, and correcting the total outlet cross-section flow process line of the basin. The application improves the simulation precision of the whole process of the rainstorm flood in the plain river net, and can be widely applied to the technical field of hydrological simulation.

Description

Technical Field

[0001] This application relates to the field of hydrological simulation technology, and in particular to a method and related equipment for simulating runoff generation and confluence in a plain network river area. Background Technology

[0002] Plain river network areas are generally characterized by flat terrain, minimal ground gradient, crisscrossing natural waterways, interconnected river systems, and widespread distribution of surface depressions and small flatlands. Simultaneously, these areas are densely populated with water conservancy and municipal engineering projects such as roads, embankments, drainage pumping stations, stormwater outlets, and artificial drainage zones. Due to the strong effects of these artificial engineering projects on the segmentation, obstruction, and diversion of surface runoff, natural runoff patterns are significantly altered. Unlike the single-tree-like watersheds of mountainous areas, plain river network areas present major hydrological challenges, including variable runoff direction, blurred confluence boundaries, waterlogging in depressions, strong interference from artificial water control, and complex upstream and downstream hydraulic interactions. These unique hydrological characteristics influence the formation and evolution of runoff generation and confluence processes within the watershed, and are key factors limiting the accuracy of flood forecasting in plain river network areas. Therefore, runoff generation and confluence simulation in plain river network areas is particularly important.

[0003] However, existing runoff generation and runoff simulation methods have the following problems: they rely solely on a single natural topography to divide runoff units, failing to distinguish between various differentiated runoff boundaries such as natural runoff, engineering barriers, artificial drainage control, and river network reception. This makes it impossible to identify and resolve spatial overlap and conflict issues among multiple source boundaries, and to establish topological relationships of runoff units that fit actual working conditions, resulting in difficulty in realistically reproducing the complex runoff transport patterns in plain river networks. Traditional hydrological models operate offline with fixed parameters, making it impossible to use measured hydrological data to dynamically correct simulation errors in real time. As the simulation time increases, the cumulative simulation error continues to expand, affecting the simulation accuracy of the entire process of rainstorms and floods in plain river networks. Summary of the Invention

[0004] The main objective of this application is to propose a method and related equipment for simulating runoff generation and confluence in plain river networks, which can improve the accuracy of simulating the entire process of rainstorm floods in plain river networks.

[0005] To achieve the above objectives, one aspect of this application proposes a method for simulating runoff generation and confluence in plain network river areas, comprising: Obtain the original DEM data of the plain river network area, and perform correction processing on the original DEM data to obtain corrected DEM data; Based on the corrected DEM data, a flow direction matrix for the entire region is generated, and based on the flow direction matrix, the catchment area matrix for the entire region is calculated. A binary raster water system layer is generated based on the water catchment area matrix and a preset water catchment area threshold. Based on the flow direction matrix, the river raster in the binary raster water system layer is classified to generate a river level matrix. The sub-basins corresponding to each hydrological station are determined based on the flow direction matrix and the river channel hierarchy matrix. Based on the corrected DEM data and the multi-source spatial data of the plain river network area, multiple confluence boundaries are delineated; Based on multiple confluence boundaries and preset boundary priorities, each sub-basin is optimized, and a catchment unit partition is constructed based on the optimized sub-basins. The catchment unit partitions, the corrected DEM data, and the meteorological data of the plain river network area are input into the Topmodel model for simulation calculation to obtain the total outlet section flow process line of the basin. The flow process curve at the total outlet section of the basin is corrected to obtain the corrected flow process curve at the total outlet section of the basin.

[0006] In some embodiments, the corrected DEM data includes a plurality of first gratings; The step of generating a flow direction matrix for the entire region based on the corrected DEM data includes: Select one first grid cell from multiple first grid cells that has not been marked with the water outlet direction as the current center grid cell, and determine multiple neighboring grid cells corresponding to the current center grid cell; Determine the horizontal straight-line distance from the center point of the current central grid to the center points of each of the neighboring grids; Calculate the elevation difference between each of the neighboring grid cells and the current center grid cell; Based on the elevation difference and the corresponding horizontal straight-line distance, calculate the distance weight difference between each neighboring grid and the current center grid; The water outlet direction of the current central grid is determined based on the distance weight difference, and the current central grid with the determined water outlet direction is marked. Return to the step of selecting an unmarked first grid cell from multiple first grid cells as the current center grid cell, until all first grid cells are marked, collect the water outflow directions of all first grid cells, and generate a flow direction matrix for the entire region.

[0007] In some embodiments, calculating the catchment area matrix of the entire region based on the flow direction matrix includes: The water catchment area values ​​of all first grid cells in the corrected DEM data are initialized to 1; Starting from any of the first grid cells, trace the water outflow direction indicated by the flow direction matrix cell by cell to the boundary of the plain network river area, and increment the catchment area value of all the first grid cells passed along the tracing path by 1 each time. Return to the step of starting from any of the first grid cells and tracing grid by grid along the outflow direction indicated by the flow direction matrix to the boundary of the plain network river area, until all the first grid cells have been used as starting points to complete the tracing, and obtain the grid count of each of the first grid cells. Multiply the statistical count of each of the first grid cells by the actual area of ​​the first grid cell to generate the catchment area matrix of the entire region.

[0008] In some embodiments, optimizing each of the sub-basins based on a plurality of confluence boundaries and a preset boundary priority includes: The multiple confluence boundaries are rasterized according to the raster size and spatial range of the modified DEM data to obtain raster layers corresponding to the multiple confluence boundaries, wherein the raster layer includes multiple second rasters; The raster layers corresponding to multiple confluence boundaries are spatially superimposed grid by grid, and the number of boundary types covered by each second raster is counted based on the superimposition result; The number of conflict grids is determined based on the number of boundary types; The target confluence boundary of the conflict grid is determined according to the preset boundary priority. The sub-basin is optimized based on the target confluence boundary of the conflict grid.

[0009] In some embodiments, optimizing the sub-basin based on the target confluence boundary of the conflict grid includes: Based on the target confluence boundary of the conflict grid, each sub-basin is subjected to boundary cutting and local merging processing to obtain each sub-basin after cutting and merging. The drainage direction is corrected for each of the cut and merged sub-basins.

[0010] In some embodiments, the step of inputting the catchment unit zoning, the corrected DEM data, and the meteorological data of the plain river network area into the Topmodel model for simulation calculation to obtain the total outlet cross-sectional discharge process curve of the basin includes: The topographic index of the catchment unit zone is calculated based on the corrected DEM data, and the area distribution matrix of the topographic index is statistically analyzed. Based on the meteorological data and the area distribution matrix, the soil water deficit of each third grid cell in the catchment unit partition is determined time-by-time, wherein the meteorological data includes rainfall time series and evaporation time series; The yield of each of the third grid cells is determined based on the soil water shortage, and the yield of each of the third grid cells is accumulated to generate the outlet yield sequence of the catchment unit partition. The slope runoff calculation was performed on the outlet flow sequence using the isochronous flow method to obtain the outlet cross-sectional flow process line of the catchment unit partition. Based on the discharge process curve at the outlet section, the river confluence calculation is performed to obtain the discharge process curve at the total outlet section of the basin.

[0011] In some embodiments, correcting the total outlet flow process curve of the watershed to obtain a corrected total outlet flow process curve includes: Acquire historical measured flow, real-time rainfall data, and underlying surface data of the watershed within the plain network river area; The total outlet section flow process line of the basin, the real-time rainfall data and the underlying surface data are input into the preset first error prediction model, second error prediction model and third error prediction model to obtain the corresponding first error prediction result, second error prediction result and third error prediction result; Based on the first error prediction result, the second error prediction result, the third error prediction, and the historical measured traffic flow, a pre-established fusion model is trained to obtain a trained fusion model. The first error prediction result, the second error prediction result, and the third error prediction result are input into the trained fusion model to obtain the comprehensive error prediction value. The total outlet flow process curve of the basin is corrected based on the comprehensive error prediction value to obtain the corrected total outlet flow process curve of the basin.

[0012] To achieve the above objectives, another aspect of this application provides a runoff simulation device for a plain network river area, the device comprising: The data correction module is used to acquire the original DEM data of the plain river network area, and to correct the original DEM data to obtain corrected DEM data. The catchment area matrix calculation module is used to generate a flow direction matrix for the entire region based on the corrected DEM data, and to calculate the catchment area matrix for the entire region based on the flow direction matrix. The river level matrix generation module is used to generate a binary raster river system layer based on the catchment area matrix and a preset catchment area threshold, and to classify the river raster in the binary raster river system layer based on the flow direction matrix to generate a river level matrix. The sub-basin determination module is used to determine the sub-basin corresponding to each hydrological station based on the flow direction matrix and the channel hierarchy matrix. The confluence boundary delineation module is used to delineate multiple confluence boundaries based on the corrected DEM data and the multi-source spatial data of the plain river network area. The catchment unit partitioning module is used to optimize each of the sub-basins according to multiple catchment boundaries and preset boundary priorities, and to construct catchment unit partitions based on the optimized sub-basins; The simulation calculation module is used to input the catchment unit partition, the corrected DEM data and the meteorological data of the plain river network area into the Topmodel model for simulation calculation to obtain the total outlet section flow process line of the basin. The flow process line correction module is used to correct the flow process line at the total outlet section of the basin, so as to obtain the corrected flow process line at the total outlet section of the basin.

[0013] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0015] This application also discloses a computer program product or computer program, which includes computer instructions 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 execute the computer instructions, causing the computer device to perform the aforementioned method.

[0016] The embodiments of this application include at least the following beneficial effects: This application provides a method for simulating runoff generation and confluence in a plain network river area. This method first acquires the original DEM data of the plain network river area, corrects the original DEM data to obtain corrected DEM data; based on the corrected DEM data, a flow direction matrix for the entire area is generated, and based on the flow direction matrix, the catchment area matrix for the entire area is calculated; a binary raster river system layer is generated based on the catchment area matrix and a preset catchment area threshold, and based on the flow direction matrix, the river rasters in the binary raster river system layer are classified to generate a river channel level matrix; based on the flow direction matrix, the upstream confluence of each hydrological station is traced back, and based on the river channel... The hierarchical matrix identifies the confluence nodes of the upstream confluences, and then determines the sub-basins corresponding to each hydrological station based on the confluence nodes; multiple confluence boundaries are delineated based on the corrected DEM data and multi-source spatial data of the plain network river area; each sub-basin is optimized based on the multiple confluence boundaries and preset boundary priorities, and a catchment unit partition is constructed based on the optimized sub-basins; the catchment unit partitions, the corrected DEM data, and the meteorological data of the plain network river area are input into the Topmodel model for simulation calculation to obtain the total outlet section flow process line of the basin; the total outlet section flow process line of the basin is corrected to obtain the corrected total outlet section flow process line of the basin. This application uses corrected DEM data and multi-source spatial data to delineate multiple runoff boundaries. Based on these multiple runoff boundaries and preset boundary priorities, the sub-basins are optimized. Based on the optimized sub-basins, a topological relationship of catchment units that conforms to actual working conditions can be established, thereby constructing a refined catchment unit partition. This can realistically reproduce complex runoff transport patterns and improve the accuracy and rationality of catchment partitioning. The application also uses the Topmodel model to simulate and calculate the flow process line of the total outlet section of the basin and corrects it, effectively suppressing the accumulation of errors in the simulation process and improving the simulation accuracy of the entire process of rainstorm floods in plain river networks. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application; Figure 2 This is a flowchart of a runoff generation and confluence simulation method for a plain network river area provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a plain network river area runoff simulation device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application.

[0019] It is understood that the terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] Existing runoff simulation methods suffer from the following problems: they rely solely on a single natural topography to divide runoff units, failing to distinguish between various differentiated runoff boundaries such as natural runoff, engineering barriers, artificial drainage control, and river network reception. This makes it impossible to identify and resolve spatial overlap and conflict issues among multiple source boundaries, and to establish topological relationships of runoff units that closely reflect actual working conditions, resulting in an inability to accurately reproduce the complex runoff transport patterns in plain river networks. Furthermore, traditional hydrological models operate offline with fixed parameters, making it impossible to use measured hydrological data for real-time dynamic correction of simulation errors. As the simulation time increases, the cumulative simulation error continues to expand, affecting the accuracy of the entire process of typhoon flood simulation in plain river networks.

[0023] In view of this, this application provides a method and related equipment for simulating runoff generation and confluence in a plain network river area. The method first acquires the original DEM data of the plain network river area, corrects the original DEM data to obtain corrected DEM data; based on the corrected DEM data, a flow direction matrix for the entire region is generated, and based on the flow direction matrix, the catchment area matrix for the entire region is calculated; a binary raster river system layer is generated based on the catchment area matrix and a preset catchment area threshold, and based on the flow direction matrix, the river rasters in the binary raster river system layer are classified to generate a river class matrix; based on the flow direction matrix, the upstream confluence of each hydrological station is traced back, and based on the river class... The matrix identifies the confluence nodes of the upstream confluences, and then determines the sub-basins corresponding to each hydrological station based on the confluence nodes; multiple confluence boundaries are delineated based on the corrected DEM data and multi-source spatial data of the plain network river area; each sub-basin is optimized based on the multiple confluence boundaries and the preset boundary priority, and a catchment unit partition is constructed based on the optimized sub-basins; the catchment unit partition, the corrected DEM data, and the meteorological data of the plain network river area are input into the Topmodel model for simulation calculation to obtain the total outlet section flow process line of the basin; the total outlet section flow process line of the basin is corrected to obtain the corrected total outlet section flow process line of the basin. This application uses corrected DEM data and multi-source spatial data to delineate multiple runoff boundaries. Based on these multiple runoff boundaries and preset boundary priorities, the sub-basins are optimized. Based on the optimized sub-basins, a topological relationship of catchment units that conforms to actual working conditions can be established, thereby constructing a refined catchment unit partition. This can realistically reproduce complex runoff transport patterns and improve the accuracy and rationality of catchment partitioning. The application also uses the Topmodel model to simulate and calculate the flow process line of the total outlet section of the basin and corrects it, effectively suppressing the accumulation of errors in the simulation process and improving the simulation accuracy of the entire process of rainstorm floods in plain river networks.

[0024] The runoff simulation method for plain network river areas provided in this application relates to the field of hydrological simulation technology. This runoff simulation method for plain network river areas can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the runoff simulation method for plain network river areas, but is not limited to the above forms.

[0025] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0026] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided in an embodiment of this application. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0027] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0028] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0029] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc. It can also be a vehicle-mounted terminal of the various device types described above, but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment does not impose any limitations.

[0030] For example, based on Figure 1 The implementation environment shown in this application embodiment provides a method for simulating runoff generation and confluence in a plain network river area. The following description uses the application of this method in server 101 as an example. It can be understood that this method can also be applied to terminal 102.

[0031] Reference Figure 2 , Figure 2 The flowchart illustrates a method for simulating runoff generation and confluence in a plain network river area, applied to a server, as provided in this application embodiment. The execution entity of this method can be any of the aforementioned computer devices (including a server or terminal). See also... Figure 2 The method may include the following steps: S100. Obtain the original DEM data of the plain river network area, and perform correction processing on the original DEM data to obtain corrected DEM data.

[0032] DEM data is a type of terrain elevation data stored in the form of a raster matrix. It divides the plains and river network areas into regularly arranged rasters, and each raster cell records the average elevation value (altitude) of that location.

[0033] In this embodiment, the specific process for correcting the original DEM data is as follows: First, the original DEM data is scanned grid by grid, and a potential outflow point identification operation is performed on each unmarked grid: taking the current grid as the center, it is searched to see if there are any unmarked grids in its neighboring grids with an elevation value lower than the current grid; if so, the neighboring grid is marked as a potential outflow point of the current depression unit. After traversing all grids in the current depression unit, the grid with the lowest elevation value is selected from all the retrieved potential outflow points as the lowest outflow point of the depression unit. Second, the elevation value of the lowest outflow point is compared with the elevation values ​​of each grid in the current depression unit: if the elevation value of the lowest outflow point is higher than the elevation value of a certain grid in the depression unit, the depression unit is determined to be a closed depression; otherwise, if the elevation value of the lowest outflow point is not higher than the elevation value of any grid in the depression unit, the depression unit does not constitute a closed depression and no correction is required. Finally, for depression units identified as closed depressions, all grid cells with elevation values ​​lower than the lowest outflow point elevation are uniformly raised to the lowest outflow point elevation, creating a slope terrain that continuously descends from the edge towards the outflow point within the depression unit. This eliminates the terrain blockage problem that prevents water flow, ultimately obtaining the corrected DEM data.

[0034] This application embodiment identifies closed depressions and flatlands in plains and constructs continuous slopes by raising the elevation of cells within the depressions, ensuring smooth outflow of plain runoff and conforming to the runoff generation characteristics of plain areas dominated by surface runoff.

[0035] S200. Based on the corrected DEM data, generate a flow direction matrix for the entire region, and calculate the catchment area matrix for the entire region based on the flow direction matrix.

[0036] The corrected DEM data includes multiple first rasters.

[0037] In some embodiments of this application, the D8 unidirectional flow method based on a 3×3 grid analysis window is used to determine the outflow direction of each grid cell. The principle of the D8 unidirectional flow method is that water flows out of a single grid cell in only one direction and can only flow into one of its eight neighboring grid cells. For each central grid cell, the unique outflow direction is determined by calculating the steepest slope between it and its neighboring grid cells. Exemplarily, the step of generating the flow direction matrix for the entire region based on the corrected DEM data includes steps S210-S260: S210. Select one first grid cell that has not been marked with the water outlet direction from a plurality of first grid cells as the current center grid cell, and determine a plurality of neighboring grid cells corresponding to the current center grid cell; Specifically, from multiple first grids, one first grid that is not marked with the water outflow direction is selected as the current center grid. The current center grid and its eight surrounding neighboring grids together form a 3×3 analysis window to determine the eight neighboring grids corresponding to the current center grid. Among them, the eight neighboring grids are located in the eight directions of the current center grid: upper left, upper, upper right, left, right, lower left, lower, and lower right.

[0038] S220. Determine the horizontal straight-line distance from the center point of the current center grid to the center points of each of the neighboring grids; S230. Calculate the elevation difference between each of the neighboring grids and the current center grid; Specifically, by subtracting the elevation values ​​of the eight neighboring grids from the current center grid elevation value, the elevation difference between each neighboring grid and the current center grid can be obtained.

[0039] S240. Calculate the distance weight difference between each neighboring grid and the current central grid based on the elevation difference and the corresponding horizontal straight-line distance. In practice, for each neighboring grid cell, the elevation difference is divided by the corresponding horizontal straight-line distance to obtain the distance weight difference between each neighboring grid cell and the current central grid cell.

[0040] S250. Determine the water outlet direction of the current central grid cell based on the distance weight difference, and mark the current central grid cell whose water outlet direction is determined. In this embodiment of the application, the distance weight difference corresponding to each of the eight neighboring grids is compared, and the direction of the neighboring grid corresponding to the one with the largest value is selected as the unique water outlet direction of the current central grid. The water outlet direction of the current central grid with the determined water outlet direction is marked to indicate that the water outlet direction of the first grid has been determined.

[0041] S260. Return to the step of selecting a first grid cell from multiple first grid cells that has not been marked with the water outlet direction as the current center grid cell, until all first grid cells are marked, collect the water outlet directions of all first grid cells, and generate a flow direction matrix for the entire region.

[0042] Specifically, after determining the outflow direction of a current central grid cell, the process returns to repeat steps S210 to S250 until all first grid cells are marked, and the outflow directions of all first grid cells are collected to generate a flow direction matrix covering the entire area.

[0043] It is understood that the catchment area in this embodiment refers to the total area of ​​all upstream grids flowing into a certain grid, reflecting the degree of flow accumulation of that grid in the water system; further, the step of calculating the catchment area matrix of the entire region based on the flow direction matrix includes S270-S2101: S270. Initialize the water catchment area values ​​of all first grid cells in the corrected DEM data to 1; S280. Starting from any of the first grid cells, trace the water outflow direction indicated by the flow direction matrix cell by cell to the boundary of the plain network river area, and increment the water catchment area value of all the first grid cells passed along the tracing path by 1 each time. S290. Return to the step of starting from any of the first grid cells and tracing grid by grid along the outflow direction indicated by the flow direction matrix to the boundary of the plain network river area, until all the first grid cells have been used as starting points to complete the tracing, and obtain the grid count of each of the first grid cells. Specifically, each first grid cell is traversed, and each cell is used as a starting point for the path tracing and value accumulation operation in step S280. After the tracing of each starting grid cell is completed, the next grid cell that has not been used as a starting point is selected and the process continues until all first grid cells have been used as starting points for the tracing. After the traversal is completed, the water catchment area value of each first grid cell records the total number of upstream grid cells flowing into that grid cell. This value is the grid cell count for that grid cell.

[0044] S2101. Multiply the statistical number of each first grid cell by the actual area of ​​the first grid cell to generate a water catchment area matrix for the entire region.

[0045] S300. Generate a binary raster water system layer based on the water catchment area matrix and a preset water catchment area threshold, and classify the river raster in the binary raster water system layer based on the flow direction matrix to generate a river level matrix.

[0046] The catchment area threshold can be set based on the topographic features of the plain network river area.

[0047] In this embodiment, the water catchment area value of each grid in the water catchment area matrix is ​​compared with a preset water catchment area threshold one by one: if the water catchment area value of a certain grid is greater than or equal to the preset water catchment area threshold, the grid is determined to be a river grid and assigned a value of 1; if the water catchment area value of a certain grid is less than the preset water catchment area threshold, the grid is determined to be a non-river grid and assigned a value of 0 or null. After traversing all grids in the entire area and completing the above comparison and assignment operations, a binary grid water system layer is generated. The binary grid water system layer indicates the spatial location and direction of the river grids in the plain network river area in a binary form.

[0048] Furthermore, this application implements a method based on the flow direction matrix, which classifies river rasters in a binary raster drainage layer according to the Stradivarius rule, and generates a river class matrix. The steps include: identifying rasters with no tributaries flowing into them at the top as first-level rivers; when two first-level rivers converge, the downstream river class after the convergence is upgraded to second-level; when two rivers of the same level converge, the downstream river class after the convergence is upgraded by one level; when two rivers of different levels converge, the downstream river class after the convergence adopts the higher of the two. After traversing all river rasters in the binary raster drainage layer and completing the hierarchical classification and class assignment according to the above rules, a river class matrix is ​​generated. It can be understood that the river class matrix is ​​used to store the class information of all river rasters in the binary raster drainage layer.

[0049] S400. Determine the sub-basin corresponding to each hydrological station based on the flow direction matrix and the river channel hierarchy matrix; This application embodiment firstly traces the upstream confluence of each hydrological station in reverse based on the flow direction matrix, and identifies the confluence nodes of the upstream confluences based on the river hierarchy matrix. Then, based on the confluence nodes, it determines the sub-basin corresponding to each hydrological station. Specifically, it first obtains the spatial coordinates of each hydrological station within the plain network river area, matches the spatial coordinates of each hydrological station to the corresponding first grid position in the corrected DEM data, and uses this first grid as the starting point for tracing the source of the hydrological station. Based on the flow direction matrix, it performs reverse tracing grid by grid along the direction opposite to the outflow direction, i.e., from the water... Starting from the first grid cell where the hydrological station is located, the flow direction is traced backward according to the outflow direction recorded in the flow direction matrix to identify and mark all the first grid cells upstream of the first grid cell where the hydrological station is located, forming an upstream confluence grid set for each hydrological station; secondly, based on the river hierarchy matrix, the grid nodes at the intersection of the main river system and the upstream sub-river system in the upstream confluence grid set are identified, which are the confluence nodes where the sub-river system flows into the main river system; finally, the independent confluence range upstream of the confluence node is delineated using the confluence node as the dividing boundary, which is the exclusive sub-basin corresponding to the hydrological station.

[0050] S500. Based on the corrected DEM data and the multi-source spatial data of the plain river network area, multiple confluence boundaries are delineated.

[0051] In this embodiment of the application, the multi-source spatial data includes natural water system data, road and embankment engineering vector data, pump and gate outlet location data, and underlying surface data.

[0052] Specifically, in this embodiment, the river topography in the modified DEM data is first corrected by combining the modified DEM data, natural water system data, road and embankment engineering vector data, pump gate outlet location data and underlying surface data, and artificial intelligence method is used to correct the defects in the river topography in the modified DEM data. After correction, the embankment and road linear barrier layers are constructed based on the engineering vector data. Based on this, four types of confluence boundaries are divided: (1) Natural water catchment boundary: the main river identification threshold is determined, wherein the main river identification threshold is greater than the preset water catchment area threshold. The grids in the water catchment area matrix that have a water catchment area greater than or equal to the main river identification threshold are extracted as the main river grids. The river confluence nodes and the watershed outlet are used as the outlets. The upstream confluence grids of each outlet are traced in reverse based on the flow direction matrix. The sub-catchment units obtained by the traces are aggregated and merged to generate the natural water catchment boundary. (2) Engineering barrier boundary: Establish double-sided buffer zones for the road centerline and embankment centerline in the road and embankment engineering vector data respectively. Use the range of the double-sided buffer zones as the boundary to block the flow path of surface runoff across the road or embankment and generate the engineering barrier boundary. (3) Drainage control boundary: Obtain drainage zoning data and pump gate service range data of the plain network river area. Combine the pump gate outlet location data, superimpose the drainage zoning range and the pump gate service range to delineate the boundary of the artificial confluence control area and generate the drainage control boundary. (4) River network receiving boundary: Based on the flood channel defined by the river section, the receiving water body corresponding to the outlet location and the lake storage range, determine the final runoff receiving water body boundary and generate the river network receiving boundary.

[0053] S600. Optimize each of the sub-basins according to the multiple confluence boundaries and the preset boundary priority, and construct a catchment unit partition based on the optimized sub-basins.

[0054] In this embodiment, based on the generated four types of catchment boundary layers (natural catchment boundaries, engineering barrier boundaries, drainage control boundaries, and river network receiving boundaries) and the sub-basins corresponding to each hydrological station, spatial overlay analysis and a multi-level weighted decision mechanism are used to optimize each sub-basin, thereby constructing refined catchment unit partitions. In this embodiment, the preset boundary priorities, from highest to lowest, are: engineering barrier boundaries, drainage control boundaries, natural catchment boundaries, and river network receiving boundaries.

[0055] For example, the steps of optimizing each sub-basin based on multiple confluence boundaries and preset boundary priorities include S610-S650: S610. Rasterize the multiple confluence boundaries according to the raster size and spatial range of the modified DEM data to obtain a raster layer corresponding to the multiple confluence boundaries, wherein the raster layer includes multiple second rasters. S620. Overlay the raster layers corresponding to the multiple confluence boundaries grid by grid, and count the number of boundary types covered by each second raster based on the overlay result. Specifically, the four boundary raster layers are overlaid in space grid by grid. For each second raster position, the number of boundary types among the four types of confluence boundaries that cover it is counted, and the boundary types and quantities covered by each second raster are recorded.

[0056] S630. Determine the conflict grid according to the number of boundary types; Specifically, a second grid that covers two or more boundary types is identified as a conflicting grid; a second grid that covers only a single boundary type or is not covered by any boundary is identified as a non-conflicting grid.

[0057] S640. Determine the target confluence boundary of the conflict grid according to the preset boundary priority; Specifically, for conflicting grates, all boundary types covered by the conflicting grates are obtained, and a step-by-step decision is made according to a preset priority order from high to low: if the conflicting grates cover both engineering barrier boundaries and other types of boundaries, the engineering barrier boundary is taken as the target runoff boundary for the grates; if the grates do not cover engineering barrier boundaries but cover drainage control boundaries and other types of boundaries, the drainage control boundary is taken as the target runoff boundary for the grates; if the grates only cover natural catchment boundaries and river network receiving boundaries, the natural catchment boundary is taken as the target runoff boundary for the grates. Through this step-by-step decision-making, each conflicting grates is assigned a unique target runoff boundary, while non-conflicting grates retain their original single boundary type as the target runoff boundary.

[0058] S650. Optimize the sub-basin according to the target confluence boundary of the conflict grid, the steps of which include S651-S652: S651. Based on the target confluence boundary of the conflict grid, perform boundary cutting and local merging processing on each of the sub-basins to obtain each sub-basin after cutting and merging. Specifically, for each conflicting grid with a defined target confluence boundary, its assigned target return boundary is used as the basis for boundary correction. Sub-basins that intersect with the target return boundary of the conflicting grid are cut off. At the same time, for sub-basin segments with consistent boundary assignments and spatial adjacency after cutting, local merging is performed so that the grids within each merged sub-basin have consistent confluence boundary assignments.

[0059] S652. Correct the drainage direction of each sub-basin after the cutting and merging.

[0060] Furthermore, based on each optimized sub-basin, a complete hydraulic topology relationship is established from the catchment unit to the outlet, pump gate and then to the river channel, and a refined catchment unit partition is constructed based on this topology relationship.

[0061] This application first uses corrected DEM data and multi-source spatial data to delineate four confluence boundaries: natural catchment boundaries, engineering barrier boundaries, drainage control boundaries, and river network receiving boundaries. Then, based on the four confluence boundaries and boundary priorities (i.e., prioritizing engineering barrier and drainage control boundaries, and then coordinating with natural terrain boundaries), the sub-basins are optimized. Based on the optimized sub-basins, a topological relationship of catchment units that fits the actual working conditions can be established, thereby constructing a refined catchment unit partition, solving the problem of conflict between artificial engineering and natural terrain boundaries in plain areas, and accurately restoring the actual runoff path.

[0062] S700. Input the catchment unit partition, the corrected DEM data, and the meteorological data of the plain river network area into the Topmodel model for simulation calculation to obtain the total outlet section flow process line of the basin.

[0063] The Topmodel model is a watershed hydrological model built based on topographic indices and the principle of variable runoff area. It uses regional soil moisture content to determine the size and location of the dynamic runoff area. The Topmodel model uses the water balance equation and Darcy's law to describe hydrological processes and simplifies them based on three assumptions: the hydraulic gradient of groundwater level can be approximated by local surface slope; the watershed hydrological dynamics can be represented by a continuous steady state; and the saturated hydraulic conductivity of soil decreases exponentially with depth. The model combines surface flow and interflow calculations, performs unit watershed runoff calculations using the isochrone method, and employs a constant-velocity flood calculation method approximating kinematic waves for river runoff.

[0064] For example, the steps of inputting the catchment unit zoning, corrected DEM data, and meteorological data of the plain river network area into the Topmodel model for simulation calculation to obtain the total outlet section flow process curve of the watershed include S710-S750: S710. Calculate the topographic index of the catchment unit partition based on the corrected DEM data, and statistically analyze the area distribution matrix of the topographic index. It should be noted that a catchment unit partition includes multiple third grids; specifically, based on the corrected DEM data, the topographic index of each third grid in the catchment unit partition is calculated, and the expression for the topographic index is: Topography Index ; in, The uphill runoff area that flows into the third grid through a unit contour line length. The slope of the third grid section.

[0065] It is understandable that the area distribution matrix of the terrain index stores the total area of ​​the third grid corresponding to each index interval.

[0066] S720. Based on the meteorological data and the area distribution matrix, determine the soil water shortage of each third grid cell in the catchment unit partition for each time period. The meteorological data includes rainfall time series and evaporation time series.

[0067] In this embodiment, for each calculation period, based on the rainfall and evaporation during that period, and combined with the area distribution matrix of the topographic index, the soil saturation water deficit of each third grid is calculated grid by grid. The soil water deficit reflects the remaining water capacity of the third grid; the smaller the water deficit, the closer the soil is to saturation, and the more likely it is to generate runoff.

[0068] S730. Determine the flow rate of each of the third grids based on the soil water shortage, sum the flow rates of each of the third grids, and generate the outlet flow rate sequence of the water catchment unit partition. S740. The slope runoff calculation is performed on the outlet flow sequence using the isochronous flow method to obtain the outlet cross-sectional flow process line of the catchment unit partition. The principle of the isochronous flow method is as follows: It is assumed that the time it takes for the flow rate of each third grid cell within a catchment unit to reach the outlet depends only on the length of the confluence path from that third grid cell to the outlet and the surface velocity. The catchment unit is divided into several isochronous flow zones, and the flow rates within each isochronous flow zone arrive at the outlet sequentially according to their corresponding confluence times and are then superimposed. After calculation using the isochronous flow method, the flow process curve at the outlet section of each catchment unit can be derived.

[0069] S750. Based on the discharge process curve at the outlet section, perform river confluence calculation to obtain the discharge process curve at the total outlet section of the basin.

[0070] In some embodiments of this application, the outflow process curve at the outlet section is taken as the inflow, and the river confluence calculation is performed using the constant wave velocity flood calculation method of approximate kinematic waves. The confluence is superimposed step by step along the river network to obtain the total outflow process curve at the outlet section of the basin.

[0071] S800. Correct the flow process curve of the total outlet section of the basin to obtain the corrected flow process curve of the total outlet section of the basin.

[0072] In this embodiment, a multi-model fusion model is constructed using three types of machine learning models: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Backpropagation (BP) Neural Network. The model uses the total outlet section flow process line output by the Topmodel hydrological model, historical measured flow (flood) process data, hourly meteorological observation rainfall data, and regional underlying surface data as joint input features to dynamically correct the generated total outlet section flow process line. For example, the steps of correcting the total outlet section flow process line to obtain the corrected total outlet section flow process line include S810-S850: S810. Obtain historical measured flow, real-time rainfall data, and underlying surface data of the watershed within the plain network river area; Specifically, the total outlet section flow process curve of the basin output by the Topmodel model in each historical period (i.e., the initial simulated flow output by the Topmodel model) and the historical measured flow of the corresponding period are obtained. Furthermore, in this embodiment, the initial simulated flow rate for the same historical period is subtracted from the historical measured flow rate to calculate the simulation error sequence for each period. The initial simulated flow rate, simulation error sequence, rainfall time series for the corresponding period, and underlying surface data for each historical period are subjected to spatiotemporal matching and alignment preprocessing to form a standard training sample set. The standard training sample set is then divided into a training set and a validation set using the sliding window method for training the error prediction model.

[0073] S820. Input the total outlet section flow process line of the basin, the real-time rainfall data and the underlying surface data into the preset first error prediction model, second error prediction model and third error prediction model to obtain the corresponding first error prediction result, second error prediction result and third error prediction result; In the embodiments of this application, the first error prediction model is an LSTM network, the second error prediction model is a CNN network, and the third error prediction model is a BP neural network.

[0074] In some embodiments of this application, the training process of the LSTM network, CNN network, and BP neural network is as follows: using the initial simulated flow, simulated error sequence, rainfall time series, and underlying surface data of the same time period in the training set as joint input features, and the historical measured flow corresponding to that time period as the output target, the LSTM network, CNN network, and BP neural network are trained independently, and the network parameters of each network are updated using the Adam adaptive optimization algorithm. During the training process, the training weight of the samples corresponding to extreme rainstorms and floods is increased, and the training and validation of the three networks are completed to obtain the trained LSTM error prediction model (i.e., the first error model), CNN error prediction model (i.e., the second error model), and BP error prediction model (i.e., the third error model).

[0075] The process curve of the total outlet section of the basin for the current period, real-time rainfall data, and underlying surface data are input into the trained LSTM error prediction model, CNN error prediction model, and BP error prediction model, respectively, and the corresponding first error prediction result, second error prediction result, and third error prediction result are output.

[0076] S830. Based on the first error prediction result, the second error prediction result, the third error prediction, and the historical measured traffic, train the pre-established fusion model to obtain the trained fusion model; In this embodiment, the first error prediction result, the second error prediction result, and the third error prediction result are used as input features of the fusion model, and the historical measured traffic flow of the corresponding time period is used as the output target. The fusion model is trained to learn the optimal combination weights of the three models, namely the first error prediction model, the second error prediction model, and the third error prediction model, so as to minimize the prediction error after fusion and obtain the trained fusion model.

[0077] S840. Input the first error prediction result, the second error prediction result and the third error prediction result into the trained fusion model to obtain the comprehensive error prediction value. Specifically, the first error prediction result, the second error prediction result, and the third error prediction result are input into the trained fusion model. The fusion model then performs weighted fusion of the three error prediction results according to the optimal combination weights obtained during training, to obtain the comprehensive error prediction value.

[0078] S850. Correct the flow process line of the total outlet section of the basin according to the comprehensive error prediction value to obtain the corrected flow process line of the total outlet section of the basin.

[0079] Specifically, the comprehensive error prediction value is used to compensate the flow process line of the total outlet section of the basin for the current period. That is, the comprehensive error prediction value is used to compensate the flow process line (initial simulated flow) of the total outlet section for the current period, dynamically correcting the peak flow, peak time and complete flood process line of the total outlet section of the basin, and outputting the corrected flow process line of the total outlet section of the basin.

[0080] This application uses the Topmodel model to characterize the basic runoff generation and confluence patterns under terrain control, and then integrates multiple machine learning models such as LSTM, CNN, and BP to perform real-time dynamic correction on flood peak, peak time, and flood process line. This can make up for the shortcomings of single hydrological models in adapting to sudden extreme floods, improve the simulation accuracy of the entire process of rainstorm floods in plain river networks, and enable real-time prediction of flood processes in plain river network areas, thus meeting the needs of real-time flood forecasting for flood control.

[0081] To explain in detail the principles of the technical solution of this application, the overall process of this application will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principles of this application and should not be regarded as a limitation of this application.

[0082] In one specific embodiment, the runoff generation and confluence simulation process of the plain network river area of ​​this application includes the following steps: In practice, a plain area with flat terrain, dense river network, and dense dikes and drainage pumping stations can be selected as the research area.

[0083] S1. Correct and process the original DEM data: Specifically, the process begins by identifying potential outflow points for each raster in the original DEM data. This involves searching the neighboring raster cells for unmarked raster cells with lower elevations than the current raster. If such cells exist, they are marked as potential outflow points for the current depression cell. After traversing all raster cells within the current depression cell, the raster cell with the lowest elevation is selected from all potential outflow points and designated as the lowest outflow point for that depression cell. Next, if the elevation of the lowest outflow point is higher than the elevation of any raster cell within the depression cell, the depression cell is determined to be a closed depression. Finally, for depression cells determined to be closed depressions, all raster cells within them with elevations lower than the lowest outflow point are uniformly raised to the lowest outflow point's elevation, resulting in the corrected DEM data.

[0084] S2. Determining water flow direction based on the 3×3 grid D8 single-flow method: Specifically, based on the corrected DEM data, using a 3×3 DEM grid as the analysis unit, the unidirectional flow method is employed to determine the outflow direction of each grid in the corrected DEM data. Water flows out of a single grid in only one direction, merging only into the eight surrounding adjacent grids. In this embodiment, the steepest slope method is used to calculate the flow direction: the distance weight difference (grid elevation difference / grid center straight-line distance) between the current central grid and neighboring grids is calculated, and the neighboring grid corresponding to the maximum distance weight difference is selected as the sole outflow direction of the current central grid. By determining the outflow direction of each grid in the corrected DEM data, a flow direction matrix for the entire region can be generated.

[0085] S3. Calculate the catchment area of ​​the entire region: Specifically, the catchment area is the total area of ​​all upstream grids flowing into the current grid. The calculation process is as follows: First, initialize the catchment area value of all grids to 1; second, traverse the flow direction matrix, follow the water flow direction grid by grid to the boundary of the study area, and increment the catchment area of ​​all grids along the tracing path by 1 each time; after the traversal is completed, multiply the statistical count of each grid by its corresponding actual area to obtain the catchment area matrix of the entire region.

[0086] S4, River Classification: First, a catchment area threshold is set based on the topographic features of the study area. If the catchment area value of a certain grid in the catchment area matrix is ​​greater than or equal to the preset catchment area threshold, the grid is identified as a river grid and assigned a value of 1. If the catchment area value of a certain grid is less than the preset catchment area threshold, the grid is identified as a non-river grid and assigned a value of 0 or null. After traversing all grids in the entire area to complete the above comparison and assignment operations, a binary grid water system layer is generated.

[0087] Secondly, based on the flow direction matrix, the river raster in the binary raster water system layer is classified according to the Straler rule to generate a river class matrix.

[0088] S5. Four types of confluence boundary identification: Specifically, firstly, the upstream confluence of each hydrological station is traced back in reverse according to the flow direction matrix, and the confluence nodes of the upstream confluence are identified according to the river level matrix. Then, the sub-basin corresponding to each hydrological station is determined according to the confluence nodes. Secondly, by combining the corrected DEM data, natural water system data, road and embankment engineering vector data, pump and gate outlet location data, and underlying surface data, artificial intelligence methods are used to correct the defects in the river topography in the corrected DEM data. After correction, linear barrier layers for embankments and roads are constructed based on the engineering vector data. Based on this, four types of confluence boundaries are defined: natural water catchment boundaries, engineering barrier boundaries, drainage control boundaries, and river network receiving boundaries.

[0089] S6. Construct water catchment unit partitions: Specifically, the number of coverage boundaries of each grid is statistically analyzed by overlaying the four types of catchment boundary layers pixel by pixel. If a conflicting grid is identified as being covered by two or more boundaries, the target catchment boundary to which the conflicting grid belongs is determined according to the priority rule of engineering barrier boundary > drainage control boundary > natural catchment boundary > river network receiving boundary. Secondly, for each conflicting grid with a determined target catchment boundary, its target return boundary is used as the basis for boundary correction, and the sub-basins intersecting with the target return boundary of the conflicting grid are cut off. At the same time, for sub-basin segments with consistent boundary affiliation and spatial adjacency after cutting, local merging is performed, and the drainage direction of each cut and merged sub-basin is corrected. Finally, based on the sub-basins after drainage direction correction, a complete hydraulic topology relationship from the catchment unit to the outlet, pump gate and then to the river channel is established, and a refined catchment unit partition is constructed based on this topology relationship.

[0090] S7, Topmodel hydrological model runoff generation and runoff calculation: Specifically, firstly, the topographic index in the catchment unit partition is calculated, and the area distribution matrix of the topographic index is statistically analyzed. Secondly, based on the time series data of rainfall and evaporation, combined with the area distribution matrix of the topographic index, the soil water deficit of each grid is determined time-by-time based on the topographic index. The grid with the smaller the soil water deficit, the higher the priority of runoff generation. The runoff of each grid in the catchment unit partition is accumulated to generate the watershed outlet runoff sequence of the catchment unit partition. The isochronous method is used to perform slope runoff calculation on the outlet runoff sequence to derive the flow process line of the watershed outlet section.

[0091] S8. A multi-model fusion model is constructed using three types of machine learning models: Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Backpropagation Neural Network. The total outlet section flow process line output by the Topmodel hydrological model, historical measured flow (flood) process data, hourly meteorological observation rainfall data, and regional underlying surface data are used as joint input features to dynamically correct the generated total outlet section flow process line.

[0092] It should be noted that the embodiments of this application are applicable to plain river network areas with flat terrain, dense river networks, numerous dike and pumping station projects, variable runoff direction, and strong artificial drainage intervention. They can realize the simulation of the entire process of rainstorm runoff generation, slope confluence and river confluence and flood forecasting. At the same time, they can support engineering applications such as flood control scheduling, water system management, urban waterlogging prevention and control and water resource scheduling in plain areas.

[0093] In summary, this application provides a method and related equipment for simulating runoff generation and confluence in a plain network river area. The method first acquires the original DEM data of the plain network river area, then corrects the original DEM data to obtain corrected DEM data. Based on the corrected DEM data, a flow direction matrix for the entire region is generated, and the catchment area matrix for the entire region is calculated based on the flow direction matrix. A binary raster river system layer is generated based on the catchment area matrix and a preset catchment area threshold. Based on the flow direction matrix, the river rasters in the binary raster river system layer are classified to generate a river class matrix. The upstream confluence of each hydrological station is traced back based on the flow direction matrix, and the river class is then used to determine the river class. The matrix identifies the confluence nodes of the upstream confluences, and then determines the sub-basins corresponding to each hydrological station based on the confluence nodes; multiple confluence boundaries are delineated based on the corrected DEM data and multi-source spatial data of the plain network river area; each sub-basin is optimized based on the multiple confluence boundaries and the preset boundary priority, and a catchment unit partition is constructed based on the optimized sub-basins; the catchment unit partition, the corrected DEM data, and the meteorological data of the plain network river area are input into the Topmodel model for simulation calculation to obtain the total outlet section flow process line of the basin; the total outlet section flow process line of the basin is corrected to obtain the corrected total outlet section flow process line of the basin. This application uses corrected DEM data and multi-source spatial data to delineate multiple runoff boundaries. Based on these multiple runoff boundaries and preset boundary priorities, the sub-basins are optimized. Based on the optimized sub-basins, a topological relationship of catchment units that conforms to actual working conditions can be established, thereby constructing a refined catchment unit partition. This can realistically reproduce complex runoff transport patterns and improve the accuracy and rationality of catchment partitioning. The application also uses the Topmodel model to simulate and calculate the flow process line of the total outlet section of the basin and corrects it, effectively suppressing the accumulation of errors in the simulation process and improving the simulation accuracy of the entire process of rainstorm floods in plain river networks.

[0094] like Figure 3 As shown in the figure, this application embodiment also provides a structural schematic diagram of a plain network river area runoff simulation device. This device can implement the above-mentioned method and may include: Data correction module 21 is used to acquire the original DEM data of the plain river network area, and to correct the original DEM data to obtain corrected DEM data. The catchment area matrix calculation module 22 is used to generate a flow direction matrix for the entire region based on the corrected DEM data, and to calculate the catchment area matrix for the entire region based on the flow direction matrix. The river level matrix generation module 23 is used to generate a binary raster water system layer based on the catchment area matrix and a preset catchment area threshold, and to classify the river raster in the binary raster water system layer based on the flow direction matrix to generate a river level matrix. Sub-basin determination module 24 is used to determine the sub-basin corresponding to each hydrological station based on the flow direction matrix and the river class matrix; The confluence boundary delineation module 25 is used to delineate multiple confluence boundaries based on the corrected DEM data and the multi-source spatial data of the plain river network area. The catchment unit partitioning module 26 is used to optimize each of the sub-basins according to multiple catchment boundaries and preset boundary priorities, and to construct catchment unit partitions based on the optimized sub-basins; The simulation calculation module 27 is used to input the catchment unit partition, the corrected DEM data and the meteorological data of the plain river network area into the Topmodel model for simulation calculation to obtain the total outlet section flow process line of the basin. The flow process line correction module 28 is used to correct the flow process line of the total outlet section of the basin to obtain the corrected flow process line of the total outlet section of the basin.

[0095] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions 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.

[0096] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method for simulating runoff generation and confluence in plain network river areas. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0097] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment 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.

[0098] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to implement the plain river network runoff simulation method of this application embodiment. The 903 input / output interface is used to implement information input and output. The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0099] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for simulating runoff generation and confluence in plain network river areas.

[0100] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0101] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0102] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0103] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0104] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0107] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for simulating runoff generation and confluence in a plain network river area, characterized in that, include: Obtain the original DEM data of the plain river network area, and perform correction processing on the original DEM data to obtain corrected DEM data; Based on the corrected DEM data, a flow direction matrix for the entire region is generated, and based on the flow direction matrix, the catchment area matrix for the entire region is calculated. A binary raster water system layer is generated based on the water catchment area matrix and a preset water catchment area threshold. Based on the flow direction matrix, the river raster in the binary raster water system layer is classified to generate a river level matrix. The sub-basins corresponding to each hydrological station are determined based on the flow direction matrix and the river channel hierarchy matrix. Based on the corrected DEM data and the multi-source spatial data of the plain river network area, multiple confluence boundaries are delineated; Based on multiple confluence boundaries and preset boundary priorities, each sub-basin is optimized, and a catchment unit partition is constructed based on the optimized sub-basins. The catchment unit partitions, the corrected DEM data, and the meteorological data of the plain river network area are input into the Topmodel model for simulation calculation to obtain the total outlet section flow process line of the basin. The flow process curve at the total outlet section of the basin is corrected to obtain the corrected flow process curve at the total outlet section of the basin.

2. The method for simulating runoff generation and confluence in plain network river areas according to claim 1, characterized in that, The corrected DEM data includes multiple first grids; The step of generating a flow direction matrix for the entire region based on the corrected DEM data includes: Select one first grid cell from multiple first grid cells that has not been marked with the water outlet direction as the current center grid cell, and determine multiple neighboring grid cells corresponding to the current center grid cell; Determine the horizontal straight-line distance from the center point of the current central grid to the center points of each of the neighboring grids; Calculate the elevation difference between each of the neighboring grid cells and the current center grid cell; Based on the elevation difference and the corresponding horizontal straight-line distance, calculate the distance weight difference between each neighboring grid and the current center grid; The water outlet direction of the current central grid is determined based on the distance weight difference, and the current central grid with the determined water outlet direction is marked. Return to the step of selecting an unmarked first grid cell from multiple first grid cells as the current center grid cell, until all first grid cells are marked, collect the water outflow directions of all first grid cells, and generate a flow direction matrix for the entire region.

3. The method for simulating runoff generation and confluence in plain network river areas according to claim 2, characterized in that, The calculation of the catchment area matrix for the entire region based on the flow direction matrix includes: The water catchment area values ​​of all first grid cells in the corrected DEM data are initialized to 1; Starting from any of the first grid cells, trace the water outflow direction indicated by the flow direction matrix cell by cell to the boundary of the plain network river area, and increment the catchment area value of all the first grid cells passed along the tracing path by 1 each time. Return to the step of starting from any of the first grid cells and tracing grid by grid along the outflow direction indicated by the flow direction matrix to the boundary of the plain network river area, until all the first grid cells have been used as starting points to complete the tracing, and obtain the grid count of each of the first grid cells. Multiply the statistical count of each of the first grid cells by the actual area of ​​the first grid cell to generate the catchment area matrix of the entire region.

4. The method for simulating runoff generation and confluence in plain network river areas according to claim 1, characterized in that, The optimization of each sub-basin based on multiple confluence boundaries and a preset boundary priority includes: The multiple confluence boundaries are rasterized according to the raster size and spatial range of the modified DEM data to obtain raster layers corresponding to the multiple confluence boundaries, wherein the raster layer includes multiple second rasters; The raster layers corresponding to multiple confluence boundaries are spatially superimposed grid by grid, and the number of boundary types covered by each second raster is counted based on the superimposition result; The number of conflict grids is determined based on the number of boundary types; The target confluence boundary of the conflict grid is determined according to the preset boundary priority. The sub-basin is optimized based on the target confluence boundary of the conflict grid.

5. The method for simulating runoff generation and confluence in plain network river areas according to claim 4, characterized in that, The optimization of the sub-basin based on the target confluence boundary of the conflict grid includes: Based on the target confluence boundary of the conflict grid, each sub-basin is subjected to boundary cutting and local merging processing to obtain each sub-basin after cutting and merging. The drainage direction is corrected for each of the cut and merged sub-basins.

6. The method for simulating runoff generation and confluence in plain network river areas according to claim 1, characterized in that, The process of inputting the catchment unit zoning, the corrected DEM data, and the meteorological data of the plain river network area into the Topmodel model for simulation calculation to obtain the total outlet cross-sectional discharge process curve of the basin includes: The topographic index of the catchment unit zone is calculated based on the corrected DEM data, and the area distribution matrix of the topographic index is statistically analyzed. Based on the meteorological data and the area distribution matrix, the soil water deficit of each third grid cell in the catchment unit partition is determined time-by-time, wherein the meteorological data includes rainfall time series and evaporation time series; The yield of each of the third grid cells is determined based on the soil water shortage, and the yield of each of the third grid cells is accumulated to generate the outlet yield sequence of the catchment unit partition. The slope runoff calculation was performed on the outlet flow sequence using the isochronous flow method to obtain the outlet cross-sectional flow process line of the catchment unit partition. Based on the discharge process curve at the outlet section, the river confluence calculation is performed to obtain the discharge process curve at the total outlet section of the basin.

7. The method for simulating runoff generation and confluence in plain network river areas according to claim 1, characterized in that, The step of correcting the flow process curve at the total outlet section of the basin to obtain the corrected flow process curve at the total outlet section of the basin includes: Acquire historical measured flow, real-time rainfall data, and underlying surface data of the watershed within the plain network river area; The total outlet section flow process line of the basin, the real-time rainfall data and the underlying surface data are input into the preset first error prediction model, second error prediction model and third error prediction model to obtain the corresponding first error prediction result, second error prediction result and third error prediction result; Based on the first error prediction result, the second error prediction result, the third error prediction, and the historical measured traffic flow, a pre-established fusion model is trained to obtain a trained fusion model. The first error prediction result, the second error prediction result, and the third error prediction result are input into the trained fusion model to obtain the comprehensive error prediction value. The total outlet flow process curve of the basin is corrected based on the comprehensive error prediction value to obtain the corrected total outlet flow process curve of the basin.

8. A device for simulating runoff generation and confluence in a plain network of rivers, characterized in that, The device includes: The data correction module is used to acquire the original DEM data of the plain river network area, and to correct the original DEM data to obtain corrected DEM data. The catchment area matrix calculation module is used to generate a flow direction matrix for the entire region based on the corrected DEM data, and to calculate the catchment area matrix for the entire region based on the flow direction matrix. The river level matrix generation module is used to generate a binary raster river system layer based on the catchment area matrix and a preset catchment area threshold, and to classify the river raster in the binary raster river system layer based on the flow direction matrix to generate a river level matrix. The sub-basin determination module is used to determine the sub-basin corresponding to each hydrological station based on the flow direction matrix and the channel hierarchy matrix. The confluence boundary delineation module is used to delineate multiple confluence boundaries based on the corrected DEM data and the multi-source spatial data of the plain river network area. The catchment unit partitioning module is used to optimize each of the sub-basins according to multiple catchment boundaries and preset boundary priorities, and to construct catchment unit partitions based on the optimized sub-basins; The simulation calculation module is used to input the catchment unit partition, the corrected DEM data and the meteorological data of the plain river network area into the Topmodel model for simulation calculation to obtain the total outlet section flow process line of the basin. The flow process line correction module is used to correct the flow process line of the total outlet section of the basin, so as to obtain the corrected flow process line of the total outlet section of the basin.

9. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the plain network river area runoff simulation method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the plain network river area runoff simulation method as described in any one of claims 1 to 7.

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