Urban flood process simulation and sluice group dispatching method, device, equipment, medium and program product

Abstract

The application is suitable for the field of digital twin technology, and provides a city flood process simulation and sluice group scheduling method, device, equipment, medium and program product. The method comprises the following steps: superimposing building rigid body resistance flow attribute, drainage pipe network flow capacity parameter and sluice control section position to city three-dimensional terrain and calculating net rainfall intensity distribution data of each grid unit; taking the net rainfall intensity distribution data as a continuous equation source term, the building rigid body resistance flow attribute as a solid wall boundary condition, and the drainage pipe network flow capacity parameter as a negative source term constraint, calculating the global water depth distribution, flow velocity field and the inner river water level and the outer river water level of each sluice control section by a two-dimensional shallow water equation; and performing reverse recursive accumulation on each sluice opening degree combination to obtain an optimal sluice group scheduling scheme. The application realizes closed-loop coupling of flood evolution simulation and sluice group optimization scheduling, and improves the accuracy of multi-sluice joint opening timing.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a method, apparatus, equipment, medium, and program product for simulating urban flooding processes and scheduling sluice gate groups. Background Technology

[0002] Urban flooding is one of the major natural disasters threatening urban safety. Accurate simulation of urban flooding processes and the development of scientific scheduling plans are core requirements for flood prevention and disaster reduction. Existing urban flooding simulation technologies use simplified planar terrain or low-precision digital elevation models as the simulation base, failing to incorporate the actual outlines and heights of urban buildings as physical flow constraints into hydrodynamic calculations. This results in significant deviations between flood flow paths and actual conditions. Furthermore, the commonly used assumption of spatially uniform rainfall input ignores the spatially non-uniform distribution of urban rainfall, leading to distortions in runoff calculations for different regions.

[0003] However, existing simulation methods often simplify or even ignore drainage networks, failing to incorporate the actual flow capacity of each node in the drainage network as a negative source term into the surface water dynamic equation. This fails to accurately reflect the urban drainage system's ability to absorb surface water, further reducing the accuracy of flood simulations. Moreover, existing technologies rely on the experience and judgment of dispatchers, lacking a systematic method for quantitatively optimizing the sluice gate opening sequence based on flood evolution simulation predictions. This makes it impossible to automatically generate the optimal sluice gate opening sequence scheme with the goal of minimizing the water level difference between the inner and outer rivers in scenarios involving the joint dispatch of multiple sluice gates, resulting in low flood control dispatch efficiency and the risk of misjudgment. Summary of the Invention

[0004] This invention provides a method, device, equipment, medium, and program product for simulating urban flooding processes and scheduling sluice gate groups. This invention solves the problem of distorted flood flow paths caused by neglecting the flow obstruction properties of building entities in existing technologies.

[0005] In a first aspect, embodiments of this application provide a method for simulating urban flooding processes and scheduling sluice gate groups, including: The rigid body flow resistance properties of buildings, the flow capacity parameters of drainage pipe networks, and the location of sluice gate control sections are superimposed on the three-dimensional urban terrain to obtain a digital twin base plate of the three-dimensional urban terrain and calculate the net rainfall intensity distribution data of each grid cell. Using the net rainfall intensity distribution data as the source term of the continuous equation, the rigid body flow resistance property of the building as the solid wall boundary condition, and the flow capacity parameter of the drainage network as the negative source term constraint, the water depth distribution, velocity field, and the water level of the inner and outer rivers at each sluice gate control section are calculated by two-dimensional shallow water equations. Based on the water levels of the inner river and the outer river, the optimal cost is accumulated by reverse recursion for each combination of sluice gate openings within each scheduling time step, and then backtracked forward to obtain the optimal scheduling scheme for the sluice gate group.

[0006] Optionally, in a first implementation of the first aspect of the present invention, the step of superimposing the rigid body flow resistance properties of the building, the flow capacity parameters of the drainage network, and the location of the sluice gate control section onto the three-dimensional urban terrain to obtain a digital twin base plate of the three-dimensional urban terrain and calculating the net rainfall intensity distribution data of each grid cell includes: Using satellite DEM elevation data as the elevation benchmark, a three-dimensional structured raster computational grid for the city is constructed. The raster cells that overlap with the building outlines in the raster computational grid are given impermeable rigid body properties and the bottom elevation is set as the top elevation of the building to obtain a three-dimensional urban terrain grid. Based on the urban 3D terrain grid, the maximum allowable drainage flow of each node of the drainage network is mapped to the corresponding grid cell, and the drainage intensity per unit area is converted according to the area of ​​the corresponding grid cell. Combining the current grid cell water depth, calculation time step, rainwater inlet inflow capacity and remaining flow capacity of the network, the drainage intensity per unit area is limited to form negative source term constraint parameters. The monitoring section positions of the inner river side and the outer river side of each sluice gate are marked to the corresponding sluice gate control section grid positions to obtain the urban 3D terrain digital twin base plate. The net rainfall intensity distribution data of each grid cell is calculated by combining the vegetation coverage rate and impermeable area ratio of each grid cell in the digital twin base plate of the urban three-dimensional terrain.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the step of calculating the net rainfall intensity distribution data of each grid cell by combining the vegetation coverage rate and impervious area ratio of each grid cell in the urban three-dimensional terrain digital twin base plate includes: Using the geographic coordinates of each rainfall monitoring station as the generator, Thiessen polygons are constructed within the coverage area of ​​the urban three-dimensional terrain digital twin base plate. Each grid cell in the urban three-dimensional terrain digital twin base plate is assigned to the Thiessen polygon corresponding to the nearest rainfall monitoring station to obtain non-uniform rainfall distribution data. Based on non-uniform rainfall process data, the rainfall amount of each grid cell is converted into the rainfall intensity within the corresponding calculation time step according to the rainfall observation time interval. The vegetation interception intensity is determined by combining the vegetation coverage rate, and the infiltration deduction intensity is determined by combining the permeable area ratio, soil infiltration capacity and underlying surface type. The impermeable area ratio is used as the surface runoff enhancement constraint parameter to obtain the net rainfall intensity distribution data of each grid cell.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the step of using the net rainfall intensity distribution data as a source term of a continuous equation, the rigid flow resistance properties of the building as a solid wall boundary condition, and the flow capacity parameters of the drainage network as a negative source term constraint, and calculating the overall water depth distribution, velocity field, and the water levels of the inner and outer rivers at each sluice gate control section through a two-dimensional shallow water equation, includes: The net rainfall intensity distribution data is used as the source term of the continuous equation, the solid wall reflection condition of the grid cell corresponding to the rigid body flow resistance property of the building is used as the water flow boundary, and the maximum allowable drainage flow of each node of the drainage network is used as the negative source term of the corresponding grid cell. The global water depth distribution and velocity field are calculated by two-dimensional shallow water equation. Based on the overall water depth distribution, the bottom elevation of the grid cells on the inner river side and the outer river side of each sluice gate is added to the water depth value of the corresponding grid cell in the water depth distribution to obtain the inner river water level and outer river water level of each sluice gate control section.

[0009] Optionally, in the fourth implementation of the first aspect of the present invention, the step of performing reverse recursion to accumulate the optimal cost for each sluice gate opening combination within each scheduling time step based on the inland river water level and the outer river water level, and then forward backtracking to obtain the optimal scheduling scheme for the sluice gate group, includes: Using the inland river water level and the outer river water level as the initial state, the process is progressively reversed from the last scheduling time step to the current scheduling time step. Within each scheduling time step, all feasible opening combinations of each sluice gate are enumerated. Each sluice gate opening combination is used as the dynamic boundary condition of the sluice gate and substituted into the two-dimensional shallow water equation to predict the inland river water level and the outer river water level of the next scheduling time step. The sum of the differences between the inland and outer river water levels at each sluice gate control section is used as the single-step cost to accumulate to the optimal cost function, thereby obtaining the cumulative optimal cost and optimal opening record of each sluice gate opening combination within each scheduling time step. Based on the cumulative optimal cost and the optimal opening record, the optimal opening of each scheduling time step is traced back in a forward direction from the current scheduling time step to the last scheduling time step to obtain the optimal scheduling scheme for the sluice gate group.

[0010] Optionally, in the fifth implementation of the first aspect of the present invention, the opening degree of each sluice gate in the current scheduling time step of the optimal scheduling scheme of the sluice gate group is used as the dynamic boundary condition of the sluice gate to update the two-dimensional shallow water equation, thereby driving the flood evolution simulation of the next scheduling cycle.

[0011] Secondly, embodiments of this application provide an urban flooding process simulation and sluice gate group scheduling device, comprising: The modeling module is used to overlay the rigid body flow resistance properties of buildings, the flow capacity parameters of drainage pipe networks, and the location of sluice gate control sections onto the three-dimensional urban terrain to obtain a digital twin base plate of the three-dimensional urban terrain and calculate the net rainfall intensity distribution data of each grid cell. The calculation module is used to use the net rainfall intensity distribution data as the source term of the continuous equation, the rigid body flow resistance property of the building as the solid wall boundary condition, and the flow capacity parameter of the drainage network as the negative source term constraint, and calculate the water depth distribution, velocity field and water level of the inner and outer rivers at each sluice gate control section through the two-dimensional shallow water equation. The scheduling module is used to perform reverse recursion to accumulate the optimal cost for each combination of sluice gate openings within each scheduling time step based on the water level of the inner river and the water level of the outer river, and then backtrack forward to obtain the optimal scheduling scheme for the sluice gate group.

[0012] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned urban flooding process simulation and sluice gate group scheduling method.

[0013] Fourthly, embodiments of this application provide a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described urban flooding process simulation and sluice gate group scheduling method.

[0014] Fifthly, embodiments of this application provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the above-described urban flooding process simulation and sluice gate group scheduling method.

[0015] In one of the solutions provided by the aforementioned urban flood process simulation and sluice gate group scheduling methods, devices, equipment, media, and program products, a three-dimensional digital twin base plate of the urban terrain is constructed by combining satellite DEM elevation data with the rigid flow resistance properties of buildings, the flow capacity parameters of drainage pipe networks, and the location of sluice gate control sections. This allows buildings in the simulation calculation domain to participate in the hydrodynamic solution as solid boundary conditions, realistically reproducing the obstruction and bypass effects of buildings on flood flow paths, and solving the problem of flood flow path distortion caused by neglecting the solid flow resistance properties of buildings in existing technologies. By constructing Thiessen polygons using the geographical coordinates of each rainfall monitoring station as generators, discrete point rainfall observations are expanded into a spatially non-uniform rainfall distribution covering the entire area. After deducting interception and infiltration by combining the vegetation coverage and impermeable area ratio of each grid cell, the net rainfall intensity distribution is obtained, solving the problem of flow generation calculation distortion caused by the spatially uniform rainfall assumption in existing technologies, and improving the spatial distribution accuracy of rainfall input. By introducing the maximum allowable drainage flow of each node in the drainage network into the two-dimensional shallow water equation as a negative source term, the coupled calculation of the drainage network's absorption capacity and the surface flood evolution process is realized, making the simulation results more realistically reflect the urban drainage system's ability to absorb accumulated water. At the sluice gate scheduling level, this invention takes minimizing the water level difference between the inner and outer rivers at each sluice gate control section as the optimization objective. It uses a dynamic programming method to backward recursively calculate the optimal cost for each sluice gate opening combination within each scheduling time step and then backtracks forward to automatically generate the optimal scheduling scheme for the sluice gate group. This scheme is then used as the dynamic boundary condition for the sluice gates to update the two-dimensional shallow water equation in real time to drive the simulation of the next scheduling cycle, realizing the closed-loop coupling between flood evolution simulation and optimal scheduling of the sluice gate group. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the urban flooding process simulation and sluice gate group scheduling system in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for simulating urban flooding processes and scheduling sluice gate groups in one embodiment of the present invention; Figure 3 yes Figure 2 A schematic diagram of the implementation process of step S10; Figure 4 yes Figure 3 A schematic diagram of the implementation process of step S13; Figure 5 yes Figure 2A schematic diagram of the implementation process of step S20; Figure 6 yes Figure 2 A schematic diagram of the implementation process of step S30; Figure 7 This is a schematic diagram of a device for simulating urban flooding processes and scheduling sluice gates in one embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that, as used in this specification and the appended claims, the term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0020] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0022] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0023] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0024] To address the problems mentioned above in the background art, this application proposes a method, apparatus, equipment, medium, and program product for urban flood process simulation and sluice gate group scheduling. The urban flood process simulation and sluice gate group scheduling method provided by this invention can be applied to, for example... Figure 1 The urban flooding process simulation and sluice gate group scheduling system shown includes a client and a server.

[0025] In one embodiment, such as Figure 2 As shown, a method for simulating urban flooding processes and scheduling sluice gate groups is provided, which is then applied to... Figure 1 Taking the urban flood process simulation and sluice gate group scheduling system as an example, the following steps are included: S10: Overlay the rigid body flow resistance properties of the building, the flow capacity parameters of the drainage network and the location of the sluice gate control section onto the three-dimensional urban terrain to obtain a digital twin base plate of the three-dimensional urban terrain and calculate the net rainfall intensity distribution data of each grid cell. S20: Using net rainfall intensity distribution data as the source term of the continuous equation, the rigid body flow resistance property of the building as the solid wall boundary condition, and the flow capacity parameter of the drainage network as the negative source term constraint, the water depth distribution, velocity field, and the water level of the inner and outer rivers at each sluice gate control section are calculated through two-dimensional shallow water equations. S30: Based on the water levels of the inner and outer rivers, the optimal cost is accumulated by reverse recursion for each combination of sluice gate openings within each scheduling time step, and then backtracked forward to obtain the optimal scheduling scheme for the sluice gate group.

[0026] In this embodiment, the three-dimensional urban terrain is used as the supporting foundation. The grid cells that overlap with the building outline are registered as rigid flow-blocking units of the building. The elevation of the top surface of the building is written into the bottom elevation field of the corresponding grid cell, so that the building is represented as an insurmountable boundary in the hydrodynamic solution. At the same time, the maximum allowable drainage flow, rainwater inlet inflow capacity, and remaining flow capacity of the drainage network nodes are mapped to the corresponding grid cells. The monitoring section positions on the inner river side and the outer river side of the sluice gate are marked on the grid position of the sluice gate control section, forming a digital twin base plate of the three-dimensional urban terrain that can simultaneously describe the terrain undulation, building flow obstruction, drainage absorption, and sluice gate boundary. The rainfall observed by each rainfall monitoring station is expanded to a spatially non-uniform rainfall distribution. Combined with vegetation coverage, impermeable area ratio, permeable area ratio, soil infiltration capacity, and underlying surface type, the net rainfall intensity distribution data of each grid cell in the actual surface runoff process within the current calculation time step is calculated.

[0027] Net rainfall intensity distribution data is used as the source of water volume increase, drainage network flow capacity parameters are used as the source of water volume deduction, and the rigid body flow resistance properties of the structure are converted into solid wall reflection boundary conditions, so that water will propagate around the boundary of the structure. During the numerical solution of the two-dimensional shallow water equation, the global water depth distribution and velocity field are updated simultaneously. In each calculation time step, the calculation module reads the water depth, velocity, bottom elevation, drainage negative source term and sluice gate boundary state of the previous moment, completes the numerical advancement of the water volume continuity relationship and momentum propagation relationship, and obtains a new water depth matrix and velocity component matrix. The water level value after superimposing the bottom elevation and water depth is read at the monitoring section grid cell on the inner river side and the monitoring section grid cell on the outer river side of the sluice gate, which is used as the inner river water level and outer river water level of each sluice gate.

[0028] After receiving the water levels of the inner and outer rivers at each sluice gate control section, the sluice gate group scheduling problem is transformed into a sequential decision-making problem involving multiple time steps and multiple sluice gate opening combinations. Within the scheduling time window, feasible opening combinations that meet the requirements of structural safety, water level safety, and backflow prevention are enumerated. Each sluice gate opening is used as a dynamic boundary condition for the sluice gates and fed into the two-dimensional shallow water equation to predict the water level change in the next scheduling time step. The scheduling module accumulates the costs corresponding to each opening combination in reverse from the end of the scheduling time window to the current time, and uses the difference between the inner and outer river water levels at each sluice gate control section as the basis for judging the scheduling merits. The optimal opening selection corresponding to each scheduling time step is recorded. Then, forward backtracking is performed from the current time to the end of the scheduling time window to obtain the optimal scheduling scheme for the sluice gate group.

[0029] In one embodiment, such as Figure 3 As shown, step S10 specifically includes the following steps: S11: Using satellite DEM elevation data as the elevation benchmark, a three-dimensional structured raster computational grid for the city is constructed. Raster cells that overlap with the building outline in the raster computational grid are given impermeable rigid body properties and their bottom elevation is set to the building top elevation to obtain a three-dimensional urban terrain grid. S12: Based on the urban 3D terrain grid, the maximum allowable drainage flow of each node of the drainage network is mapped to the corresponding grid cell, and the drainage intensity per unit area is converted according to the area of ​​the corresponding grid cell. Combining the current grid cell water depth, calculation time step, rainwater inlet inflow capacity and remaining flow capacity of the network, the drainage intensity per unit area is limited to form negative source term constraint parameters. The monitoring section positions of the inner river side and the outer river side of each sluice gate are marked to the corresponding sluice gate control section grid positions to obtain the urban 3D terrain digital twin base plate. S13: Calculate the net rainfall intensity distribution data of each grid cell by combining the vegetation coverage rate and impermeable area ratio of each grid cell in the urban three-dimensional terrain digital twin base plate.

[0030] In this embodiment, after receiving satellite DEM elevation data covering the target city area, the satellite DEM elevation data is converted to a unified urban geographic coordinate system, and a three-dimensional structured raster computational grid for the city is established according to the spatial resolution corresponding to the satellite DEM elevation data, so that each raster cell has a planar coordinate index and a bottom elevation field. The planar outline coordinates, bottom elevation, and top elevation of buildings are spatially registered, and the building outlines are projected into the three-dimensional structured raster computational grid for the city. For raster cells that overlap with the building outlines, impermeable rigid body attribute tags are written, and the bottom elevation field of the corresponding raster cell is updated to the top elevation of the building. This allows the building area to participate in boundary processing as an impassable solid flow-blocking area in the hydrodynamic calculation, thereby obtaining a three-dimensional urban terrain grid that includes real terrain undulations and building flow-blocking boundaries.

[0031] After the urban 3D terrain mesh is formed, data on drainage network nodes, connecting pipe sections, storm drain locations, and sluice gate control sections are loaded. The corresponding grid cells are located based on the planar coordinates of the drainage nodes. The maximum allowable drainage flow rate for each node in the drainage network is written into the corresponding grid cell, and the drainage intensity per unit area is calculated by combining the grid cell area. When forming negative source constraint parameters, the water depth, calculation time step, storm drain inflow capacity, and remaining flow capacity of the network for the current grid cell are read. A limiting process is applied to the drainage intensity per unit area, ensuring that the drainage deduction is constrained by the actual water volume, inlet inflow capacity, and remaining network capacity, preventing the negative source constraint parameters from exceeding the drainage range of the current grid cell. Simultaneously, the monitoring section locations on the inner river side and outer river side of each sluice gate are mapped to the sluice gate control section grid locations. A binding relationship is established in the grid attribute table between the sluice gate number, section direction, inner river side grid index, and outer river side grid index, thus forming a digital twin base plate of the urban 3D terrain that can be directly called by the flood simulation module.

[0032] Based on the vegetation cover, impervious area ratio, underlying surface type, and rainfall input information already recorded in each grid cell of the urban three-dimensional terrain digital twin base plate, net rainfall intensity distribution data corresponding to each grid cell is generated. Among them, vegetation cover is mainly used to characterize rainfall interception deduction, impervious area ratio is mainly used to characterize surface runoff enhancement constraint, and permeable areas also need to be combined with soil infiltration capacity to determine infiltration deduction intensity. Finally, net rainfall intensity distribution data that can be used as the source term input of the two-dimensional shallow water equation continuous equation is obtained.

[0033] In one embodiment, such as Figure 4 As shown, step S13 specifically includes the following steps: S131: Using the geographic coordinates of each rainfall monitoring station as the generator, construct Thiessen polygons within the coverage area of ​​the urban 3D terrain digital twin base plate, and assign each grid cell in the urban 3D terrain digital twin base plate to the Thiessen polygon corresponding to the nearest rainfall monitoring station to obtain non-uniform rainfall distribution data. S132: Based on non-uniform rainfall distribution data, the net rainfall intensity distribution data of each grid cell in the urban three-dimensional terrain digital twin base plate is obtained by subtracting the interception amount corresponding to the vegetation coverage rate and the infiltration amount corresponding to the ratio of impermeable area from the rainfall of each grid cell.

[0034] In this embodiment, at the current calculation time t, data streams from N rainfall monitoring stations covering the target city area are accessed, where N represents the number of rainfall monitoring stations participating in the current calculation, and t represents the current calculation time. The rainfall observation value of the i-th rainfall monitoring station is read. and geographic coordinates Where i represents the serial number of the rainfall monitoring station, This represents the observed rainfall value at the current calculation time for the i-th rainfall monitoring station. This represents the planar coordinates of the i-th rainfall monitoring station in a unified urban geographic coordinate system. Using the geographic coordinates of each rainfall monitoring station as Thiessen polygon generators, non-overlapping rainfall impact zones are constructed within the coverage boundary of the urban 3D terrain digital twin base. The center point coordinates of each raster cell are compared with the coordinates of all rainfall monitoring stations, and the raster cell is assigned to the zone corresponding to the rainfall monitoring station with the smallest distance. After completing the zone assignment, according to... Assign values ​​to grid cells, where Indicates coordinates as The rainfall obtained by the grid cells at the current calculation time. The coordinates of the center point of the grid cell are represented, thereby expanding the point-like rainfall monitoring data into non-uniform rainfall distribution data covering the entire area. The rainfall observation time interval can be set to 5 minutes. Considering that automatic rain gauges can provide minute-level or short-term cumulative rainfall, the 5-minute interval can balance the real-time nature of rainfall spatial interpolation and the stability of monitoring data. Before forming the source term of the continuous equation of the two-dimensional shallow water equation, the cumulative rainfall over the 5-minute period is normalized by 300 seconds and the unit conversion from millimeters to meters is completed to obtain the rainfall intensity in meters per second. The runoff input time step of the two-dimensional shallow water equation can be set to 60 seconds. Considering that the evolution of urban flooding is sensitive to changes in short-term heavy rainfall, the 60-second time step is used to read the normalized rainfall intensity and participate in the net rainfall intensity update within the current calculation time step, avoiding repeatedly writing the cumulative rainfall over the 5-minute period into every 60-second calculation time step.

[0035] For each grid cell in the urban 3D terrain digital twin base plate, vegetation cover, impervious area ratio, underlying surface type, and soil infiltration capacity are read. Runoff correction is applied to non-uniform rainfall distribution data. The interception effect corresponding to vegetation cover is used to deduct a portion of rainfall falling onto the vegetation canopy or green space surface. The impervious area ratio characterizes the restriction of infiltration processes by hardened paving, roads, roofs, etc., while the permeable area is determined by combining soil infiltration capacity to determine the actual infiltration deduction intensity. While retaining the original calculation relationships, the following can be adopted: Calculate the net rainfall intensity of the grid cells, where Indicates coordinates as The net rainfall intensity of the grid cells entering the surface runoff process at the current calculation time. Indicates coordinates as The vegetation coverage of the grid cells Indicates coordinates as The impermeable area ratio of the grid cells. For grid cells corresponding to the rigid flow-blocking properties of buildings, based on the slope aspect relationship of the grid cells at the building boundary, the runoff formed on the roof is channeled into adjacent downhill non-building grid cells, maintaining the flow-blocking properties of the building area and allowing rainfall runoff to enter surrounding roads, squares, or low-lying areas for confluence. The net rainfall intensity of all grid cells after time normalization, unit conversion, vegetation interception deduction, and infiltration deduction is written into a two-dimensional net rainfall intensity distribution matrix according to the grid coordinate index, and the matrix content is updated at each calculation time step, serving as the time-varying input of the source term of the continuous equation of the two-dimensional shallow water equation.

[0036] In one embodiment, such as Figure 5 As shown, step S20 specifically includes the following steps: S21: Using net rainfall intensity distribution data as the source term of the continuous equation, the solid wall reflection condition of the grid cell corresponding to the rigid body flow resistance property of the building as the water body flow boundary, and the maximum allowable drainage flow of each node of the drainage network as the negative source term of the corresponding grid cell, the global water depth distribution and velocity field are calculated through two-dimensional shallow water equations. S22: Based on the overall water depth distribution, the bottom elevation of the grid cells of the monitoring section on the inner river side and the grid cells of the monitoring section on the outer river side of each sluice gate is added to the water depth value of the corresponding grid cell in the water depth distribution to obtain the water level of the inner river and the water level of the outer river at each sluice gate control section.

[0037] In this embodiment, after receiving the digital twin base plate of the city's three-dimensional terrain and the net rainfall intensity distribution data, the calculation module uses the structured raster computational grid as the solution domain for the two-dimensional shallow water equation. At each calculation time step, it reads the bottom elevation of the raster cells, the rigid body flow obstruction markers of buildings, the negative source term constraint parameters of drainage nodes, and the current water depth and velocity fields. The calculation time step can be set to 60 seconds, a value chosen primarily considering the minute-level evolution characteristics of urban flooding processes. 60 seconds provides a relatively stable trade-off between the city-level computational scale, the frequency of rainfall input updates, and the response to water level changes. During the calculation process, the net rainfall intensity distribution data is... The maximum allowable drainage flow at each node of the drainage network, after being limited, is written as a negative source term into the same grid cell. The grid cells corresponding to the rigid flow-impedance properties of buildings are removed from the flowable computational domain or registered as solid-wall boundary cells. This causes the water flux to be constrained by reflection in the direction normal to the building boundary, allowing the water to continue propagating along roads, low-lying areas, and the outer edges of buildings. For the main hydrodynamic control relationship, a two-dimensional shallow water equation set can be used for further analysis. ; ; ; in, Indicates coordinates as The grid cells at time Water depth, in units of ; Indicates water flow at The velocity component in the direction, in units of ; Indicates water flow at The velocity component in the direction, in units of ; Indicates the drainage node grid cell at time... Drainage flow rate, in units of ; Represents the area of ​​a single grid cell, in units of ; Represents gravitational acceleration, with units of . ; Indicates coordinates as The bottom elevation of the grid cell, in units of ; and They represent direction and The dimension of the bed friction term is consistent with the right-hand side of the momentum equation. During actual operation, the calculation module calculates the water surface elevation difference and momentum flux between adjacent grid cells for each non-building grid cell, and updates the water depth distribution and velocity field for the next calculation time step based on the terrain slope, current water depth, velocity components, and bed friction term. When a grid cell has a drainage node, the drainage flow rate is taken as the smaller value jointly limited by the drainage network's allowable drainage capacity, the storm drain inlet's inflow capacity, the network's remaining flow capacity, and the current drainable volume within the grid cell, avoiding negative water depths after deduction from the continuous equation. Rigid building flow-blocking grid cells maintain their non-water-storage and non-penetrating properties in numerical processing. The normal flow rate at the building boundary is set to zero or treated as a solid-wall reflection condition, while tangential propagation continues to participate in the shallow water equation solution along the adjacent passable grid cells, ensuring that the flood flow path matches the building's planar profile.

[0038] After outputting the overall water depth distribution, perform cross-section reading and locate the first cross-section according to the sluice gate control cross-section index table. The monitoring grid units on the inner river side and the outer river side of the sluice gate are used to form the cross-sectional water level by adding the bottom elevation of the corresponding grid unit to the water depth value, which can be expressed as: , ,in, Indicates the first The sluice gate at all times The water level of Neijiang, in units of ; Indicates the first The sluice gate at all times The water level of the outer river, in units of ; Indicates the first The coordinates of the center grid corresponding to the monitoring section on the inner river side of the sluice gate; Indicates the first The coordinates of the grid center of the monitoring section on the outer river side of the sluice gate are displayed. After the water level of the section is read, the water depth matrix, flow velocity component matrix, inundation range and water levels of the inner and outer rivers of each sluice gate control section are written into the simulation result cache. The water depth distribution is overlaid as a color level layer onto the digital twin base plate of the urban three-dimensional terrain, and the flow velocity direction is displayed as a vector arrow.

[0039] In one embodiment, such as Figure 6 As shown, step S30 specifically includes the following steps: S31: Taking the water levels of the inner and outer rivers as the initial state, the process is recursively reversed from the last scheduling time step to the current scheduling time step. In each scheduling time step, all feasible opening combinations of each sluice gate are enumerated. The opening combinations of each sluice gate are used as dynamic boundary conditions of the sluice gates and substituted into the two-dimensional shallow water equation to predict the water levels of the inner and outer rivers in the next scheduling time step. The sum of the water level differences between the inner and outer rivers at the control sections of each sluice gate is used as the single-step cost to accumulate to the optimal cost function, so as to obtain the cumulative optimal cost and optimal opening record of each sluice gate opening combination in each scheduling time step. S32: Based on the cumulative optimal cost and optimal opening record, backtrack from the current scheduling time step to the last scheduling time step to obtain the optimal opening of each scheduling time step, and obtain the optimal scheduling scheme of the sluice gate group.

[0040] In this embodiment, after completing the current calculation cycle of the two-dimensional shallow water equation, the water levels of the inner and outer rivers at the control sections of each sluice gate are read, and the sluice gate group scheduling time window is set to... Set the scheduling time steps to The scheduling step size satisfies ,in This indicates the total duration of a single rolling optimization coverage, expressed in minutes or hours. This represents the number of off-stage steps within the scheduling time window; This represents the time interval between two adjacent scheduling time steps. For example, It can be set to 120 minutes. It can be set to 8. The corresponding time window is 15 minutes. This value ensures that the scheduling results cover the main variation range of water level propagation under short-term heavy rainfall, while avoiding excessively long scheduling time windows that would lead to excessive computational burden for multiple predictions of the shallow water equation. At each scheduling time step... Constructing state vectors and decision vector ,in Indicates the scheduling time step number. , , Indicates the number of sluice gates participating in the joint dispatch. Indicates the first The sluice gate is in the The water level of the Neijiang River at each scheduling time step, in meters (m). Indicates the first The sluice gate is in the The opening degree of each scheduling time step, the value set adopts Where 0 represents fully closed and 1 represents fully open, the 0.25 interval facilitates the decomposition of the gate's mechanical actions into a finite number of stable control positions, reducing the complexity of the combined search caused by continuous opening optimization. The scheduling time step is 0-step, enumerating all gate opening combinations that satisfy the constraints in each state, and incorporating each opening combination as a dynamic boundary condition into the two-dimensional shallow water equation to predict the inland and outer river water levels in the next scheduling time step. Constraints may include that the opening variation of the same gate in adjacent scheduling time steps does not exceed... The water level of the inner river should not exceed the safe warning level, and the water level of the outer river should not be lower than the lower limit for preventing backflow. This indicates the maximum allowable change in gate opening per step. For example, it can be set to 0.25, ensuring that the gate opening changes by a maximum of one increment per scheduling cycle, reducing sudden changes in water level and impact on the gate mechanism. The cost per step can be calculated according to... Calculation, where Indicates the first Each scheduling time step is in the state and opening combination The single-step fee; Indicates the first The weighting coefficient of each sluice gate; Indicates the first The sluice gate is in the The water level of the outer river at each scheduling time step is expressed in meters (m). Weighting coefficients are determined comprehensively based on the river channel grade where the sluice gate is located, the importance of the protected object, historical flood sensitivity, and scheduling priority. The weighting coefficients for sluice gates controlling the main river channel are higher than those for ordinary tributary sluice gates, while the weighting coefficients for low-risk auxiliary drainage sluice gates are lower than those for ordinary tributary sluice gates. Specific values ​​can be adjusted based on historical flood replay results and scheduling management requirements, and should remain fixed within the same scheduling task. The cost of a single step is added to the cumulative optimal cost for the next scheduling time step, and then calculated according to… Update the optimal cost function, where Indicates from the first The cumulative minimum cost from one scheduling time step to the end of the scheduling time window. Indicates the first The set of feasible opening combinations that satisfy the constraints of opening change, water level safety, and backflow prevention within a scheduling time step. Representing the two-dimensional shallow water equation in aperture combination The state of the next scheduling time step predicted after the action is set to the final state boundary. After each minimum cost comparison is completed, the optimal opening combination for the corresponding state is recorded synchronously, forming an index record of "state - optimal opening - cumulative cost".

[0041] After the reverse recursion is completed, the optimal opening record is read from the actual water level state of the current scheduling time step to determine the sluice gate opening combination to be issued at the current moment. Then, the predicted next state is used as an index to continue reading the optimal opening for the next scheduling time step until the end of the scheduling time window, thus obtaining the optimal scheduling scheme for the sluice gate group. The optimal scheduling scheme for the sluice gate group includes the opening value of each sluice gate at each scheduling time step, the opening time when it changes from closed to open, the closing time when it changes from open to closed, and the corresponding predicted results of the water level difference between the inner and outer rivers. After the forward backtracking is completed, only the optimal opening combination corresponding to the current scheduling time step is issued to the dynamic boundary conditions of the sluice gates in the two-dimensional shallow water equation. The opening of subsequent scheduling time steps is temporarily stored as a rolling prediction scheme. After the new water depth distribution, velocity field, inner river water level, and outer river water level are generated, the reverse recursion and forward backtracking are executed again, so that the sluice gate group scheduling scheme is continuously updated with the flood evolution results.

[0042] In one embodiment, the opening degree of each sluice gate in the current scheduling time step of the optimal scheduling scheme of the sluice gate group is used as the dynamic boundary condition of the sluice gate to update the two-dimensional shallow water equation, driving the flood evolution simulation of the next scheduling cycle.

[0043] In this embodiment, after the scheduling module completes the forward backtracking, it extracts the optimal opening combination corresponding to the current scheduling time step and writes the opening value of each sluice gate into the sluice gate control section attribute table, so that the sluice gate number, control section grid column, inner river side monitoring section grid, outer river side monitoring section grid, and current opening value form a synchronous record. After receiving the sluice gate opening command, the calculation module pauses the use of the sluice gate boundary parameters retained in the previous scheduling cycle, and updates the equivalent weir crest elevation, flow state, and upstream and downstream source-sink coupling relationship of the corresponding sluice gate control section according to the current opening value; when the opening of a sluice gate is zero, the sluice gate control section is treated as a closed boundary, and no water exchange occurs on both sides of the control section; when the opening of a sluice gate is greater than zero, the sluice gate control section calculates the exchange flow between the inner and outer rivers according to the weir flow flow relationship, and the water volume is deducted from the high water level side and increased on the low water level side, so that the opening change can be directly reflected in the continuous equation source-sink term of the two-dimensional shallow water equation. The flow rate can be expressed by the weir flow calculation relationship as follows: ,in Indicates the first The flow rate of the sluice gate at the current calculation time. Indicates the first The flow coefficient of the sluice gate, Indicates the first The opening degree of the sluice gate at the current calculation time. Indicates the first The effective width of the sluice gate Indicates the first The water level on the upstream side of the sluice gate that is used in the flow calculation. Indicates the first The current equivalent weir crest elevation of the sluice gates is determined. When the water level difference is insufficient to form effective flow, the flow rate is set to zero to avoid abnormal exchanges in the two-dimensional shallow water equations that are opposite to the water level direction. After updating the dynamic boundary conditions of the sluice gates, the two-dimensional shallow water equations continue to advance the flood evolution simulation for the next scheduling cycle using the latest water depth distribution, velocity field, net rainfall intensity distribution, drainage negative source term, and sluice gate flow source and sink term as initial inputs. The updated global water depth distribution, velocity field, inundation range, and the water levels of the inner and outer rivers at each sluice gate control section are calculated. The scheduling module refreshes the current state vector using the new inner and outer river water levels and re-executes the opening combination enumeration, reverse recursion, and forward backtracking to continuously and dynamically correct the sluice gate group scheduling scheme according to changes in rainfall input, drainage capacity, surface runoff, and water level difference.

[0044] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0045] In one embodiment, a device for simulating urban flooding processes and scheduling sluice gate groups is provided, which corresponds one-to-one with the urban flooding process simulation and sluice gate group scheduling method described in the above embodiments. For example... Figure 7 As shown, the urban flooding process simulation and sluice gate group scheduling device includes: Modeling module 701 is used to overlay the rigid body flow resistance properties of buildings, the flow capacity parameters of drainage pipe networks and the location of sluice gate control sections onto the three-dimensional urban terrain to obtain a digital twin base plate of the three-dimensional urban terrain and calculate the net rainfall intensity distribution data of each grid cell. The calculation module 702 is used to use net rainfall intensity distribution data as the source term of the continuous equation, the rigid body flow resistance property of the building as the solid wall boundary condition, and the flow capacity parameter of the drainage network as the negative source term constraint, and calculate the water depth distribution, velocity field and water level of the inner and outer rivers at each sluice gate control section through two-dimensional shallow water equations. The scheduling module 703 is used to perform reverse recursion to accumulate the optimal cost for each combination of sluice gate openings within each scheduling time step based on the water levels of the inner and outer rivers, and then backtrack forward to obtain the optimal scheduling scheme for the sluice gate group.

[0046] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0047] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0048] This application also provides a computer device, such as... Figure 8 As shown, the computer device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, it implements the steps in any of the above method embodiments, or when the processor executes the computer program, it implements the functions of each module / unit in the above device embodiments.

[0049] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0050] Those skilled in the art will understand that Figure 8 The computer device described is merely an example and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0051] The aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), or Field Programmable Gate Arrays (FPGAs). Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0052] The memory can be an internal storage unit of the computer device, such as a hard drive or RAM. The memory can also be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory can include both internal and external storage units of the computer device.

[0053] This application also provides a readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the various method embodiments described above.

[0054] This application provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0055] 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, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0056] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0057] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0058] In the embodiments provided in this application, it should be understood that the disclosed apparatus / devices and methods can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units 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 devices or units may be electrical, mechanical, or other forms.

[0059] The units described 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.

[0060] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for simulating urban flooding processes and scheduling sluice gate groups, characterized in that, include: The rigid body flow resistance properties of buildings, the flow capacity parameters of drainage pipe networks, and the location of sluice gate control sections are superimposed on the three-dimensional urban terrain to obtain a digital twin base plate of the three-dimensional urban terrain and calculate the net rainfall intensity distribution data of each grid cell. Using the net rainfall intensity distribution data as the source term of the continuous equation, the rigid body flow resistance property of the building as the solid wall boundary condition, and the flow capacity parameter of the drainage network as the negative source term constraint, the water depth distribution, velocity field, and the water level of the inner and outer rivers at each sluice gate control section are calculated by two-dimensional shallow water equations. Based on the water levels of the inner river and the outer river, the optimal cost is accumulated by reverse recursion for each combination of sluice gate openings within each scheduling time step, and then backtracked forward to obtain the optimal scheduling scheme for the sluice gate group.

2. The urban flooding process simulation and sluice gate group scheduling method as described in claim 1, characterized in that, The process involves overlaying the rigid flow resistance properties of buildings, the flow capacity parameters of drainage pipe networks, and the location of sluice gate control sections onto the urban three-dimensional terrain to obtain a digital twin base plate of the urban three-dimensional terrain and calculating the net rainfall intensity distribution data of each grid cell, including: Using satellite DEM elevation data as the elevation benchmark, a three-dimensional structured raster computational grid for the city is constructed. The raster cells that overlap with the building outlines in the raster computational grid are given impermeable rigid body properties and the bottom elevation is set as the top elevation of the building to obtain a three-dimensional urban terrain grid. Based on the urban 3D terrain grid, the maximum allowable drainage flow of each node of the drainage network is mapped to the corresponding grid cell, and the drainage intensity per unit area is converted according to the area of ​​the corresponding grid cell. Combining the current grid cell water depth, calculation time step, rainwater inlet inflow capacity and remaining flow capacity of the network, the drainage intensity per unit area is limited to form negative source term constraint parameters. The monitoring section positions of the inner river side and the outer river side of each sluice gate are marked to the corresponding sluice gate control section grid positions to obtain the urban 3D terrain digital twin base plate. The net rainfall intensity distribution data of each grid cell is calculated by combining the vegetation coverage rate and impermeable area ratio of each grid cell in the digital twin base plate of the urban three-dimensional terrain.

3. The urban flooding process simulation and sluice gate group scheduling method as described in claim 2, characterized in that, The calculation of net rainfall intensity distribution data for each grid cell, based on the vegetation coverage and impervious area ratio of each grid cell in the urban three-dimensional terrain digital twin base plate, includes: Using the geographic coordinates of each rainfall monitoring station as the generator, Thiessen polygons are constructed within the coverage area of ​​the urban three-dimensional terrain digital twin base plate. Each grid cell in the urban three-dimensional terrain digital twin base plate is assigned to the Thiessen polygon corresponding to the nearest rainfall monitoring station to obtain non-uniform rainfall distribution data. Based on non-uniform rainfall process data, the rainfall amount of each grid cell is converted into the rainfall intensity within the corresponding calculation time step according to the rainfall observation time interval. The vegetation interception intensity is determined by combining the vegetation coverage rate, and the infiltration deduction intensity is determined by combining the permeable area ratio, soil infiltration capacity and underlying surface type. The impermeable area ratio is used as the surface runoff enhancement constraint parameter to obtain the net rainfall intensity distribution data of each grid cell.

4. The method for simulating urban flooding processes and scheduling sluice gates as described in claim 1, characterized in that, The process involves using the net rainfall intensity distribution data as the source term of a continuous equation, the rigid flow resistance properties of the building as a solid wall boundary condition, and the flow capacity parameters of the drainage network as a negative source term constraint. The calculation of the overall water depth distribution, velocity field, and the water levels of the inner and outer rivers at each sluice gate control section using a two-dimensional shallow water equation includes: The net rainfall intensity distribution data is used as the source term of the continuous equation, the solid wall reflection condition of the grid cell corresponding to the rigid body flow resistance property of the building is used as the water flow boundary, and the maximum allowable drainage flow of each node of the drainage network is used as the negative source term of the corresponding grid cell. The global water depth distribution and velocity field are calculated by two-dimensional shallow water equation. Based on the overall water depth distribution, the bottom elevation of the grid cells on the inner river side and the outer river side of each sluice gate is added to the water depth value of the corresponding grid cell in the water depth distribution to obtain the inner river water level and outer river water level of each sluice gate control section.

5. The urban flooding process simulation and sluice gate group scheduling method as described in claim 4, characterized in that, Based on the water levels of the inner and outer rivers, the optimal scheduling scheme for the sluice gate group is obtained by backward recursion to accumulate the optimal cost for each combination of sluice gate openings within each scheduling time step, followed by forward backtracking. This includes: Using the inland river water level and the outer river water level as the initial state, the process is progressively reversed from the last scheduling time step to the current scheduling time step. Within each scheduling time step, all feasible opening combinations of each sluice gate are enumerated. Each sluice gate opening combination is used as the dynamic boundary condition of the sluice gate and substituted into the two-dimensional shallow water equation to predict the inland river water level and the outer river water level of the next scheduling time step. The sum of the differences between the inland and outer river water levels at each sluice gate control section is used as the single-step cost to accumulate to the optimal cost function, thereby obtaining the cumulative optimal cost and optimal opening record of each sluice gate opening combination within each scheduling time step. Based on the cumulative optimal cost and the optimal opening record, the optimal opening of each scheduling time step is traced back in a forward direction from the current scheduling time step to the last scheduling time step to obtain the optimal scheduling scheme for the sluice gate group.

6. The urban flooding process simulation and sluice gate group scheduling method as described in claim 5, characterized in that, The opening degree of each sluice gate in the current scheduling time step of the optimal scheduling scheme of the sluice gate group is used as the dynamic boundary condition of the sluice gate to update the two-dimensional shallow water equation, driving the flood evolution simulation of the next scheduling cycle.

7. A device for simulating urban flooding processes and scheduling sluice gate groups, characterized in that, The steps for implementing the urban flooding process simulation and sluice gate group scheduling method as described in any one of claims 1 to 6 include: The modeling module is used to overlay the rigid body flow resistance properties of buildings, the flow capacity parameters of drainage pipe networks, and the location of sluice gate control sections onto the three-dimensional urban terrain to obtain a digital twin base plate of the three-dimensional urban terrain and calculate the net rainfall intensity distribution data of each grid cell. The calculation module is used to use the net rainfall intensity distribution data as the source term of the continuous equation, the rigid body flow resistance property of the building as the solid wall boundary condition, and the flow capacity parameter of the drainage network as the negative source term constraint, and calculate the water depth distribution, velocity field and water level of the inner and outer rivers at each sluice gate control section through the two-dimensional shallow water equation. The scheduling module is used to perform reverse recursion to accumulate the optimal cost for each combination of sluice gate openings within each scheduling time step based on the water level of the inner river and the water level of the outer river, and then backtrack forward to obtain the optimal scheduling scheme for the sluice gate group.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the urban flooding process simulation and sluice gate group scheduling method as described in any one of claims 1 to 6.

9. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the urban flooding process simulation and sluice gate group scheduling method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the urban flooding process simulation and sluice gate group scheduling method as described in any one of claims 1 to 6.

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